diff --git a/01_materials/notebooks/ClassW_Jan_7_Classfication-1.ipynb b/01_materials/notebooks/ClassW_Jan_7_Classfication-1.ipynb
new file mode 100644
index 000000000..cabdaceff
--- /dev/null
+++ b/01_materials/notebooks/ClassW_Jan_7_Classfication-1.ipynb
@@ -0,0 +1,8605 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load in libraries\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.colors as mcolors\n",
+    "from mpl_toolkits import mplot3d"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>diagnosis</th>\n",
+       "      <th>radius_mean</th>\n",
+       "      <th>texture_mean</th>\n",
+       "      <th>perimeter_mean</th>\n",
+       "      <th>area_mean</th>\n",
+       "      <th>smoothness_mean</th>\n",
+       "      <th>compactness_mean</th>\n",
+       "      <th>concavity_mean</th>\n",
+       "      <th>concave points_mean</th>\n",
+       "      <th>...</th>\n",
+       "      <th>radius_worst</th>\n",
+       "      <th>texture_worst</th>\n",
+       "      <th>perimeter_worst</th>\n",
+       "      <th>area_worst</th>\n",
+       "      <th>smoothness_worst</th>\n",
+       "      <th>compactness_worst</th>\n",
+       "      <th>concavity_worst</th>\n",
+       "      <th>concave points_worst</th>\n",
+       "      <th>symmetry_worst</th>\n",
+       "      <th>fractal_dimension_worst</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>842302</td>\n",
+       "      <td>M</td>\n",
+       "      <td>17.99</td>\n",
+       "      <td>10.38</td>\n",
+       "      <td>122.80</td>\n",
+       "      <td>1001.0</td>\n",
+       "      <td>0.11840</td>\n",
+       "      <td>0.27760</td>\n",
+       "      <td>0.30010</td>\n",
+       "      <td>0.14710</td>\n",
+       "      <td>...</td>\n",
+       "      <td>25.380</td>\n",
+       "      <td>17.33</td>\n",
+       "      <td>184.60</td>\n",
+       "      <td>2019.0</td>\n",
+       "      <td>0.16220</td>\n",
+       "      <td>0.66560</td>\n",
+       "      <td>0.7119</td>\n",
+       "      <td>0.2654</td>\n",
+       "      <td>0.4601</td>\n",
+       "      <td>0.11890</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>842517</td>\n",
+       "      <td>M</td>\n",
+       "      <td>20.57</td>\n",
+       "      <td>17.77</td>\n",
+       "      <td>132.90</td>\n",
+       "      <td>1326.0</td>\n",
+       "      <td>0.08474</td>\n",
+       "      <td>0.07864</td>\n",
+       "      <td>0.08690</td>\n",
+       "      <td>0.07017</td>\n",
+       "      <td>...</td>\n",
+       "      <td>24.990</td>\n",
+       "      <td>23.41</td>\n",
+       "      <td>158.80</td>\n",
+       "      <td>1956.0</td>\n",
+       "      <td>0.12380</td>\n",
+       "      <td>0.18660</td>\n",
+       "      <td>0.2416</td>\n",
+       "      <td>0.1860</td>\n",
+       "      <td>0.2750</td>\n",
+       "      <td>0.08902</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>84300903</td>\n",
+       "      <td>M</td>\n",
+       "      <td>19.69</td>\n",
+       "      <td>21.25</td>\n",
+       "      <td>130.00</td>\n",
+       "      <td>1203.0</td>\n",
+       "      <td>0.10960</td>\n",
+       "      <td>0.15990</td>\n",
+       "      <td>0.19740</td>\n",
+       "      <td>0.12790</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23.570</td>\n",
+       "      <td>25.53</td>\n",
+       "      <td>152.50</td>\n",
+       "      <td>1709.0</td>\n",
+       "      <td>0.14440</td>\n",
+       "      <td>0.42450</td>\n",
+       "      <td>0.4504</td>\n",
+       "      <td>0.2430</td>\n",
+       "      <td>0.3613</td>\n",
+       "      <td>0.08758</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>84348301</td>\n",
+       "      <td>M</td>\n",
+       "      <td>11.42</td>\n",
+       "      <td>20.38</td>\n",
+       "      <td>77.58</td>\n",
+       "      <td>386.1</td>\n",
+       "      <td>0.14250</td>\n",
+       "      <td>0.28390</td>\n",
+       "      <td>0.24140</td>\n",
+       "      <td>0.10520</td>\n",
+       "      <td>...</td>\n",
+       "      <td>14.910</td>\n",
+       "      <td>26.50</td>\n",
+       "      <td>98.87</td>\n",
+       "      <td>567.7</td>\n",
+       "      <td>0.20980</td>\n",
+       "      <td>0.86630</td>\n",
+       "      <td>0.6869</td>\n",
+       "      <td>0.2575</td>\n",
+       "      <td>0.6638</td>\n",
+       "      <td>0.17300</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>84358402</td>\n",
+       "      <td>M</td>\n",
+       "      <td>20.29</td>\n",
+       "      <td>14.34</td>\n",
+       "      <td>135.10</td>\n",
+       "      <td>1297.0</td>\n",
+       "      <td>0.10030</td>\n",
+       "      <td>0.13280</td>\n",
+       "      <td>0.19800</td>\n",
+       "      <td>0.10430</td>\n",
+       "      <td>...</td>\n",
+       "      <td>22.540</td>\n",
+       "      <td>16.67</td>\n",
+       "      <td>152.20</td>\n",
+       "      <td>1575.0</td>\n",
+       "      <td>0.13740</td>\n",
+       "      <td>0.20500</td>\n",
+       "      <td>0.4000</td>\n",
+       "      <td>0.1625</td>\n",
+       "      <td>0.2364</td>\n",
+       "      <td>0.07678</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>564</th>\n",
+       "      <td>926424</td>\n",
+       "      <td>M</td>\n",
+       "      <td>21.56</td>\n",
+       "      <td>22.39</td>\n",
+       "      <td>142.00</td>\n",
+       "      <td>1479.0</td>\n",
+       "      <td>0.11100</td>\n",
+       "      <td>0.11590</td>\n",
+       "      <td>0.24390</td>\n",
+       "      <td>0.13890</td>\n",
+       "      <td>...</td>\n",
+       "      <td>25.450</td>\n",
+       "      <td>26.40</td>\n",
+       "      <td>166.10</td>\n",
+       "      <td>2027.0</td>\n",
+       "      <td>0.14100</td>\n",
+       "      <td>0.21130</td>\n",
+       "      <td>0.4107</td>\n",
+       "      <td>0.2216</td>\n",
+       "      <td>0.2060</td>\n",
+       "      <td>0.07115</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>565</th>\n",
+       "      <td>926682</td>\n",
+       "      <td>M</td>\n",
+       "      <td>20.13</td>\n",
+       "      <td>28.25</td>\n",
+       "      <td>131.20</td>\n",
+       "      <td>1261.0</td>\n",
+       "      <td>0.09780</td>\n",
+       "      <td>0.10340</td>\n",
+       "      <td>0.14400</td>\n",
+       "      <td>0.09791</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23.690</td>\n",
+       "      <td>38.25</td>\n",
+       "      <td>155.00</td>\n",
+       "      <td>1731.0</td>\n",
+       "      <td>0.11660</td>\n",
+       "      <td>0.19220</td>\n",
+       "      <td>0.3215</td>\n",
+       "      <td>0.1628</td>\n",
+       "      <td>0.2572</td>\n",
+       "      <td>0.06637</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>566</th>\n",
+       "      <td>926954</td>\n",
+       "      <td>M</td>\n",
+       "      <td>16.60</td>\n",
+       "      <td>28.08</td>\n",
+       "      <td>108.30</td>\n",
+       "      <td>858.1</td>\n",
+       "      <td>0.08455</td>\n",
+       "      <td>0.10230</td>\n",
+       "      <td>0.09251</td>\n",
+       "      <td>0.05302</td>\n",
+       "      <td>...</td>\n",
+       "      <td>18.980</td>\n",
+       "      <td>34.12</td>\n",
+       "      <td>126.70</td>\n",
+       "      <td>1124.0</td>\n",
+       "      <td>0.11390</td>\n",
+       "      <td>0.30940</td>\n",
+       "      <td>0.3403</td>\n",
+       "      <td>0.1418</td>\n",
+       "      <td>0.2218</td>\n",
+       "      <td>0.07820</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>567</th>\n",
+       "      <td>927241</td>\n",
+       "      <td>M</td>\n",
+       "      <td>20.60</td>\n",
+       "      <td>29.33</td>\n",
+       "      <td>140.10</td>\n",
+       "      <td>1265.0</td>\n",
+       "      <td>0.11780</td>\n",
+       "      <td>0.27700</td>\n",
+       "      <td>0.35140</td>\n",
+       "      <td>0.15200</td>\n",
+       "      <td>...</td>\n",
+       "      <td>25.740</td>\n",
+       "      <td>39.42</td>\n",
+       "      <td>184.60</td>\n",
+       "      <td>1821.0</td>\n",
+       "      <td>0.16500</td>\n",
+       "      <td>0.86810</td>\n",
+       "      <td>0.9387</td>\n",
+       "      <td>0.2650</td>\n",
+       "      <td>0.4087</td>\n",
+       "      <td>0.12400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>568</th>\n",
+       "      <td>92751</td>\n",
+       "      <td>B</td>\n",
+       "      <td>7.76</td>\n",
+       "      <td>24.54</td>\n",
+       "      <td>47.92</td>\n",
+       "      <td>181.0</td>\n",
+       "      <td>0.05263</td>\n",
+       "      <td>0.04362</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>9.456</td>\n",
+       "      <td>30.37</td>\n",
+       "      <td>59.16</td>\n",
+       "      <td>268.6</td>\n",
+       "      <td>0.08996</td>\n",
+       "      <td>0.06444</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.2871</td>\n",
+       "      <td>0.07039</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>569 rows × 32 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
+       "0      842302         M        17.99         10.38          122.80     1001.0   \n",
+       "1      842517         M        20.57         17.77          132.90     1326.0   \n",
+       "2    84300903         M        19.69         21.25          130.00     1203.0   \n",
+       "3    84348301         M        11.42         20.38           77.58      386.1   \n",
+       "4    84358402         M        20.29         14.34          135.10     1297.0   \n",
+       "..        ...       ...          ...           ...             ...        ...   \n",
+       "564    926424         M        21.56         22.39          142.00     1479.0   \n",
+       "565    926682         M        20.13         28.25          131.20     1261.0   \n",
+       "566    926954         M        16.60         28.08          108.30      858.1   \n",
+       "567    927241         M        20.60         29.33          140.10     1265.0   \n",
+       "568     92751         B         7.76         24.54           47.92      181.0   \n",
+       "\n",
+       "     smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
+       "0            0.11840           0.27760         0.30010              0.14710   \n",
+       "1            0.08474           0.07864         0.08690              0.07017   \n",
+       "2            0.10960           0.15990         0.19740              0.12790   \n",
+       "3            0.14250           0.28390         0.24140              0.10520   \n",
+       "4            0.10030           0.13280         0.19800              0.10430   \n",
+       "..               ...               ...             ...                  ...   \n",
+       "564          0.11100           0.11590         0.24390              0.13890   \n",
+       "565          0.09780           0.10340         0.14400              0.09791   \n",
+       "566          0.08455           0.10230         0.09251              0.05302   \n",
+       "567          0.11780           0.27700         0.35140              0.15200   \n",
+       "568          0.05263           0.04362         0.00000              0.00000   \n",
+       "\n",
+       "     ...  radius_worst  texture_worst  perimeter_worst  area_worst  \\\n",
+       "0    ...        25.380          17.33           184.60      2019.0   \n",
+       "1    ...        24.990          23.41           158.80      1956.0   \n",
+       "2    ...        23.570          25.53           152.50      1709.0   \n",
+       "3    ...        14.910          26.50            98.87       567.7   \n",
+       "4    ...        22.540          16.67           152.20      1575.0   \n",
+       "..   ...           ...            ...              ...         ...   \n",
+       "564  ...        25.450          26.40           166.10      2027.0   \n",
+       "565  ...        23.690          38.25           155.00      1731.0   \n",
+       "566  ...        18.980          34.12           126.70      1124.0   \n",
+       "567  ...        25.740          39.42           184.60      1821.0   \n",
+       "568  ...         9.456          30.37            59.16       268.6   \n",
+       "\n",
+       "     smoothness_worst  compactness_worst  concavity_worst  \\\n",
+       "0             0.16220            0.66560           0.7119   \n",
+       "1             0.12380            0.18660           0.2416   \n",
+       "2             0.14440            0.42450           0.4504   \n",
+       "3             0.20980            0.86630           0.6869   \n",
+       "4             0.13740            0.20500           0.4000   \n",
+       "..                ...                ...              ...   \n",
+       "564           0.14100            0.21130           0.4107   \n",
+       "565           0.11660            0.19220           0.3215   \n",
+       "566           0.11390            0.30940           0.3403   \n",
+       "567           0.16500            0.86810           0.9387   \n",
+       "568           0.08996            0.06444           0.0000   \n",
+       "\n",
+       "     concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
+       "0                  0.2654          0.4601                  0.11890  \n",
+       "1                  0.1860          0.2750                  0.08902  \n",
+       "2                  0.2430          0.3613                  0.08758  \n",
+       "3                  0.2575          0.6638                  0.17300  \n",
+       "4                  0.1625          0.2364                  0.07678  \n",
+       "..                    ...             ...                      ...  \n",
+       "564                0.2216          0.2060                  0.07115  \n",
+       "565                0.1628          0.2572                  0.06637  \n",
+       "566                0.1418          0.2218                  0.07820  \n",
+       "567                0.2650          0.4087                  0.12400  \n",
+       "568                0.0000          0.2871                  0.07039  \n",
+       "\n",
+       "[569 rows x 32 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# loading the data , make sure you know where you are. \n",
+    "cancer = pd.read_csv('dataset/wdbc.csv')\n",
+    "cancer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 569 entries, 0 to 568\n",
+      "Data columns (total 32 columns):\n",
+      " #   Column                   Non-Null Count  Dtype  \n",
+      "---  ------                   --------------  -----  \n",
+      " 0   id                       569 non-null    int64  \n",
+      " 1   diagnosis                569 non-null    object \n",
+      " 2   radius_mean              569 non-null    float64\n",
+      " 3   texture_mean             569 non-null    float64\n",
+      " 4   perimeter_mean           569 non-null    float64\n",
+      " 5   area_mean                569 non-null    float64\n",
+      " 6   smoothness_mean          569 non-null    float64\n",
+      " 7   compactness_mean         569 non-null    float64\n",
+      " 8   concavity_mean           569 non-null    float64\n",
+      " 9   concave points_mean      569 non-null    float64\n",
+      " 10  symmetry_mean            569 non-null    float64\n",
+      " 11  fractal_dimension_mean   569 non-null    float64\n",
+      " 12  radius_se                569 non-null    float64\n",
+      " 13  texture_se               569 non-null    float64\n",
+      " 14  perimeter_se             569 non-null    float64\n",
+      " 15  area_se                  569 non-null    float64\n",
+      " 16  smoothness_se            569 non-null    float64\n",
+      " 17  compactness_se           569 non-null    float64\n",
+      " 18  concavity_se             569 non-null    float64\n",
+      " 19  concave points_se        569 non-null    float64\n",
+      " 20  symmetry_se              569 non-null    float64\n",
+      " 21  fractal_dimension_se     569 non-null    float64\n",
+      " 22  radius_worst             569 non-null    float64\n",
+      " 23  texture_worst            569 non-null    float64\n",
+      " 24  perimeter_worst          569 non-null    float64\n",
+      " 25  area_worst               569 non-null    float64\n",
+      " 26  smoothness_worst         569 non-null    float64\n",
+      " 27  compactness_worst        569 non-null    float64\n",
+      " 28  concavity_worst          569 non-null    float64\n",
+      " 29  concave points_worst     569 non-null    float64\n",
+      " 30  symmetry_worst           569 non-null    float64\n",
+      " 31  fractal_dimension_worst  569 non-null    float64\n",
+      "dtypes: float64(30), int64(1), object(1)\n",
+      "memory usage: 142.4+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "#take a close look at data, no of column, rows, data type, non null values\n",
+    "cancer.info()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['M', 'B'], dtype=object)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# investigate and return unique cateogry in a column\n",
+    "cancer[\"diagnosis\"].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Malignant', 'Benign'], dtype=object)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# rename using a dictionary becasue they are object data type\n",
+    "cancer[\"diagnosis\"] = cancer['diagnosis'].replace({\n",
+    "    \"M\": \"Malignant\", \n",
+    "    \"B\": \"Benign\"\n",
+    "    })\n",
+    "cancer[\"diagnosis\"].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "diagnosis\n",
+       "Benign       357\n",
+       "Malignant    212\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# use value count for good and bad case\n",
+    "cancer['diagnosis'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "diagnosis\n",
+       "Benign       0.627417\n",
+       "Malignant    0.372583\n",
+       "Name: proportion, dtype: float64"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# % count?\n",
+    "cancer['diagnosis'].value_counts(normalize = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "diagnosis\n",
+       "Benign       357\n",
+       "Malignant    212\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# use groupby category in a column\n",
+    "cancer.groupby(\"diagnosis\").size()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "diagnosis\n",
+       "Benign       62.741652\n",
+       "Malignant    37.258348\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#get % by getting the length of this column\n",
+    "(cancer.groupby(\"diagnosis\").size() / cancer.shape[0])*100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# use maplotlit\n",
+    "# Create mapping between values and colors\n",
+    "labels = cancer[\"diagnosis\"].unique().tolist()\n",
+    "colors = list(mcolors.TABLEAU_COLORS.keys())\n",
+    "color_map = {l: colors[i % len(colors)] for i, l in enumerate(labels)}\n",
+    "\n",
+    "# Plot\n",
+    "plt.scatter(cancer[\"perimeter_mean\"], cancer['concavity_mean'], \n",
+    "            color=cancer[\"diagnosis\"].map(color_map))\n",
+    "\n",
+    "# Create custom legend handles\n",
+    "handles = [plt.Line2D([0], [0], marker='o', color='w', label=label,\n",
+    "                      markersize=10, markerfacecolor=color_map[label])\n",
+    "           for label in labels]\n",
+    "\n",
+    "# Add labels and legend\n",
+    "plt.xlabel('Perimeter Mean')\n",
+    "plt.ylabel('Concavity Mean')\n",
+    "plt.title('Scatter Plot of Perimeter Mean vs Concavity Mean')\n",
+    "plt.legend(handles=handles, title='Diagnosis')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot existing data\n",
+    "plt.scatter(cancer[\"perimeter_mean\"], cancer['concavity_mean'], \n",
+    "            color=cancer[\"diagnosis\"].map(color_map))\n",
+    "\n",
+    "# Create custom legend handles\n",
+    "handles = [plt.Line2D([0], [0], marker='o', color='w', label=label,\n",
+    "                      markersize=10, markerfacecolor=color_map[label])\n",
+    "           for label in labels]\n",
+    "\n",
+    "# Add new observation\n",
+    "new_observation = {'perimeter_mean': 97, 'concavity_mean': 0.20}\n",
+    "plt.scatter(new_observation['perimeter_mean'], new_observation['concavity_mean'],\n",
+    "            color='red', edgecolor='black', s=100, label='New Observation')\n",
+    "\n",
+    "# Add labels and legend\n",
+    "plt.xlabel('Perimeter Mean')\n",
+    "plt.ylabel('Concavity Mean')\n",
+    "plt.title('Scatter Plot of Perimeter Mean vs Concavity Mean')\n",
+    "plt.legend(handles=handles + [plt.Line2D([0], [0], marker='o', color='w', \n",
+    "                                          markerfacecolor='red', markeredgecolor='black', \n",
+    "                                          markersize=10, label='New Observation')], \n",
+    "           title='Diagnosis')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# usuall standardize the data first, but not for here\n",
+    "# new observation\n",
+    "new_obs_Perimeter = 97\n",
+    "new_obs_Concavity = 0.20"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#dist calculation\n",
+    "\n",
+    "cancer['dist_from_new'] =(\n",
+    "\n",
+    "(cancer ['perimeter_mean'] - new_obs_Perimeter)**2 +\n",
+    "(cancer['concavity_mean'] - new_obs_Concavity)**2 \n",
+    ") ** (1/2)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0      25.800194\n",
+       "1      35.900178\n",
+       "2      33.000000\n",
+       "3      19.420044\n",
+       "4      38.100000\n",
+       "         ...    \n",
+       "564    45.000021\n",
+       "565    34.200046\n",
+       "566    11.300511\n",
+       "567    43.100266\n",
+       "568    49.080407\n",
+       "Name: dist_from_new, Length: 569, dtype: float64"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cancer ['dist_from_new']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>perimeter_mean</th>\n",
+       "      <th>concavity_mean</th>\n",
+       "      <th>diagnosis</th>\n",
+       "      <th>dist_from_new</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>291</th>\n",
+       "      <td>97.03</td>\n",
+       "      <td>0.05940</td>\n",
+       "      <td>Benign</td>\n",
+       "      <td>0.143765</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>138</th>\n",
+       "      <td>96.85</td>\n",
+       "      <td>0.15390</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>0.156924</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>96.73</td>\n",
+       "      <td>0.16390</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>0.272403</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>514</th>\n",
+       "      <td>97.26</td>\n",
+       "      <td>0.07486</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>0.288548</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54</th>\n",
+       "      <td>97.26</td>\n",
+       "      <td>0.05253</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>0.298910</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     perimeter_mean  concavity_mean  diagnosis  dist_from_new\n",
+       "291           97.03         0.05940     Benign       0.143765\n",
+       "138           96.85         0.15390  Malignant       0.156924\n",
+       "15            96.73         0.16390  Malignant       0.272403\n",
+       "514           97.26         0.07486  Malignant       0.288548\n",
+       "54            97.26         0.05253  Malignant       0.298910"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# take the least 5\n",
+    "nearest_5 = cancer.nsmallest(5, \"dist_from_new\")[[\n",
+    "    \"perimeter_mean\",\n",
+    "    \"concavity_mean\",\n",
+    "    \"diagnosis\",\n",
+    "    \"dist_from_new\"\n",
+    "]]\n",
+    "\n",
+    "nearest_5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create mapping between values and colors\n",
+    "labels = cancer[\"diagnosis\"].unique().tolist()\n",
+    "colors = list(mcolors.TABLEAU_COLORS.keys())\n",
+    "color_map = {l: colors[i % len(colors)] for i, l in enumerate(labels)}\n",
+    "\n",
+    "# Create a 3D plot\n",
+    "ax = plt.axes(projection=\"3d\")\n",
+    "\n",
+    "# Plot data points with color corresponding to diagnosis\n",
+    "sc = ax.scatter3D(cancer['perimeter_mean'], cancer['concavity_mean'], cancer['symmetry_mean'], \n",
+    "                  c=cancer['diagnosis'].map(color_map), marker='o')\n",
+    "\n",
+    "# Add axis labels\n",
+    "ax.set_xlabel('Perimeter Mean')\n",
+    "ax.set_ylabel('Concavity Mean')\n",
+    "ax.set_zlabel('Symmetry Mean')\n",
+    "ax.set_title('3D Scatter Plot of Perimeter Mean, Concavity Mean, and Symmetry Mean')\n",
+    "\n",
+    "# Create custom legend handles\n",
+    "handles = [plt.Line2D([0], [0], marker='o', color='w', label=label,\n",
+    "                      markersize=10, markerfacecolor=color_map[label])\n",
+    "           for label in labels]\n",
+    "\n",
+    "# Add legend\n",
+    "plt.legend(handles=handles, title='Diagnosis')\n",
+    "\n",
+    "# Show plot\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<module 'matplotlib.pyplot' from 'c:\\\\Users\\\\tinti\\\\miniconda3\\\\envs\\\\dsi_participant\\\\lib\\\\site-packages\\\\matplotlib\\\\pyplot.py'>"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create mapping between values and colors\n",
+    "labels = cancer[\"diagnosis\"].unique().tolist()\n",
+    "colors = list(mcolors.TABLEAU_COLORS.keys())\n",
+    "color_map = {l: colors[i % len(colors)] for i, l in enumerate(labels)}\n",
+    "\n",
+    "# Create a 3D plot\n",
+    "ax = plt.axes(projection=\"3d\")\n",
+    "\n",
+    "# Plot data points with color corresponding to diagnosis\n",
+    "sc = ax.scatter3D(cancer['perimeter_mean'], cancer['concavity_mean'], cancer['symmetry_mean'], \n",
+    "                  c=cancer['diagnosis'].map(color_map), marker='o')\n",
+    "\n",
+    "# Define the new observation\n",
+    "new_observation = {'perimeter_mean': 97, 'concavity_mean': 0.20, 'symmetry_mean': 0.22}\n",
+    "\n",
+    "# Plot the new observation\n",
+    "ax.scatter3D(new_observation['perimeter_mean'], new_observation['concavity_mean'], \n",
+    "             new_observation['symmetry_mean'], color='red', edgecolor='black', \n",
+    "             s=100, marker='o', label='New Observation')\n",
+    "\n",
+    "# Add axis labels\n",
+    "ax.set_xlabel('Perimeter Mean')\n",
+    "ax.set_ylabel('Concavity Mean')\n",
+    "ax.set_zlabel('Symmetry Mean')\n",
+    "ax.set_title('3D Scatter Plot of Perimeter Mean, Concavity Mean, and Symmetry Mean')\n",
+    "\n",
+    "# Create custom legend handles\n",
+    "handles = [plt.Line2D([0], [0], marker='o', color='w', label=label,\n",
+    "                      markersize=10, markerfacecolor=color_map[label])\n",
+    "           for label in labels]\n",
+    "\n",
+    "# Add custom legend for new observation\n",
+    "handles.append(plt.Line2D([0], [0], marker='o', color='red', label='New Observation', \n",
+    "                          markersize=10, markeredgecolor='black'))\n",
+    "\n",
+    "# Add legend\n",
+    "plt.legend(handles=handles, title='Diagnosis')\n",
+    "\n",
+    "# Show plot\n",
+    "plt\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "new_obs_Perimeter = 97\n",
+    "new_obs_Concavity = 0.20\n",
+    "new_obs_Symmetry = 0.22"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0      25.800203\n",
+       "1      35.900199\n",
+       "2      33.000003\n",
+       "3      19.420085\n",
+       "4      38.100020\n",
+       "         ...    \n",
+       "564    45.000046\n",
+       "565    34.200075\n",
+       "566    11.300676\n",
+       "567    43.100270\n",
+       "568    49.080446\n",
+       "Name: dist_from_new, Length: 569, dtype: float64"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "##********** **Calculating the distance *******as KNN do ********\n",
+    "\n",
+    "cancer[\"dist_from_new\"] = (\n",
+    "      (cancer[\"perimeter_mean\"] - new_obs_Perimeter) ** 2\n",
+    "    + (cancer[\"concavity_mean\"] - new_obs_Concavity) ** 2\n",
+    "    + (cancer[\"symmetry_mean\"] - new_obs_Symmetry) ** 2\n",
+    ")**(1/2)\n",
+    "\n",
+    "cancer[\"dist_from_new\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>perimeter_mean</th>\n",
+       "      <th>concavity_mean</th>\n",
+       "      <th>symmetry_mean</th>\n",
+       "      <th>diagnosis</th>\n",
+       "      <th>dist_from_new</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>291</th>\n",
+       "      <td>97.03</td>\n",
+       "      <td>0.05940</td>\n",
+       "      <td>0.1879</td>\n",
+       "      <td>Benign</td>\n",
+       "      <td>0.147305</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>138</th>\n",
+       "      <td>96.85</td>\n",
+       "      <td>0.15390</td>\n",
+       "      <td>0.1957</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>0.158795</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>96.73</td>\n",
+       "      <td>0.16390</td>\n",
+       "      <td>0.2303</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>0.272597</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>514</th>\n",
+       "      <td>97.26</td>\n",
+       "      <td>0.07486</td>\n",
+       "      <td>0.1561</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>0.295539</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54</th>\n",
+       "      <td>97.26</td>\n",
+       "      <td>0.05253</td>\n",
+       "      <td>0.1616</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>0.304562</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     perimeter_mean  concavity_mean  symmetry_mean  diagnosis  dist_from_new\n",
+       "291           97.03         0.05940         0.1879     Benign       0.147305\n",
+       "138           96.85         0.15390         0.1957  Malignant       0.158795\n",
+       "15            96.73         0.16390         0.2303  Malignant       0.272597\n",
+       "514           97.26         0.07486         0.1561  Malignant       0.295539\n",
+       "54            97.26         0.05253         0.1616  Malignant       0.304562"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nearest_5 = cancer.nsmallest(5, \"dist_from_new\")[[\n",
+    "    \"perimeter_mean\",\n",
+    "    \"concavity_mean\",\n",
+    "    \"symmetry_mean\",\n",
+    "    \"diagnosis\",\n",
+    "    \"dist_from_new\"]]\n",
+    "\n",
+    "nearest_5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn import set_config\n",
+    "set_config (transform_output= \"pandas\"\n",
+    "            )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.neighbors import KNeighborsClassifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>diagnosis</th>\n",
+       "      <th>perimeter_mean</th>\n",
+       "      <th>concavity_mean</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>122.80</td>\n",
+       "      <td>0.30010</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>132.90</td>\n",
+       "      <td>0.08690</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>130.00</td>\n",
+       "      <td>0.19740</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>77.58</td>\n",
+       "      <td>0.24140</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>135.10</td>\n",
+       "      <td>0.19800</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>564</th>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>142.00</td>\n",
+       "      <td>0.24390</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>565</th>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>131.20</td>\n",
+       "      <td>0.14400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>566</th>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>108.30</td>\n",
+       "      <td>0.09251</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>567</th>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>140.10</td>\n",
+       "      <td>0.35140</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>568</th>\n",
+       "      <td>Benign</td>\n",
+       "      <td>47.92</td>\n",
+       "      <td>0.00000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>569 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     diagnosis  perimeter_mean  concavity_mean\n",
+       "0    Malignant          122.80         0.30010\n",
+       "1    Malignant          132.90         0.08690\n",
+       "2    Malignant          130.00         0.19740\n",
+       "3    Malignant           77.58         0.24140\n",
+       "4    Malignant          135.10         0.19800\n",
+       "..         ...             ...             ...\n",
+       "564  Malignant          142.00         0.24390\n",
+       "565  Malignant          131.20         0.14400\n",
+       "566  Malignant          108.30         0.09251\n",
+       "567  Malignant          140.10         0.35140\n",
+       "568     Benign           47.92         0.00000\n",
+       "\n",
+       "[569 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cancer_train = cancer[[\"diagnosis\", \"perimeter_mean\", \"concavity_mean\"]]\n",
+    "cancer_train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;&nbsp;KNeighborsClassifier<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></label><div class=\"sk-toggleable__content \"><pre>KNeighborsClassifier()</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "KNeighborsClassifier()"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "knn = KNeighborsClassifier(n_neighbors=5)\n",
+    "knn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# define our perditor variable (xs)\n",
+    "X = cancer_train[[\"perimeter_mean\", \"concavity_mean\"]]\n",
+    "y = cancer_train[[\"diagnosis\"]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\tinti\\miniconda3\\envs\\dsi_participant\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:238: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+      "  return self._fit(X, y)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-2 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-2 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-2 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-2 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;KNeighborsClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsClassifier()</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "KNeighborsClassifier()"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# fit into the knn model\n",
+    "knn.fit(X,y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Malignant'], dtype=object)"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "new_obs = pd.DataFrame({\"perimeter_mean\": [97], \"concavity_mean\": [0.20]})\n",
+    "knn.predict(new_obs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Malignant'], dtype=object)"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# predict our diagnosis \n",
+    "knn.predict(new_obs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Jan 8 ####"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#divde data\n",
+    "# adj paramente\n",
+    "#tuning\n",
+    "#play on test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.metrics import recall_score, precision_score\n",
+    "from sklearn.model_selection import cross_validate\n",
+    "from sklearn.model_selection import GridSearchCV\n",
+    "from sklearn.preprocessing import StandardScaler"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>diagnosis</th>\n",
+       "      <th>radius_mean</th>\n",
+       "      <th>texture_mean</th>\n",
+       "      <th>perimeter_mean</th>\n",
+       "      <th>area_mean</th>\n",
+       "      <th>smoothness_mean</th>\n",
+       "      <th>compactness_mean</th>\n",
+       "      <th>concavity_mean</th>\n",
+       "      <th>concave points_mean</th>\n",
+       "      <th>...</th>\n",
+       "      <th>radius_worst</th>\n",
+       "      <th>texture_worst</th>\n",
+       "      <th>perimeter_worst</th>\n",
+       "      <th>area_worst</th>\n",
+       "      <th>smoothness_worst</th>\n",
+       "      <th>compactness_worst</th>\n",
+       "      <th>concavity_worst</th>\n",
+       "      <th>concave points_worst</th>\n",
+       "      <th>symmetry_worst</th>\n",
+       "      <th>fractal_dimension_worst</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>842302</td>\n",
+       "      <td>M</td>\n",
+       "      <td>17.99</td>\n",
+       "      <td>10.38</td>\n",
+       "      <td>122.80</td>\n",
+       "      <td>1001.0</td>\n",
+       "      <td>0.11840</td>\n",
+       "      <td>0.27760</td>\n",
+       "      <td>0.30010</td>\n",
+       "      <td>0.14710</td>\n",
+       "      <td>...</td>\n",
+       "      <td>25.380</td>\n",
+       "      <td>17.33</td>\n",
+       "      <td>184.60</td>\n",
+       "      <td>2019.0</td>\n",
+       "      <td>0.16220</td>\n",
+       "      <td>0.66560</td>\n",
+       "      <td>0.7119</td>\n",
+       "      <td>0.2654</td>\n",
+       "      <td>0.4601</td>\n",
+       "      <td>0.11890</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>842517</td>\n",
+       "      <td>M</td>\n",
+       "      <td>20.57</td>\n",
+       "      <td>17.77</td>\n",
+       "      <td>132.90</td>\n",
+       "      <td>1326.0</td>\n",
+       "      <td>0.08474</td>\n",
+       "      <td>0.07864</td>\n",
+       "      <td>0.08690</td>\n",
+       "      <td>0.07017</td>\n",
+       "      <td>...</td>\n",
+       "      <td>24.990</td>\n",
+       "      <td>23.41</td>\n",
+       "      <td>158.80</td>\n",
+       "      <td>1956.0</td>\n",
+       "      <td>0.12380</td>\n",
+       "      <td>0.18660</td>\n",
+       "      <td>0.2416</td>\n",
+       "      <td>0.1860</td>\n",
+       "      <td>0.2750</td>\n",
+       "      <td>0.08902</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>84300903</td>\n",
+       "      <td>M</td>\n",
+       "      <td>19.69</td>\n",
+       "      <td>21.25</td>\n",
+       "      <td>130.00</td>\n",
+       "      <td>1203.0</td>\n",
+       "      <td>0.10960</td>\n",
+       "      <td>0.15990</td>\n",
+       "      <td>0.19740</td>\n",
+       "      <td>0.12790</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23.570</td>\n",
+       "      <td>25.53</td>\n",
+       "      <td>152.50</td>\n",
+       "      <td>1709.0</td>\n",
+       "      <td>0.14440</td>\n",
+       "      <td>0.42450</td>\n",
+       "      <td>0.4504</td>\n",
+       "      <td>0.2430</td>\n",
+       "      <td>0.3613</td>\n",
+       "      <td>0.08758</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>84348301</td>\n",
+       "      <td>M</td>\n",
+       "      <td>11.42</td>\n",
+       "      <td>20.38</td>\n",
+       "      <td>77.58</td>\n",
+       "      <td>386.1</td>\n",
+       "      <td>0.14250</td>\n",
+       "      <td>0.28390</td>\n",
+       "      <td>0.24140</td>\n",
+       "      <td>0.10520</td>\n",
+       "      <td>...</td>\n",
+       "      <td>14.910</td>\n",
+       "      <td>26.50</td>\n",
+       "      <td>98.87</td>\n",
+       "      <td>567.7</td>\n",
+       "      <td>0.20980</td>\n",
+       "      <td>0.86630</td>\n",
+       "      <td>0.6869</td>\n",
+       "      <td>0.2575</td>\n",
+       "      <td>0.6638</td>\n",
+       "      <td>0.17300</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>84358402</td>\n",
+       "      <td>M</td>\n",
+       "      <td>20.29</td>\n",
+       "      <td>14.34</td>\n",
+       "      <td>135.10</td>\n",
+       "      <td>1297.0</td>\n",
+       "      <td>0.10030</td>\n",
+       "      <td>0.13280</td>\n",
+       "      <td>0.19800</td>\n",
+       "      <td>0.10430</td>\n",
+       "      <td>...</td>\n",
+       "      <td>22.540</td>\n",
+       "      <td>16.67</td>\n",
+       "      <td>152.20</td>\n",
+       "      <td>1575.0</td>\n",
+       "      <td>0.13740</td>\n",
+       "      <td>0.20500</td>\n",
+       "      <td>0.4000</td>\n",
+       "      <td>0.1625</td>\n",
+       "      <td>0.2364</td>\n",
+       "      <td>0.07678</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>564</th>\n",
+       "      <td>926424</td>\n",
+       "      <td>M</td>\n",
+       "      <td>21.56</td>\n",
+       "      <td>22.39</td>\n",
+       "      <td>142.00</td>\n",
+       "      <td>1479.0</td>\n",
+       "      <td>0.11100</td>\n",
+       "      <td>0.11590</td>\n",
+       "      <td>0.24390</td>\n",
+       "      <td>0.13890</td>\n",
+       "      <td>...</td>\n",
+       "      <td>25.450</td>\n",
+       "      <td>26.40</td>\n",
+       "      <td>166.10</td>\n",
+       "      <td>2027.0</td>\n",
+       "      <td>0.14100</td>\n",
+       "      <td>0.21130</td>\n",
+       "      <td>0.4107</td>\n",
+       "      <td>0.2216</td>\n",
+       "      <td>0.2060</td>\n",
+       "      <td>0.07115</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>565</th>\n",
+       "      <td>926682</td>\n",
+       "      <td>M</td>\n",
+       "      <td>20.13</td>\n",
+       "      <td>28.25</td>\n",
+       "      <td>131.20</td>\n",
+       "      <td>1261.0</td>\n",
+       "      <td>0.09780</td>\n",
+       "      <td>0.10340</td>\n",
+       "      <td>0.14400</td>\n",
+       "      <td>0.09791</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23.690</td>\n",
+       "      <td>38.25</td>\n",
+       "      <td>155.00</td>\n",
+       "      <td>1731.0</td>\n",
+       "      <td>0.11660</td>\n",
+       "      <td>0.19220</td>\n",
+       "      <td>0.3215</td>\n",
+       "      <td>0.1628</td>\n",
+       "      <td>0.2572</td>\n",
+       "      <td>0.06637</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>566</th>\n",
+       "      <td>926954</td>\n",
+       "      <td>M</td>\n",
+       "      <td>16.60</td>\n",
+       "      <td>28.08</td>\n",
+       "      <td>108.30</td>\n",
+       "      <td>858.1</td>\n",
+       "      <td>0.08455</td>\n",
+       "      <td>0.10230</td>\n",
+       "      <td>0.09251</td>\n",
+       "      <td>0.05302</td>\n",
+       "      <td>...</td>\n",
+       "      <td>18.980</td>\n",
+       "      <td>34.12</td>\n",
+       "      <td>126.70</td>\n",
+       "      <td>1124.0</td>\n",
+       "      <td>0.11390</td>\n",
+       "      <td>0.30940</td>\n",
+       "      <td>0.3403</td>\n",
+       "      <td>0.1418</td>\n",
+       "      <td>0.2218</td>\n",
+       "      <td>0.07820</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>567</th>\n",
+       "      <td>927241</td>\n",
+       "      <td>M</td>\n",
+       "      <td>20.60</td>\n",
+       "      <td>29.33</td>\n",
+       "      <td>140.10</td>\n",
+       "      <td>1265.0</td>\n",
+       "      <td>0.11780</td>\n",
+       "      <td>0.27700</td>\n",
+       "      <td>0.35140</td>\n",
+       "      <td>0.15200</td>\n",
+       "      <td>...</td>\n",
+       "      <td>25.740</td>\n",
+       "      <td>39.42</td>\n",
+       "      <td>184.60</td>\n",
+       "      <td>1821.0</td>\n",
+       "      <td>0.16500</td>\n",
+       "      <td>0.86810</td>\n",
+       "      <td>0.9387</td>\n",
+       "      <td>0.2650</td>\n",
+       "      <td>0.4087</td>\n",
+       "      <td>0.12400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>568</th>\n",
+       "      <td>92751</td>\n",
+       "      <td>B</td>\n",
+       "      <td>7.76</td>\n",
+       "      <td>24.54</td>\n",
+       "      <td>47.92</td>\n",
+       "      <td>181.0</td>\n",
+       "      <td>0.05263</td>\n",
+       "      <td>0.04362</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>9.456</td>\n",
+       "      <td>30.37</td>\n",
+       "      <td>59.16</td>\n",
+       "      <td>268.6</td>\n",
+       "      <td>0.08996</td>\n",
+       "      <td>0.06444</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.2871</td>\n",
+       "      <td>0.07039</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>569 rows × 32 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
+       "0      842302         M        17.99         10.38          122.80     1001.0   \n",
+       "1      842517         M        20.57         17.77          132.90     1326.0   \n",
+       "2    84300903         M        19.69         21.25          130.00     1203.0   \n",
+       "3    84348301         M        11.42         20.38           77.58      386.1   \n",
+       "4    84358402         M        20.29         14.34          135.10     1297.0   \n",
+       "..        ...       ...          ...           ...             ...        ...   \n",
+       "564    926424         M        21.56         22.39          142.00     1479.0   \n",
+       "565    926682         M        20.13         28.25          131.20     1261.0   \n",
+       "566    926954         M        16.60         28.08          108.30      858.1   \n",
+       "567    927241         M        20.60         29.33          140.10     1265.0   \n",
+       "568     92751         B         7.76         24.54           47.92      181.0   \n",
+       "\n",
+       "     smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
+       "0            0.11840           0.27760         0.30010              0.14710   \n",
+       "1            0.08474           0.07864         0.08690              0.07017   \n",
+       "2            0.10960           0.15990         0.19740              0.12790   \n",
+       "3            0.14250           0.28390         0.24140              0.10520   \n",
+       "4            0.10030           0.13280         0.19800              0.10430   \n",
+       "..               ...               ...             ...                  ...   \n",
+       "564          0.11100           0.11590         0.24390              0.13890   \n",
+       "565          0.09780           0.10340         0.14400              0.09791   \n",
+       "566          0.08455           0.10230         0.09251              0.05302   \n",
+       "567          0.11780           0.27700         0.35140              0.15200   \n",
+       "568          0.05263           0.04362         0.00000              0.00000   \n",
+       "\n",
+       "     ...  radius_worst  texture_worst  perimeter_worst  area_worst  \\\n",
+       "0    ...        25.380          17.33           184.60      2019.0   \n",
+       "1    ...        24.990          23.41           158.80      1956.0   \n",
+       "2    ...        23.570          25.53           152.50      1709.0   \n",
+       "3    ...        14.910          26.50            98.87       567.7   \n",
+       "4    ...        22.540          16.67           152.20      1575.0   \n",
+       "..   ...           ...            ...              ...         ...   \n",
+       "564  ...        25.450          26.40           166.10      2027.0   \n",
+       "565  ...        23.690          38.25           155.00      1731.0   \n",
+       "566  ...        18.980          34.12           126.70      1124.0   \n",
+       "567  ...        25.740          39.42           184.60      1821.0   \n",
+       "568  ...         9.456          30.37            59.16       268.6   \n",
+       "\n",
+       "     smoothness_worst  compactness_worst  concavity_worst  \\\n",
+       "0             0.16220            0.66560           0.7119   \n",
+       "1             0.12380            0.18660           0.2416   \n",
+       "2             0.14440            0.42450           0.4504   \n",
+       "3             0.20980            0.86630           0.6869   \n",
+       "4             0.13740            0.20500           0.4000   \n",
+       "..                ...                ...              ...   \n",
+       "564           0.14100            0.21130           0.4107   \n",
+       "565           0.11660            0.19220           0.3215   \n",
+       "566           0.11390            0.30940           0.3403   \n",
+       "567           0.16500            0.86810           0.9387   \n",
+       "568           0.08996            0.06444           0.0000   \n",
+       "\n",
+       "     concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
+       "0                  0.2654          0.4601                  0.11890  \n",
+       "1                  0.1860          0.2750                  0.08902  \n",
+       "2                  0.2430          0.3613                  0.08758  \n",
+       "3                  0.2575          0.6638                  0.17300  \n",
+       "4                  0.1625          0.2364                  0.07678  \n",
+       "..                    ...             ...                      ...  \n",
+       "564                0.2216          0.2060                  0.07115  \n",
+       "565                0.1628          0.2572                  0.06637  \n",
+       "566                0.1418          0.2218                  0.07820  \n",
+       "567                0.2650          0.4087                  0.12400  \n",
+       "568                0.0000          0.2871                  0.07039  \n",
+       "\n",
+       "[569 rows x 32 columns]"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cancer = pd.read_csv('dataset/wdbc.csv')\n",
+    "cancer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Malignant', 'Benign'], dtype=object)"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# clean data\n",
+    "cancer[\"diagnosis\"] = cancer[\"diagnosis\"].replace({\n",
+    "    \"M\" : \"Malignant\",\n",
+    "    \"B\" : \"Benign\"\n",
+    "})\n",
+    "\n",
+    "cancer[\"diagnosis\"].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## Start fitting the model ### AW"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create a copy of the df\n",
+    "standardized_cancer = cancer.copy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# remove colns that not need to be scale\n",
+    "columns_to_exclude = ['id', 'diagnosis']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create a df that exclude the response variable and index\n",
+    "\n",
+    "columns_to_scale = standardized_cancer.columns.difference(columns_to_exclude)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# scale the df \n",
+    "scaler = StandardScaler()\n",
+    "standardized_cancer[columns_to_scale] = scaler.fit_transform(standardized_cancer[columns_to_scale])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>diagnosis</th>\n",
+       "      <th>radius_mean</th>\n",
+       "      <th>texture_mean</th>\n",
+       "      <th>perimeter_mean</th>\n",
+       "      <th>area_mean</th>\n",
+       "      <th>smoothness_mean</th>\n",
+       "      <th>compactness_mean</th>\n",
+       "      <th>concavity_mean</th>\n",
+       "      <th>concave points_mean</th>\n",
+       "      <th>...</th>\n",
+       "      <th>radius_worst</th>\n",
+       "      <th>texture_worst</th>\n",
+       "      <th>perimeter_worst</th>\n",
+       "      <th>area_worst</th>\n",
+       "      <th>smoothness_worst</th>\n",
+       "      <th>compactness_worst</th>\n",
+       "      <th>concavity_worst</th>\n",
+       "      <th>concave points_worst</th>\n",
+       "      <th>symmetry_worst</th>\n",
+       "      <th>fractal_dimension_worst</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>842302</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>1.097064</td>\n",
+       "      <td>-2.073335</td>\n",
+       "      <td>1.269934</td>\n",
+       "      <td>0.984375</td>\n",
+       "      <td>1.568466</td>\n",
+       "      <td>3.283515</td>\n",
+       "      <td>2.652874</td>\n",
+       "      <td>2.532475</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.886690</td>\n",
+       "      <td>-1.359293</td>\n",
+       "      <td>2.303601</td>\n",
+       "      <td>2.001237</td>\n",
+       "      <td>1.307686</td>\n",
+       "      <td>2.616665</td>\n",
+       "      <td>2.109526</td>\n",
+       "      <td>2.296076</td>\n",
+       "      <td>2.750622</td>\n",
+       "      <td>1.937015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>842517</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>1.829821</td>\n",
+       "      <td>-0.353632</td>\n",
+       "      <td>1.685955</td>\n",
+       "      <td>1.908708</td>\n",
+       "      <td>-0.826962</td>\n",
+       "      <td>-0.487072</td>\n",
+       "      <td>-0.023846</td>\n",
+       "      <td>0.548144</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.805927</td>\n",
+       "      <td>-0.369203</td>\n",
+       "      <td>1.535126</td>\n",
+       "      <td>1.890489</td>\n",
+       "      <td>-0.375612</td>\n",
+       "      <td>-0.430444</td>\n",
+       "      <td>-0.146749</td>\n",
+       "      <td>1.087084</td>\n",
+       "      <td>-0.243890</td>\n",
+       "      <td>0.281190</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>84300903</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>1.579888</td>\n",
+       "      <td>0.456187</td>\n",
+       "      <td>1.566503</td>\n",
+       "      <td>1.558884</td>\n",
+       "      <td>0.942210</td>\n",
+       "      <td>1.052926</td>\n",
+       "      <td>1.363478</td>\n",
+       "      <td>2.037231</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.511870</td>\n",
+       "      <td>-0.023974</td>\n",
+       "      <td>1.347475</td>\n",
+       "      <td>1.456285</td>\n",
+       "      <td>0.527407</td>\n",
+       "      <td>1.082932</td>\n",
+       "      <td>0.854974</td>\n",
+       "      <td>1.955000</td>\n",
+       "      <td>1.152255</td>\n",
+       "      <td>0.201391</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>84348301</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>-0.768909</td>\n",
+       "      <td>0.253732</td>\n",
+       "      <td>-0.592687</td>\n",
+       "      <td>-0.764464</td>\n",
+       "      <td>3.283553</td>\n",
+       "      <td>3.402909</td>\n",
+       "      <td>1.915897</td>\n",
+       "      <td>1.451707</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.281464</td>\n",
+       "      <td>0.133984</td>\n",
+       "      <td>-0.249939</td>\n",
+       "      <td>-0.550021</td>\n",
+       "      <td>3.394275</td>\n",
+       "      <td>3.893397</td>\n",
+       "      <td>1.989588</td>\n",
+       "      <td>2.175786</td>\n",
+       "      <td>6.046041</td>\n",
+       "      <td>4.935010</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>84358402</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>1.750297</td>\n",
+       "      <td>-1.151816</td>\n",
+       "      <td>1.776573</td>\n",
+       "      <td>1.826229</td>\n",
+       "      <td>0.280372</td>\n",
+       "      <td>0.539340</td>\n",
+       "      <td>1.371011</td>\n",
+       "      <td>1.428493</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.298575</td>\n",
+       "      <td>-1.466770</td>\n",
+       "      <td>1.338539</td>\n",
+       "      <td>1.220724</td>\n",
+       "      <td>0.220556</td>\n",
+       "      <td>-0.313395</td>\n",
+       "      <td>0.613179</td>\n",
+       "      <td>0.729259</td>\n",
+       "      <td>-0.868353</td>\n",
+       "      <td>-0.397100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>564</th>\n",
+       "      <td>926424</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>2.110995</td>\n",
+       "      <td>0.721473</td>\n",
+       "      <td>2.060786</td>\n",
+       "      <td>2.343856</td>\n",
+       "      <td>1.041842</td>\n",
+       "      <td>0.219060</td>\n",
+       "      <td>1.947285</td>\n",
+       "      <td>2.320965</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.901185</td>\n",
+       "      <td>0.117700</td>\n",
+       "      <td>1.752563</td>\n",
+       "      <td>2.015301</td>\n",
+       "      <td>0.378365</td>\n",
+       "      <td>-0.273318</td>\n",
+       "      <td>0.664512</td>\n",
+       "      <td>1.629151</td>\n",
+       "      <td>-1.360158</td>\n",
+       "      <td>-0.709091</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>565</th>\n",
+       "      <td>926682</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>1.704854</td>\n",
+       "      <td>2.085134</td>\n",
+       "      <td>1.615931</td>\n",
+       "      <td>1.723842</td>\n",
+       "      <td>0.102458</td>\n",
+       "      <td>-0.017833</td>\n",
+       "      <td>0.693043</td>\n",
+       "      <td>1.263669</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.536720</td>\n",
+       "      <td>2.047399</td>\n",
+       "      <td>1.421940</td>\n",
+       "      <td>1.494959</td>\n",
+       "      <td>-0.691230</td>\n",
+       "      <td>-0.394820</td>\n",
+       "      <td>0.236573</td>\n",
+       "      <td>0.733827</td>\n",
+       "      <td>-0.531855</td>\n",
+       "      <td>-0.973978</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>566</th>\n",
+       "      <td>926954</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>0.702284</td>\n",
+       "      <td>2.045574</td>\n",
+       "      <td>0.672676</td>\n",
+       "      <td>0.577953</td>\n",
+       "      <td>-0.840484</td>\n",
+       "      <td>-0.038680</td>\n",
+       "      <td>0.046588</td>\n",
+       "      <td>0.105777</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.561361</td>\n",
+       "      <td>1.374854</td>\n",
+       "      <td>0.579001</td>\n",
+       "      <td>0.427906</td>\n",
+       "      <td>-0.809587</td>\n",
+       "      <td>0.350735</td>\n",
+       "      <td>0.326767</td>\n",
+       "      <td>0.414069</td>\n",
+       "      <td>-1.104549</td>\n",
+       "      <td>-0.318409</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>567</th>\n",
+       "      <td>927241</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>1.838341</td>\n",
+       "      <td>2.336457</td>\n",
+       "      <td>1.982524</td>\n",
+       "      <td>1.735218</td>\n",
+       "      <td>1.525767</td>\n",
+       "      <td>3.272144</td>\n",
+       "      <td>3.296944</td>\n",
+       "      <td>2.658866</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.961239</td>\n",
+       "      <td>2.237926</td>\n",
+       "      <td>2.303601</td>\n",
+       "      <td>1.653171</td>\n",
+       "      <td>1.430427</td>\n",
+       "      <td>3.904848</td>\n",
+       "      <td>3.197605</td>\n",
+       "      <td>2.289985</td>\n",
+       "      <td>1.919083</td>\n",
+       "      <td>2.219635</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>568</th>\n",
+       "      <td>92751</td>\n",
+       "      <td>Benign</td>\n",
+       "      <td>-1.808401</td>\n",
+       "      <td>1.221792</td>\n",
+       "      <td>-1.814389</td>\n",
+       "      <td>-1.347789</td>\n",
+       "      <td>-3.112085</td>\n",
+       "      <td>-1.150752</td>\n",
+       "      <td>-1.114873</td>\n",
+       "      <td>-1.261820</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-1.410893</td>\n",
+       "      <td>0.764190</td>\n",
+       "      <td>-1.432735</td>\n",
+       "      <td>-1.075813</td>\n",
+       "      <td>-1.859019</td>\n",
+       "      <td>-1.207552</td>\n",
+       "      <td>-1.305831</td>\n",
+       "      <td>-1.745063</td>\n",
+       "      <td>-0.048138</td>\n",
+       "      <td>-0.751207</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>569 rows × 32 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           id  diagnosis  radius_mean  texture_mean  perimeter_mean  \\\n",
+       "0      842302  Malignant     1.097064     -2.073335        1.269934   \n",
+       "1      842517  Malignant     1.829821     -0.353632        1.685955   \n",
+       "2    84300903  Malignant     1.579888      0.456187        1.566503   \n",
+       "3    84348301  Malignant    -0.768909      0.253732       -0.592687   \n",
+       "4    84358402  Malignant     1.750297     -1.151816        1.776573   \n",
+       "..        ...        ...          ...           ...             ...   \n",
+       "564    926424  Malignant     2.110995      0.721473        2.060786   \n",
+       "565    926682  Malignant     1.704854      2.085134        1.615931   \n",
+       "566    926954  Malignant     0.702284      2.045574        0.672676   \n",
+       "567    927241  Malignant     1.838341      2.336457        1.982524   \n",
+       "568     92751     Benign    -1.808401      1.221792       -1.814389   \n",
+       "\n",
+       "     area_mean  smoothness_mean  compactness_mean  concavity_mean  \\\n",
+       "0     0.984375         1.568466          3.283515        2.652874   \n",
+       "1     1.908708        -0.826962         -0.487072       -0.023846   \n",
+       "2     1.558884         0.942210          1.052926        1.363478   \n",
+       "3    -0.764464         3.283553          3.402909        1.915897   \n",
+       "4     1.826229         0.280372          0.539340        1.371011   \n",
+       "..         ...              ...               ...             ...   \n",
+       "564   2.343856         1.041842          0.219060        1.947285   \n",
+       "565   1.723842         0.102458         -0.017833        0.693043   \n",
+       "566   0.577953        -0.840484         -0.038680        0.046588   \n",
+       "567   1.735218         1.525767          3.272144        3.296944   \n",
+       "568  -1.347789        -3.112085         -1.150752       -1.114873   \n",
+       "\n",
+       "     concave points_mean  ...  radius_worst  texture_worst  perimeter_worst  \\\n",
+       "0               2.532475  ...      1.886690      -1.359293         2.303601   \n",
+       "1               0.548144  ...      1.805927      -0.369203         1.535126   \n",
+       "2               2.037231  ...      1.511870      -0.023974         1.347475   \n",
+       "3               1.451707  ...     -0.281464       0.133984        -0.249939   \n",
+       "4               1.428493  ...      1.298575      -1.466770         1.338539   \n",
+       "..                   ...  ...           ...            ...              ...   \n",
+       "564             2.320965  ...      1.901185       0.117700         1.752563   \n",
+       "565             1.263669  ...      1.536720       2.047399         1.421940   \n",
+       "566             0.105777  ...      0.561361       1.374854         0.579001   \n",
+       "567             2.658866  ...      1.961239       2.237926         2.303601   \n",
+       "568            -1.261820  ...     -1.410893       0.764190        -1.432735   \n",
+       "\n",
+       "     area_worst  smoothness_worst  compactness_worst  concavity_worst  \\\n",
+       "0      2.001237          1.307686           2.616665         2.109526   \n",
+       "1      1.890489         -0.375612          -0.430444        -0.146749   \n",
+       "2      1.456285          0.527407           1.082932         0.854974   \n",
+       "3     -0.550021          3.394275           3.893397         1.989588   \n",
+       "4      1.220724          0.220556          -0.313395         0.613179   \n",
+       "..          ...               ...                ...              ...   \n",
+       "564    2.015301          0.378365          -0.273318         0.664512   \n",
+       "565    1.494959         -0.691230          -0.394820         0.236573   \n",
+       "566    0.427906         -0.809587           0.350735         0.326767   \n",
+       "567    1.653171          1.430427           3.904848         3.197605   \n",
+       "568   -1.075813         -1.859019          -1.207552        -1.305831   \n",
+       "\n",
+       "     concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
+       "0                2.296076        2.750622                 1.937015  \n",
+       "1                1.087084       -0.243890                 0.281190  \n",
+       "2                1.955000        1.152255                 0.201391  \n",
+       "3                2.175786        6.046041                 4.935010  \n",
+       "4                0.729259       -0.868353                -0.397100  \n",
+       "..                    ...             ...                      ...  \n",
+       "564              1.629151       -1.360158                -0.709091  \n",
+       "565              0.733827       -0.531855                -0.973978  \n",
+       "566              0.414069       -1.104549                -0.318409  \n",
+       "567              2.289985        1.919083                 2.219635  \n",
+       "568             -1.745063       -0.048138                -0.751207  \n",
+       "\n",
+       "[569 rows x 32 columns]"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# call again the whole df\n",
+    "standardized_cancer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>diagnosis</th>\n",
+       "      <th>radius_mean</th>\n",
+       "      <th>texture_mean</th>\n",
+       "      <th>perimeter_mean</th>\n",
+       "      <th>area_mean</th>\n",
+       "      <th>smoothness_mean</th>\n",
+       "      <th>compactness_mean</th>\n",
+       "      <th>concavity_mean</th>\n",
+       "      <th>concave points_mean</th>\n",
+       "      <th>...</th>\n",
+       "      <th>radius_worst</th>\n",
+       "      <th>texture_worst</th>\n",
+       "      <th>perimeter_worst</th>\n",
+       "      <th>area_worst</th>\n",
+       "      <th>smoothness_worst</th>\n",
+       "      <th>compactness_worst</th>\n",
+       "      <th>concavity_worst</th>\n",
+       "      <th>concave points_worst</th>\n",
+       "      <th>symmetry_worst</th>\n",
+       "      <th>fractal_dimension_worst</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>842302</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>1.097064</td>\n",
+       "      <td>-2.073335</td>\n",
+       "      <td>1.269934</td>\n",
+       "      <td>0.984375</td>\n",
+       "      <td>1.568466</td>\n",
+       "      <td>3.283515</td>\n",
+       "      <td>2.652874</td>\n",
+       "      <td>2.532475</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.886690</td>\n",
+       "      <td>-1.359293</td>\n",
+       "      <td>2.303601</td>\n",
+       "      <td>2.001237</td>\n",
+       "      <td>1.307686</td>\n",
+       "      <td>2.616665</td>\n",
+       "      <td>2.109526</td>\n",
+       "      <td>2.296076</td>\n",
+       "      <td>2.750622</td>\n",
+       "      <td>1.937015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>842517</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>1.829821</td>\n",
+       "      <td>-0.353632</td>\n",
+       "      <td>1.685955</td>\n",
+       "      <td>1.908708</td>\n",
+       "      <td>-0.826962</td>\n",
+       "      <td>-0.487072</td>\n",
+       "      <td>-0.023846</td>\n",
+       "      <td>0.548144</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.805927</td>\n",
+       "      <td>-0.369203</td>\n",
+       "      <td>1.535126</td>\n",
+       "      <td>1.890489</td>\n",
+       "      <td>-0.375612</td>\n",
+       "      <td>-0.430444</td>\n",
+       "      <td>-0.146749</td>\n",
+       "      <td>1.087084</td>\n",
+       "      <td>-0.243890</td>\n",
+       "      <td>0.281190</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>84300903</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>1.579888</td>\n",
+       "      <td>0.456187</td>\n",
+       "      <td>1.566503</td>\n",
+       "      <td>1.558884</td>\n",
+       "      <td>0.942210</td>\n",
+       "      <td>1.052926</td>\n",
+       "      <td>1.363478</td>\n",
+       "      <td>2.037231</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.511870</td>\n",
+       "      <td>-0.023974</td>\n",
+       "      <td>1.347475</td>\n",
+       "      <td>1.456285</td>\n",
+       "      <td>0.527407</td>\n",
+       "      <td>1.082932</td>\n",
+       "      <td>0.854974</td>\n",
+       "      <td>1.955000</td>\n",
+       "      <td>1.152255</td>\n",
+       "      <td>0.201391</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>84348301</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>-0.768909</td>\n",
+       "      <td>0.253732</td>\n",
+       "      <td>-0.592687</td>\n",
+       "      <td>-0.764464</td>\n",
+       "      <td>3.283553</td>\n",
+       "      <td>3.402909</td>\n",
+       "      <td>1.915897</td>\n",
+       "      <td>1.451707</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.281464</td>\n",
+       "      <td>0.133984</td>\n",
+       "      <td>-0.249939</td>\n",
+       "      <td>-0.550021</td>\n",
+       "      <td>3.394275</td>\n",
+       "      <td>3.893397</td>\n",
+       "      <td>1.989588</td>\n",
+       "      <td>2.175786</td>\n",
+       "      <td>6.046041</td>\n",
+       "      <td>4.935010</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>84358402</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>1.750297</td>\n",
+       "      <td>-1.151816</td>\n",
+       "      <td>1.776573</td>\n",
+       "      <td>1.826229</td>\n",
+       "      <td>0.280372</td>\n",
+       "      <td>0.539340</td>\n",
+       "      <td>1.371011</td>\n",
+       "      <td>1.428493</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.298575</td>\n",
+       "      <td>-1.466770</td>\n",
+       "      <td>1.338539</td>\n",
+       "      <td>1.220724</td>\n",
+       "      <td>0.220556</td>\n",
+       "      <td>-0.313395</td>\n",
+       "      <td>0.613179</td>\n",
+       "      <td>0.729259</td>\n",
+       "      <td>-0.868353</td>\n",
+       "      <td>-0.397100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>564</th>\n",
+       "      <td>926424</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>2.110995</td>\n",
+       "      <td>0.721473</td>\n",
+       "      <td>2.060786</td>\n",
+       "      <td>2.343856</td>\n",
+       "      <td>1.041842</td>\n",
+       "      <td>0.219060</td>\n",
+       "      <td>1.947285</td>\n",
+       "      <td>2.320965</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.901185</td>\n",
+       "      <td>0.117700</td>\n",
+       "      <td>1.752563</td>\n",
+       "      <td>2.015301</td>\n",
+       "      <td>0.378365</td>\n",
+       "      <td>-0.273318</td>\n",
+       "      <td>0.664512</td>\n",
+       "      <td>1.629151</td>\n",
+       "      <td>-1.360158</td>\n",
+       "      <td>-0.709091</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>565</th>\n",
+       "      <td>926682</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>1.704854</td>\n",
+       "      <td>2.085134</td>\n",
+       "      <td>1.615931</td>\n",
+       "      <td>1.723842</td>\n",
+       "      <td>0.102458</td>\n",
+       "      <td>-0.017833</td>\n",
+       "      <td>0.693043</td>\n",
+       "      <td>1.263669</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.536720</td>\n",
+       "      <td>2.047399</td>\n",
+       "      <td>1.421940</td>\n",
+       "      <td>1.494959</td>\n",
+       "      <td>-0.691230</td>\n",
+       "      <td>-0.394820</td>\n",
+       "      <td>0.236573</td>\n",
+       "      <td>0.733827</td>\n",
+       "      <td>-0.531855</td>\n",
+       "      <td>-0.973978</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>566</th>\n",
+       "      <td>926954</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>0.702284</td>\n",
+       "      <td>2.045574</td>\n",
+       "      <td>0.672676</td>\n",
+       "      <td>0.577953</td>\n",
+       "      <td>-0.840484</td>\n",
+       "      <td>-0.038680</td>\n",
+       "      <td>0.046588</td>\n",
+       "      <td>0.105777</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.561361</td>\n",
+       "      <td>1.374854</td>\n",
+       "      <td>0.579001</td>\n",
+       "      <td>0.427906</td>\n",
+       "      <td>-0.809587</td>\n",
+       "      <td>0.350735</td>\n",
+       "      <td>0.326767</td>\n",
+       "      <td>0.414069</td>\n",
+       "      <td>-1.104549</td>\n",
+       "      <td>-0.318409</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>567</th>\n",
+       "      <td>927241</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>1.838341</td>\n",
+       "      <td>2.336457</td>\n",
+       "      <td>1.982524</td>\n",
+       "      <td>1.735218</td>\n",
+       "      <td>1.525767</td>\n",
+       "      <td>3.272144</td>\n",
+       "      <td>3.296944</td>\n",
+       "      <td>2.658866</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.961239</td>\n",
+       "      <td>2.237926</td>\n",
+       "      <td>2.303601</td>\n",
+       "      <td>1.653171</td>\n",
+       "      <td>1.430427</td>\n",
+       "      <td>3.904848</td>\n",
+       "      <td>3.197605</td>\n",
+       "      <td>2.289985</td>\n",
+       "      <td>1.919083</td>\n",
+       "      <td>2.219635</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>568</th>\n",
+       "      <td>92751</td>\n",
+       "      <td>Benign</td>\n",
+       "      <td>-1.808401</td>\n",
+       "      <td>1.221792</td>\n",
+       "      <td>-1.814389</td>\n",
+       "      <td>-1.347789</td>\n",
+       "      <td>-3.112085</td>\n",
+       "      <td>-1.150752</td>\n",
+       "      <td>-1.114873</td>\n",
+       "      <td>-1.261820</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-1.410893</td>\n",
+       "      <td>0.764190</td>\n",
+       "      <td>-1.432735</td>\n",
+       "      <td>-1.075813</td>\n",
+       "      <td>-1.859019</td>\n",
+       "      <td>-1.207552</td>\n",
+       "      <td>-1.305831</td>\n",
+       "      <td>-1.745063</td>\n",
+       "      <td>-0.048138</td>\n",
+       "      <td>-0.751207</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>569 rows × 32 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           id  diagnosis  radius_mean  texture_mean  perimeter_mean  \\\n",
+       "0      842302  Malignant     1.097064     -2.073335        1.269934   \n",
+       "1      842517  Malignant     1.829821     -0.353632        1.685955   \n",
+       "2    84300903  Malignant     1.579888      0.456187        1.566503   \n",
+       "3    84348301  Malignant    -0.768909      0.253732       -0.592687   \n",
+       "4    84358402  Malignant     1.750297     -1.151816        1.776573   \n",
+       "..        ...        ...          ...           ...             ...   \n",
+       "564    926424  Malignant     2.110995      0.721473        2.060786   \n",
+       "565    926682  Malignant     1.704854      2.085134        1.615931   \n",
+       "566    926954  Malignant     0.702284      2.045574        0.672676   \n",
+       "567    927241  Malignant     1.838341      2.336457        1.982524   \n",
+       "568     92751     Benign    -1.808401      1.221792       -1.814389   \n",
+       "\n",
+       "     area_mean  smoothness_mean  compactness_mean  concavity_mean  \\\n",
+       "0     0.984375         1.568466          3.283515        2.652874   \n",
+       "1     1.908708        -0.826962         -0.487072       -0.023846   \n",
+       "2     1.558884         0.942210          1.052926        1.363478   \n",
+       "3    -0.764464         3.283553          3.402909        1.915897   \n",
+       "4     1.826229         0.280372          0.539340        1.371011   \n",
+       "..         ...              ...               ...             ...   \n",
+       "564   2.343856         1.041842          0.219060        1.947285   \n",
+       "565   1.723842         0.102458         -0.017833        0.693043   \n",
+       "566   0.577953        -0.840484         -0.038680        0.046588   \n",
+       "567   1.735218         1.525767          3.272144        3.296944   \n",
+       "568  -1.347789        -3.112085         -1.150752       -1.114873   \n",
+       "\n",
+       "     concave points_mean  ...  radius_worst  texture_worst  perimeter_worst  \\\n",
+       "0               2.532475  ...      1.886690      -1.359293         2.303601   \n",
+       "1               0.548144  ...      1.805927      -0.369203         1.535126   \n",
+       "2               2.037231  ...      1.511870      -0.023974         1.347475   \n",
+       "3               1.451707  ...     -0.281464       0.133984        -0.249939   \n",
+       "4               1.428493  ...      1.298575      -1.466770         1.338539   \n",
+       "..                   ...  ...           ...            ...              ...   \n",
+       "564             2.320965  ...      1.901185       0.117700         1.752563   \n",
+       "565             1.263669  ...      1.536720       2.047399         1.421940   \n",
+       "566             0.105777  ...      0.561361       1.374854         0.579001   \n",
+       "567             2.658866  ...      1.961239       2.237926         2.303601   \n",
+       "568            -1.261820  ...     -1.410893       0.764190        -1.432735   \n",
+       "\n",
+       "     area_worst  smoothness_worst  compactness_worst  concavity_worst  \\\n",
+       "0      2.001237          1.307686           2.616665         2.109526   \n",
+       "1      1.890489         -0.375612          -0.430444        -0.146749   \n",
+       "2      1.456285          0.527407           1.082932         0.854974   \n",
+       "3     -0.550021          3.394275           3.893397         1.989588   \n",
+       "4      1.220724          0.220556          -0.313395         0.613179   \n",
+       "..          ...               ...                ...              ...   \n",
+       "564    2.015301          0.378365          -0.273318         0.664512   \n",
+       "565    1.494959         -0.691230          -0.394820         0.236573   \n",
+       "566    0.427906         -0.809587           0.350735         0.326767   \n",
+       "567    1.653171          1.430427           3.904848         3.197605   \n",
+       "568   -1.075813         -1.859019          -1.207552        -1.305831   \n",
+       "\n",
+       "     concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
+       "0                2.296076        2.750622                 1.937015  \n",
+       "1                1.087084       -0.243890                 0.281190  \n",
+       "2                1.955000        1.152255                 0.201391  \n",
+       "3                2.175786        6.046041                 4.935010  \n",
+       "4                0.729259       -0.868353                -0.397100  \n",
+       "..                    ...             ...                      ...  \n",
+       "564              1.629151       -1.360158                -0.709091  \n",
+       "565              0.733827       -0.531855                -0.973978  \n",
+       "566              0.414069       -1.104549                -0.318409  \n",
+       "567              2.289985        1.919083                 2.219635  \n",
+       "568             -1.745063       -0.048138                -0.751207  \n",
+       "\n",
+       "[569 rows x 32 columns]"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "standardized_cancer = cancer.copy()\n",
+    "\n",
+    "columns_to_exclude = ['id', 'diagnosis']\n",
+    "\n",
+    "columns_to_scale = standardized_cancer.columns.difference(columns_to_exclude)\n",
+    "\n",
+    "scaler = StandardScaler()\n",
+    "standardized_cancer[columns_to_scale] = scaler.fit_transform(standardized_cancer[columns_to_scale])\n",
+    "standardized_cancer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#np.random.seed(1)\n",
+    "\n",
+    "cancer_train, cancer_test = train_test_split(\n",
+    "    standardized_cancer, train_size=0.75, shuffle= True,\n",
+    "    stratify=standardized_cancer[\"diagnosis\"], random_state= 123\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 143 entries, 257 to 200\n",
+      "Data columns (total 32 columns):\n",
+      " #   Column                   Non-Null Count  Dtype  \n",
+      "---  ------                   --------------  -----  \n",
+      " 0   id                       143 non-null    int64  \n",
+      " 1   diagnosis                143 non-null    object \n",
+      " 2   radius_mean              143 non-null    float64\n",
+      " 3   texture_mean             143 non-null    float64\n",
+      " 4   perimeter_mean           143 non-null    float64\n",
+      " 5   area_mean                143 non-null    float64\n",
+      " 6   smoothness_mean          143 non-null    float64\n",
+      " 7   compactness_mean         143 non-null    float64\n",
+      " 8   concavity_mean           143 non-null    float64\n",
+      " 9   concave points_mean      143 non-null    float64\n",
+      " 10  symmetry_mean            143 non-null    float64\n",
+      " 11  fractal_dimension_mean   143 non-null    float64\n",
+      " 12  radius_se                143 non-null    float64\n",
+      " 13  texture_se               143 non-null    float64\n",
+      " 14  perimeter_se             143 non-null    float64\n",
+      " 15  area_se                  143 non-null    float64\n",
+      " 16  smoothness_se            143 non-null    float64\n",
+      " 17  compactness_se           143 non-null    float64\n",
+      " 18  concavity_se             143 non-null    float64\n",
+      " 19  concave points_se        143 non-null    float64\n",
+      " 20  symmetry_se              143 non-null    float64\n",
+      " 21  fractal_dimension_se     143 non-null    float64\n",
+      " 22  radius_worst             143 non-null    float64\n",
+      " 23  texture_worst            143 non-null    float64\n",
+      " 24  perimeter_worst          143 non-null    float64\n",
+      " 25  area_worst               143 non-null    float64\n",
+      " 26  smoothness_worst         143 non-null    float64\n",
+      " 27  compactness_worst        143 non-null    float64\n",
+      " 28  concavity_worst          143 non-null    float64\n",
+      " 29  concave points_worst     143 non-null    float64\n",
+      " 30  symmetry_worst           143 non-null    float64\n",
+      " 31  fractal_dimension_worst  143 non-null    float64\n",
+      "dtypes: float64(30), int64(1), object(1)\n",
+      "memory usage: 36.9+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "#cancer_train.info()\n",
+    "cancer_test.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "knn = KNeighborsClassifier(n_neighbors=5\n",
+    "                           )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = cancer_train[['perimeter_mean','concavity_mean']]\n",
+    "y = cancer_train ['diagnosis']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-3 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-3 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-3 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-3 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-3 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;KNeighborsClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsClassifier()</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "KNeighborsClassifier()"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "knn.fit(X,y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>diagnosis</th>\n",
+       "      <th>predicted</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>257</th>\n",
+       "      <td>886776</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>Malignant</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>382</th>\n",
+       "      <td>90250</td>\n",
+       "      <td>Benign</td>\n",
+       "      <td>Benign</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>241</th>\n",
+       "      <td>883539</td>\n",
+       "      <td>Benign</td>\n",
+       "      <td>Benign</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>527</th>\n",
+       "      <td>91813702</td>\n",
+       "      <td>Benign</td>\n",
+       "      <td>Benign</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>368</th>\n",
+       "      <td>9011971</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>Malignant</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>8510653</td>\n",
+       "      <td>Benign</td>\n",
+       "      <td>Benign</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>247</th>\n",
+       "      <td>884626</td>\n",
+       "      <td>Benign</td>\n",
+       "      <td>Malignant</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>853201</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>Malignant</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>177</th>\n",
+       "      <td>87281702</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>Malignant</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>200</th>\n",
+       "      <td>877501</td>\n",
+       "      <td>Benign</td>\n",
+       "      <td>Benign</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>143 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           id  diagnosis  predicted\n",
+       "257    886776  Malignant  Malignant\n",
+       "382     90250     Benign     Benign\n",
+       "241    883539     Benign     Benign\n",
+       "527  91813702     Benign     Benign\n",
+       "368   9011971  Malignant  Malignant\n",
+       "..        ...        ...        ...\n",
+       "20    8510653     Benign     Benign\n",
+       "247    884626     Benign  Malignant\n",
+       "29     853201  Malignant  Malignant\n",
+       "177  87281702  Malignant  Malignant\n",
+       "200    877501     Benign     Benign\n",
+       "\n",
+       "[143 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cancer_test[\"predicted\"] = knn.predict(cancer_test[[\"perimeter_mean\", \"concavity_mean\"]])\n",
+    "cancer_test[[\"id\", \"diagnosis\", \"predicted\"]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9230769230769231"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "knn.score(\n",
+    "    cancer_test[[\"perimeter_mean\", \"concavity_mean\"]],\n",
+    "    cancer_test[\"diagnosis\"]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>Predicted</th>\n",
+       "      <th>Benign</th>\n",
+       "      <th>Malignant</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Actual</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Benign</th>\n",
+       "      <td>84</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Malignant</th>\n",
+       "      <td>5</td>\n",
+       "      <td>48</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Predicted  Benign  Malignant\n",
+       "Actual                      \n",
+       "Benign         84          6\n",
+       "Malignant       5         48"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.crosstab(\n",
+    "    cancer_test[\"diagnosis\"],\n",
+    "    cancer_test[\"predicted\"],\n",
+    "    rownames = ['Actual'],\n",
+    "    colnames = ['Predicted']\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.8888888888888888"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "precision_score(\n",
+    "    y_true=cancer_test[\"diagnosis\"],\n",
+    "    y_pred=cancer_test[\"predicted\"],\n",
+    "    pos_label=\"Malignant\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9056603773584906"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "recall_score(\n",
+    "    y_true=cancer_test[\"diagnosis\"],\n",
+    "    y_pred=cancer_test[\"predicted\"],\n",
+    "    pos_label=\"Malignant\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-4 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-4 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-4 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-4 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-4 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;KNeighborsClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsClassifier(n_neighbors=3)</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "KNeighborsClassifier(n_neighbors=3)"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cancer_subtrain, cancer_validation = train_test_split(\n",
+    "    cancer_train, train_size=0.75, stratify=cancer_train[\"diagnosis\"]\n",
+    ")\n",
+    "\n",
+    "# fit the model on the sub-training data\n",
+    "knn = KNeighborsClassifier(n_neighbors=3)\n",
+    "X = cancer_subtrain[[\"perimeter_mean\", \"concavity_mean\"]]\n",
+    "y = cancer_subtrain[\"diagnosis\"]\n",
+    "knn.fit(X, y)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.8785046728971962"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "acc = knn.score(\n",
+    "    cancer_validation[[\"perimeter_mean\", \"concavity_mean\"]],\n",
+    "    cancer_validation[\"diagnosis\"]\n",
+    ")\n",
+    "acc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>fit_time</th>\n",
+       "      <th>score_time</th>\n",
+       "      <th>test_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.009519</td>\n",
+       "      <td>0.007506</td>\n",
+       "      <td>0.906977</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.004515</td>\n",
+       "      <td>0.007529</td>\n",
+       "      <td>0.905882</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.002006</td>\n",
+       "      <td>0.010254</td>\n",
+       "      <td>0.929412</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.005520</td>\n",
+       "      <td>0.011033</td>\n",
+       "      <td>0.894118</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.003000</td>\n",
+       "      <td>0.009766</td>\n",
+       "      <td>0.894118</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   fit_time  score_time  test_score\n",
+       "0  0.009519    0.007506    0.906977\n",
+       "1  0.004515    0.007529    0.905882\n",
+       "2  0.002006    0.010254    0.929412\n",
+       "3  0.005520    0.011033    0.894118\n",
+       "4  0.003000    0.009766    0.894118"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "knn = KNeighborsClassifier(n_neighbors=3)\n",
+    "X = cancer_train[[\"perimeter_mean\", \"concavity_mean\"]]\n",
+    "y = cancer_train[\"diagnosis\"]\n",
+    "\n",
+    "returned_dictionary = cross_validate(\n",
+    "    estimator=knn,\n",
+    "    cv=5,    # setting up the cross validation number\n",
+    "    X=X,\n",
+    "    y=y\n",
+    ")\n",
+    "\n",
+    "cv_5_df = pd.DataFrame(returned_dictionary)    # Converting it to pandas DataFrame\n",
+    "\n",
+    "cv_5_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>fit_time</th>\n",
+       "      <th>score_time</th>\n",
+       "      <th>test_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>0.004912</td>\n",
+       "      <td>0.009218</td>\n",
+       "      <td>0.906101</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sem</th>\n",
+       "      <td>0.001301</td>\n",
+       "      <td>0.000723</td>\n",
+       "      <td>0.006448</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      fit_time  score_time  test_score\n",
+       "mean  0.004912    0.009218    0.906101\n",
+       "sem   0.001301    0.000723    0.006448"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cv_5_metrics = cv_5_df.agg([\"mean\", \"sem\"])\n",
+    "\n",
+    "cv_5_metrics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "parameter_grid = {\n",
+    "    \"n_neighbors\": range(1, 100, 5),\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cancer_tune_grid = GridSearchCV(\n",
+    "    estimator=knn,\n",
+    "    param_grid=parameter_grid,\n",
+    "    cv=10\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-5 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-5 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-5 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-5 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-5 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-5 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-5 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=10, estimator=KNeighborsClassifier(n_neighbors=3),\n",
+       "             param_grid={&#x27;n_neighbors&#x27;: range(1, 100, 5)})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=10, estimator=KNeighborsClassifier(n_neighbors=3),\n",
+       "             param_grid={&#x27;n_neighbors&#x27;: range(1, 100, 5)})</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">best_estimator_: KNeighborsClassifier</label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsClassifier(n_neighbors=81)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;KNeighborsClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsClassifier(n_neighbors=81)</pre></div> </div></div></div></div></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "GridSearchCV(cv=10, estimator=KNeighborsClassifier(n_neighbors=3),\n",
+       "             param_grid={'n_neighbors': range(1, 100, 5)})"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cancer_tune_grid.fit(\n",
+    "    cancer_train[[\"perimeter_mean\", \"concavity_mean\"]],\n",
+    "    cancer_train[\"diagnosis\"]\n",
+    ")\n",
+    "\n",
+    "#accuracies_grid = pd.DataFrame(cancer_tune_grid.cv_results_)\n",
+    "#accuracies_grid"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mean_fit_time</th>\n",
+       "      <th>std_fit_time</th>\n",
+       "      <th>mean_score_time</th>\n",
+       "      <th>std_score_time</th>\n",
+       "      <th>param_n_neighbors</th>\n",
+       "      <th>params</th>\n",
+       "      <th>split0_test_score</th>\n",
+       "      <th>split1_test_score</th>\n",
+       "      <th>split2_test_score</th>\n",
+       "      <th>split3_test_score</th>\n",
+       "      <th>split4_test_score</th>\n",
+       "      <th>split5_test_score</th>\n",
+       "      <th>split6_test_score</th>\n",
+       "      <th>split7_test_score</th>\n",
+       "      <th>split8_test_score</th>\n",
+       "      <th>split9_test_score</th>\n",
+       "      <th>mean_test_score</th>\n",
+       "      <th>std_test_score</th>\n",
+       "      <th>rank_test_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.002227</td>\n",
+       "      <td>0.000748</td>\n",
+       "      <td>0.005310</td>\n",
+       "      <td>0.001341</td>\n",
+       "      <td>1</td>\n",
+       "      <td>{'n_neighbors': 1}</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.860465</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.883721</td>\n",
+       "      <td>0.883721</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.857143</td>\n",
+       "      <td>0.857143</td>\n",
+       "      <td>0.880952</td>\n",
+       "      <td>0.904762</td>\n",
+       "      <td>0.901163</td>\n",
+       "      <td>0.044625</td>\n",
+       "      <td>20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.002966</td>\n",
+       "      <td>0.000881</td>\n",
+       "      <td>0.004359</td>\n",
+       "      <td>0.000745</td>\n",
+       "      <td>6</td>\n",
+       "      <td>{'n_neighbors': 6}</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.976744</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.904762</td>\n",
+       "      <td>0.857143</td>\n",
+       "      <td>0.880952</td>\n",
+       "      <td>0.928571</td>\n",
+       "      <td>0.919934</td>\n",
+       "      <td>0.034188</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.002659</td>\n",
+       "      <td>0.001008</td>\n",
+       "      <td>0.005164</td>\n",
+       "      <td>0.001184</td>\n",
+       "      <td>11</td>\n",
+       "      <td>{'n_neighbors': 11}</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.883721</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.904762</td>\n",
+       "      <td>0.857143</td>\n",
+       "      <td>0.857143</td>\n",
+       "      <td>0.904762</td>\n",
+       "      <td>0.908195</td>\n",
+       "      <td>0.031352</td>\n",
+       "      <td>18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.002157</td>\n",
+       "      <td>0.000382</td>\n",
+       "      <td>0.004882</td>\n",
+       "      <td>0.001504</td>\n",
+       "      <td>16</td>\n",
+       "      <td>{'n_neighbors': 16}</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.860465</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.880952</td>\n",
+       "      <td>0.857143</td>\n",
+       "      <td>0.857143</td>\n",
+       "      <td>0.928571</td>\n",
+       "      <td>0.910520</td>\n",
+       "      <td>0.040990</td>\n",
+       "      <td>15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.002278</td>\n",
+       "      <td>0.000706</td>\n",
+       "      <td>0.005006</td>\n",
+       "      <td>0.001240</td>\n",
+       "      <td>21</td>\n",
+       "      <td>{'n_neighbors': 21}</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.883721</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.880952</td>\n",
+       "      <td>0.857143</td>\n",
+       "      <td>0.857143</td>\n",
+       "      <td>0.928571</td>\n",
+       "      <td>0.910520</td>\n",
+       "      <td>0.036819</td>\n",
+       "      <td>15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0.002358</td>\n",
+       "      <td>0.000396</td>\n",
+       "      <td>0.004752</td>\n",
+       "      <td>0.001247</td>\n",
+       "      <td>26</td>\n",
+       "      <td>{'n_neighbors': 26}</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.860465</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.880952</td>\n",
+       "      <td>0.857143</td>\n",
+       "      <td>0.857143</td>\n",
+       "      <td>0.928571</td>\n",
+       "      <td>0.910520</td>\n",
+       "      <td>0.040990</td>\n",
+       "      <td>15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>0.002405</td>\n",
+       "      <td>0.000453</td>\n",
+       "      <td>0.004578</td>\n",
+       "      <td>0.001462</td>\n",
+       "      <td>31</td>\n",
+       "      <td>{'n_neighbors': 31}</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.860465</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.880952</td>\n",
+       "      <td>0.857143</td>\n",
+       "      <td>0.833333</td>\n",
+       "      <td>0.928571</td>\n",
+       "      <td>0.908140</td>\n",
+       "      <td>0.044557</td>\n",
+       "      <td>19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>0.002615</td>\n",
+       "      <td>0.000959</td>\n",
+       "      <td>0.004963</td>\n",
+       "      <td>0.001076</td>\n",
+       "      <td>36</td>\n",
+       "      <td>{'n_neighbors': 36}</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.860465</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.928571</td>\n",
+       "      <td>0.857143</td>\n",
+       "      <td>0.833333</td>\n",
+       "      <td>0.928571</td>\n",
+       "      <td>0.912901</td>\n",
+       "      <td>0.043938</td>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>0.002540</td>\n",
+       "      <td>0.000554</td>\n",
+       "      <td>0.005002</td>\n",
+       "      <td>0.001010</td>\n",
+       "      <td>41</td>\n",
+       "      <td>{'n_neighbors': 41}</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.883721</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.928571</td>\n",
+       "      <td>0.857143</td>\n",
+       "      <td>0.833333</td>\n",
+       "      <td>0.928571</td>\n",
+       "      <td>0.915227</td>\n",
+       "      <td>0.041657</td>\n",
+       "      <td>13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>0.002553</td>\n",
+       "      <td>0.000750</td>\n",
+       "      <td>0.004071</td>\n",
+       "      <td>0.000756</td>\n",
+       "      <td>46</td>\n",
+       "      <td>{'n_neighbors': 46}</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.883721</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.928571</td>\n",
+       "      <td>0.857143</td>\n",
+       "      <td>0.857143</td>\n",
+       "      <td>0.928571</td>\n",
+       "      <td>0.917608</td>\n",
+       "      <td>0.037368</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>0.002522</td>\n",
+       "      <td>0.000653</td>\n",
+       "      <td>0.004151</td>\n",
+       "      <td>0.000771</td>\n",
+       "      <td>51</td>\n",
+       "      <td>{'n_neighbors': 51}</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.883721</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.904762</td>\n",
+       "      <td>0.857143</td>\n",
+       "      <td>0.880952</td>\n",
+       "      <td>0.928571</td>\n",
+       "      <td>0.917608</td>\n",
+       "      <td>0.034199</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>0.002526</td>\n",
+       "      <td>0.001019</td>\n",
+       "      <td>0.004813</td>\n",
+       "      <td>0.000955</td>\n",
+       "      <td>56</td>\n",
+       "      <td>{'n_neighbors': 56}</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.883721</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.904762</td>\n",
+       "      <td>0.857143</td>\n",
+       "      <td>0.880952</td>\n",
+       "      <td>0.928571</td>\n",
+       "      <td>0.915282</td>\n",
+       "      <td>0.032425</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>0.002191</td>\n",
+       "      <td>0.000749</td>\n",
+       "      <td>0.004854</td>\n",
+       "      <td>0.001016</td>\n",
+       "      <td>61</td>\n",
+       "      <td>{'n_neighbors': 61}</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.883721</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.904762</td>\n",
+       "      <td>0.857143</td>\n",
+       "      <td>0.880952</td>\n",
+       "      <td>0.928571</td>\n",
+       "      <td>0.917608</td>\n",
+       "      <td>0.034199</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>0.002747</td>\n",
+       "      <td>0.001366</td>\n",
+       "      <td>0.005007</td>\n",
+       "      <td>0.001480</td>\n",
+       "      <td>66</td>\n",
+       "      <td>{'n_neighbors': 66}</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.883721</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.904762</td>\n",
+       "      <td>0.857143</td>\n",
+       "      <td>0.880952</td>\n",
+       "      <td>0.928571</td>\n",
+       "      <td>0.917608</td>\n",
+       "      <td>0.034199</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>0.002615</td>\n",
+       "      <td>0.000983</td>\n",
+       "      <td>0.004357</td>\n",
+       "      <td>0.000597</td>\n",
+       "      <td>71</td>\n",
+       "      <td>{'n_neighbors': 71}</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.883721</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.904762</td>\n",
+       "      <td>0.857143</td>\n",
+       "      <td>0.880952</td>\n",
+       "      <td>0.928571</td>\n",
+       "      <td>0.917608</td>\n",
+       "      <td>0.034199</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>0.002096</td>\n",
+       "      <td>0.000632</td>\n",
+       "      <td>0.005087</td>\n",
+       "      <td>0.001479</td>\n",
+       "      <td>76</td>\n",
+       "      <td>{'n_neighbors': 76}</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.883721</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.904762</td>\n",
+       "      <td>0.857143</td>\n",
+       "      <td>0.880952</td>\n",
+       "      <td>0.928571</td>\n",
+       "      <td>0.917608</td>\n",
+       "      <td>0.034199</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>0.002562</td>\n",
+       "      <td>0.000985</td>\n",
+       "      <td>0.004576</td>\n",
+       "      <td>0.000628</td>\n",
+       "      <td>81</td>\n",
+       "      <td>{'n_neighbors': 81}</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.928571</td>\n",
+       "      <td>0.857143</td>\n",
+       "      <td>0.880952</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.924695</td>\n",
+       "      <td>0.033470</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>0.002240</td>\n",
+       "      <td>0.000446</td>\n",
+       "      <td>0.004272</td>\n",
+       "      <td>0.000476</td>\n",
+       "      <td>86</td>\n",
+       "      <td>{'n_neighbors': 86}</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.928571</td>\n",
+       "      <td>0.857143</td>\n",
+       "      <td>0.880952</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.924695</td>\n",
+       "      <td>0.033470</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>0.002274</td>\n",
+       "      <td>0.000439</td>\n",
+       "      <td>0.004537</td>\n",
+       "      <td>0.000622</td>\n",
+       "      <td>91</td>\n",
+       "      <td>{'n_neighbors': 91}</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.928571</td>\n",
+       "      <td>0.833333</td>\n",
+       "      <td>0.880952</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.922315</td>\n",
+       "      <td>0.038639</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>0.001923</td>\n",
+       "      <td>0.000400</td>\n",
+       "      <td>0.004505</td>\n",
+       "      <td>0.000648</td>\n",
+       "      <td>96</td>\n",
+       "      <td>{'n_neighbors': 96}</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.928571</td>\n",
+       "      <td>0.833333</td>\n",
+       "      <td>0.880952</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.922315</td>\n",
+       "      <td>0.038639</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
+       "0        0.002227      0.000748         0.005310        0.001341   \n",
+       "1        0.002966      0.000881         0.004359        0.000745   \n",
+       "2        0.002659      0.001008         0.005164        0.001184   \n",
+       "3        0.002157      0.000382         0.004882        0.001504   \n",
+       "4        0.002278      0.000706         0.005006        0.001240   \n",
+       "5        0.002358      0.000396         0.004752        0.001247   \n",
+       "6        0.002405      0.000453         0.004578        0.001462   \n",
+       "7        0.002615      0.000959         0.004963        0.001076   \n",
+       "8        0.002540      0.000554         0.005002        0.001010   \n",
+       "9        0.002553      0.000750         0.004071        0.000756   \n",
+       "10       0.002522      0.000653         0.004151        0.000771   \n",
+       "11       0.002526      0.001019         0.004813        0.000955   \n",
+       "12       0.002191      0.000749         0.004854        0.001016   \n",
+       "13       0.002747      0.001366         0.005007        0.001480   \n",
+       "14       0.002615      0.000983         0.004357        0.000597   \n",
+       "15       0.002096      0.000632         0.005087        0.001479   \n",
+       "16       0.002562      0.000985         0.004576        0.000628   \n",
+       "17       0.002240      0.000446         0.004272        0.000476   \n",
+       "18       0.002274      0.000439         0.004537        0.000622   \n",
+       "19       0.001923      0.000400         0.004505        0.000648   \n",
+       "\n",
+       "    param_n_neighbors               params  split0_test_score  \\\n",
+       "0                   1   {'n_neighbors': 1}           1.000000   \n",
+       "1                   6   {'n_neighbors': 6}           0.930233   \n",
+       "2                  11  {'n_neighbors': 11}           0.953488   \n",
+       "3                  16  {'n_neighbors': 16}           0.953488   \n",
+       "4                  21  {'n_neighbors': 21}           0.953488   \n",
+       "5                  26  {'n_neighbors': 26}           0.953488   \n",
+       "6                  31  {'n_neighbors': 31}           0.953488   \n",
+       "7                  36  {'n_neighbors': 36}           0.953488   \n",
+       "8                  41  {'n_neighbors': 41}           0.953488   \n",
+       "9                  46  {'n_neighbors': 46}           0.953488   \n",
+       "10                 51  {'n_neighbors': 51}           0.953488   \n",
+       "11                 56  {'n_neighbors': 56}           0.953488   \n",
+       "12                 61  {'n_neighbors': 61}           0.953488   \n",
+       "13                 66  {'n_neighbors': 66}           0.953488   \n",
+       "14                 71  {'n_neighbors': 71}           0.953488   \n",
+       "15                 76  {'n_neighbors': 76}           0.953488   \n",
+       "16                 81  {'n_neighbors': 81}           0.953488   \n",
+       "17                 86  {'n_neighbors': 86}           0.953488   \n",
+       "18                 91  {'n_neighbors': 91}           0.953488   \n",
+       "19                 96  {'n_neighbors': 96}           0.953488   \n",
+       "\n",
+       "    split1_test_score  split2_test_score  split3_test_score  \\\n",
+       "0            0.860465           0.953488           0.883721   \n",
+       "1            0.906977           0.953488           0.953488   \n",
+       "2            0.883721           0.930233           0.930233   \n",
+       "3            0.860465           0.953488           0.953488   \n",
+       "4            0.883721           0.953488           0.930233   \n",
+       "5            0.860465           0.953488           0.953488   \n",
+       "6            0.860465           0.953488           0.953488   \n",
+       "7            0.860465           0.953488           0.953488   \n",
+       "8            0.883721           0.953488           0.953488   \n",
+       "9            0.883721           0.953488           0.953488   \n",
+       "10           0.883721           0.953488           0.953488   \n",
+       "11           0.883721           0.953488           0.930233   \n",
+       "12           0.883721           0.953488           0.953488   \n",
+       "13           0.883721           0.953488           0.953488   \n",
+       "14           0.883721           0.953488           0.953488   \n",
+       "15           0.883721           0.953488           0.953488   \n",
+       "16           0.906977           0.953488           0.953488   \n",
+       "17           0.906977           0.953488           0.953488   \n",
+       "18           0.906977           0.953488           0.953488   \n",
+       "19           0.906977           0.953488           0.953488   \n",
+       "\n",
+       "    split4_test_score  split5_test_score  split6_test_score  \\\n",
+       "0            0.883721           0.930233           0.857143   \n",
+       "1            0.976744           0.906977           0.904762   \n",
+       "2            0.930233           0.930233           0.904762   \n",
+       "3            0.953488           0.906977           0.880952   \n",
+       "4            0.953488           0.906977           0.880952   \n",
+       "5            0.953488           0.906977           0.880952   \n",
+       "6            0.953488           0.906977           0.880952   \n",
+       "7            0.953488           0.906977           0.928571   \n",
+       "8            0.953488           0.906977           0.928571   \n",
+       "9            0.953488           0.906977           0.928571   \n",
+       "10           0.953488           0.906977           0.904762   \n",
+       "11           0.953488           0.906977           0.904762   \n",
+       "12           0.953488           0.906977           0.904762   \n",
+       "13           0.953488           0.906977           0.904762   \n",
+       "14           0.953488           0.906977           0.904762   \n",
+       "15           0.953488           0.906977           0.904762   \n",
+       "16           0.953488           0.906977           0.928571   \n",
+       "17           0.953488           0.906977           0.928571   \n",
+       "18           0.953488           0.906977           0.928571   \n",
+       "19           0.953488           0.906977           0.928571   \n",
+       "\n",
+       "    split7_test_score  split8_test_score  split9_test_score  mean_test_score  \\\n",
+       "0            0.857143           0.880952           0.904762         0.901163   \n",
+       "1            0.857143           0.880952           0.928571         0.919934   \n",
+       "2            0.857143           0.857143           0.904762         0.908195   \n",
+       "3            0.857143           0.857143           0.928571         0.910520   \n",
+       "4            0.857143           0.857143           0.928571         0.910520   \n",
+       "5            0.857143           0.857143           0.928571         0.910520   \n",
+       "6            0.857143           0.833333           0.928571         0.908140   \n",
+       "7            0.857143           0.833333           0.928571         0.912901   \n",
+       "8            0.857143           0.833333           0.928571         0.915227   \n",
+       "9            0.857143           0.857143           0.928571         0.917608   \n",
+       "10           0.857143           0.880952           0.928571         0.917608   \n",
+       "11           0.857143           0.880952           0.928571         0.915282   \n",
+       "12           0.857143           0.880952           0.928571         0.917608   \n",
+       "13           0.857143           0.880952           0.928571         0.917608   \n",
+       "14           0.857143           0.880952           0.928571         0.917608   \n",
+       "15           0.857143           0.880952           0.928571         0.917608   \n",
+       "16           0.857143           0.880952           0.952381         0.924695   \n",
+       "17           0.857143           0.880952           0.952381         0.924695   \n",
+       "18           0.833333           0.880952           0.952381         0.922315   \n",
+       "19           0.833333           0.880952           0.952381         0.922315   \n",
+       "\n",
+       "    std_test_score  rank_test_score  \n",
+       "0         0.044625               20  \n",
+       "1         0.034188                5  \n",
+       "2         0.031352               18  \n",
+       "3         0.040990               15  \n",
+       "4         0.036819               15  \n",
+       "5         0.040990               15  \n",
+       "6         0.044557               19  \n",
+       "7         0.043938               14  \n",
+       "8         0.041657               13  \n",
+       "9         0.037368                6  \n",
+       "10        0.034199                6  \n",
+       "11        0.032425               12  \n",
+       "12        0.034199                6  \n",
+       "13        0.034199                6  \n",
+       "14        0.034199                6  \n",
+       "15        0.034199                6  \n",
+       "16        0.033470                1  \n",
+       "17        0.033470                1  \n",
+       "18        0.038639                3  \n",
+       "19        0.038639                3  "
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "accuracies_grid = pd.DataFrame(cancer_tune_grid.cv_results_)\n",
+    "accuracies_grid"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create the plot\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "\n",
+    "# Plot mean test scores with error bars\n",
+    "plt.plot(accuracies_grid['param_n_neighbors'], accuracies_grid['mean_test_score'], '-o', color='blue')\n",
+    "\n",
+    "# Add labels and legend\n",
+    "plt.xlabel('Number of Neighbors')\n",
+    "plt.ylabel('Accuracy estimate')\n",
+    "plt.title('K-Nearest Neighbors Performance')\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'n_neighbors': 81}"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cancer_tune_grid.best_params_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "large_param_grid = {\n",
+    "    \"n_neighbors\": range(1, 385, 10),\n",
+    "}\n",
+    "\n",
+    "large_cancer_tune_grid = GridSearchCV(\n",
+    "    estimator=knn,\n",
+    "    param_grid=large_param_grid,\n",
+    "    cv=10\n",
+    ")\n",
+    "\n",
+    "large_cancer_tune_grid.fit(\n",
+    "    cancer_train[[\"perimeter_mean\", \"concavity_mean\"]],\n",
+    "    cancer_train[\"diagnosis\"]\n",
+    ")\n",
+    "\n",
+    "large_accuracies_grid = pd.DataFrame(large_cancer_tune_grid.cv_results_)\n",
+    "\n",
+    "# Create the plot\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "\n",
+    "# Plot mean test scores with error bars\n",
+    "plt.plot(large_accuracies_grid['param_n_neighbors'], large_accuracies_grid['mean_test_score'], '-o', color='blue')\n",
+    "\n",
+    "# Add labels and legend\n",
+    "plt.xlabel('Number of Neighbors')\n",
+    "plt.ylabel('Accuracy estimate')\n",
+    "plt.title('K-Nearest Neighbors Performance')\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#split the data \n",
+    "# grid\n",
+    "grid \n",
+    "\n",
+    "K achieve higheest\n",
+    "using score method"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.model_selection import GridSearchCV, train_test_split\n",
+    "from sklearn.compose import make_column_transformer\n",
+    "from sklearn.pipeline import make_pipeline\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn import set_config\n",
+    "from sklearn.neighbors import KNeighborsRegressor\n",
+    "from sklearn.metrics import mean_squared_error"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn import set_config\n",
+    "set_config(transform_output=\"pandas\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>street</th>\n",
+       "      <th>city</th>\n",
+       "      <th>zip</th>\n",
+       "      <th>state</th>\n",
+       "      <th>beds</th>\n",
+       "      <th>baths</th>\n",
+       "      <th>sq__ft</th>\n",
+       "      <th>type</th>\n",
+       "      <th>sale_date</th>\n",
+       "      <th>price</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1 KENNELFORD CIR</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95823</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1144</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Mon May 19 00:00:00 EDT 2008</td>\n",
+       "      <td>200345</td>\n",
+       "      <td>38.464520</td>\n",
+       "      <td>-121.427606</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10 SEA FOAM CT</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95831</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2052</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Wed May 21 00:00:00 EDT 2008</td>\n",
+       "      <td>415000</td>\n",
+       "      <td>38.487885</td>\n",
+       "      <td>-121.545947</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>100 CHELSEA CT</td>\n",
+       "      <td>FOLSOM</td>\n",
+       "      <td>95630</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1905</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Mon May 19 00:00:00 EDT 2008</td>\n",
+       "      <td>500000</td>\n",
+       "      <td>38.694350</td>\n",
+       "      <td>-121.177259</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>100 REBECCA WAY</td>\n",
+       "      <td>FOLSOM</td>\n",
+       "      <td>95630</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2185</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Wed May 21 00:00:00 EDT 2008</td>\n",
+       "      <td>344250</td>\n",
+       "      <td>38.684790</td>\n",
+       "      <td>-121.149199</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>100 TOURMALINE CIR</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95834</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3076</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Mon May 19 00:00:00 EDT 2008</td>\n",
+       "      <td>240000</td>\n",
+       "      <td>38.634370</td>\n",
+       "      <td>-121.510779</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>809</th>\n",
+       "      <td>9880 IZILDA CT</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95829</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3863</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Fri May 16 00:00:00 EDT 2008</td>\n",
+       "      <td>598695</td>\n",
+       "      <td>38.453260</td>\n",
+       "      <td>-121.325730</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>810</th>\n",
+       "      <td>993 MANTON CT</td>\n",
+       "      <td>GALT</td>\n",
+       "      <td>95632</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2307</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Tue May 20 00:00:00 EDT 2008</td>\n",
+       "      <td>300000</td>\n",
+       "      <td>38.272942</td>\n",
+       "      <td>-121.289148</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>811</th>\n",
+       "      <td>9937 BURLINE ST</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95827</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1092</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Fri May 16 00:00:00 EDT 2008</td>\n",
+       "      <td>150000</td>\n",
+       "      <td>38.559641</td>\n",
+       "      <td>-121.323160</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>812</th>\n",
+       "      <td>9949 NESTLING CIR</td>\n",
+       "      <td>ELK GROVE</td>\n",
+       "      <td>95757</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1543</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Fri May 16 00:00:00 EDT 2008</td>\n",
+       "      <td>275000</td>\n",
+       "      <td>38.397455</td>\n",
+       "      <td>-121.468391</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>813</th>\n",
+       "      <td>9970 STATE HIGHWAY 193</td>\n",
+       "      <td>PLACERVILLE</td>\n",
+       "      <td>95667</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1929</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Tue May 20 00:00:00 EDT 2008</td>\n",
+       "      <td>485000</td>\n",
+       "      <td>38.787877</td>\n",
+       "      <td>-120.816676</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>814 rows × 12 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     street         city    zip state  beds  baths  sq__ft  \\\n",
+       "0          1 KENNELFORD CIR   SACRAMENTO  95823    CA     3      2    1144   \n",
+       "1            10 SEA FOAM CT   SACRAMENTO  95831    CA     3      3    2052   \n",
+       "2            100 CHELSEA CT       FOLSOM  95630    CA     3      2    1905   \n",
+       "3           100 REBECCA WAY       FOLSOM  95630    CA     3      2    2185   \n",
+       "4        100 TOURMALINE CIR   SACRAMENTO  95834    CA     5      3    3076   \n",
+       "..                      ...          ...    ...   ...   ...    ...     ...   \n",
+       "809          9880 IZILDA CT   SACRAMENTO  95829    CA     5      4    3863   \n",
+       "810           993 MANTON CT         GALT  95632    CA     4      3    2307   \n",
+       "811         9937 BURLINE ST   SACRAMENTO  95827    CA     3      2    1092   \n",
+       "812       9949 NESTLING CIR    ELK GROVE  95757    CA     3      2    1543   \n",
+       "813  9970 STATE HIGHWAY 193  PLACERVILLE  95667    CA     4      3    1929   \n",
+       "\n",
+       "            type                     sale_date   price   latitude   longitude  \n",
+       "0    Residential  Mon May 19 00:00:00 EDT 2008  200345  38.464520 -121.427606  \n",
+       "1    Residential  Wed May 21 00:00:00 EDT 2008  415000  38.487885 -121.545947  \n",
+       "2    Residential  Mon May 19 00:00:00 EDT 2008  500000  38.694350 -121.177259  \n",
+       "3    Residential  Wed May 21 00:00:00 EDT 2008  344250  38.684790 -121.149199  \n",
+       "4    Residential  Mon May 19 00:00:00 EDT 2008  240000  38.634370 -121.510779  \n",
+       "..           ...                           ...     ...        ...         ...  \n",
+       "809  Residential  Fri May 16 00:00:00 EDT 2008  598695  38.453260 -121.325730  \n",
+       "810  Residential  Tue May 20 00:00:00 EDT 2008  300000  38.272942 -121.289148  \n",
+       "811  Residential  Fri May 16 00:00:00 EDT 2008  150000  38.559641 -121.323160  \n",
+       "812  Residential  Fri May 16 00:00:00 EDT 2008  275000  38.397455 -121.468391  \n",
+       "813  Residential  Tue May 20 00:00:00 EDT 2008  485000  38.787877 -120.816676  \n",
+       "\n",
+       "[814 rows x 12 columns]"
+      ]
+     },
+     "execution_count": 89,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sacramento = pd.read_csv(\"dataset/sacramento.csv\")\n",
+    "sacramento"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot\n",
+    "plt.scatter(sacramento[\"sq__ft\"], sacramento['price'])\n",
+    "\n",
+    "# Add labels and legend\n",
+    "plt.xlabel(\"House size (square feet)\")\n",
+    "plt.ylabel('Price (USD)')\n",
+    "plt.title('Scatter Plot of House size vs Price')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# don't need to deal with outliner\n",
+    "# price can be affect the house size\n",
+    "# taking average of their value\n",
+    "# predict the price of house"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>street</th>\n",
+       "      <th>city</th>\n",
+       "      <th>zip</th>\n",
+       "      <th>state</th>\n",
+       "      <th>beds</th>\n",
+       "      <th>baths</th>\n",
+       "      <th>sq__ft</th>\n",
+       "      <th>type</th>\n",
+       "      <th>sale_date</th>\n",
+       "      <th>price</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>185</th>\n",
+       "      <td>2610 PHYLLIS AVE</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95820</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>804</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Mon May 19 00:00:00 EDT 2008</td>\n",
+       "      <td>120000</td>\n",
+       "      <td>38.531050</td>\n",
+       "      <td>-121.479574</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>210</th>\n",
+       "      <td>294 SPARROW DR</td>\n",
+       "      <td>GALT</td>\n",
+       "      <td>95632</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2214</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Fri May 16 00:00:00 EDT 2008</td>\n",
+       "      <td>278000</td>\n",
+       "      <td>38.258976</td>\n",
+       "      <td>-121.321266</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>487</th>\n",
+       "      <td>6000 BIRCHGLADE WAY</td>\n",
+       "      <td>CITRUS HEIGHTS</td>\n",
+       "      <td>95621</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1351</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Mon May 19 00:00:00 EDT 2008</td>\n",
+       "      <td>158000</td>\n",
+       "      <td>38.701660</td>\n",
+       "      <td>-121.323249</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>267</th>\n",
+       "      <td>361 MAHONIA CIR</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95835</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2175</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Mon May 19 00:00:00 EDT 2008</td>\n",
+       "      <td>261000</td>\n",
+       "      <td>38.676172</td>\n",
+       "      <td>-121.509761</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>674</th>\n",
+       "      <td>8164 CHENIN BLANC LN</td>\n",
+       "      <td>FAIR OAKS</td>\n",
+       "      <td>95628</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1315</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Tue May 20 00:00:00 EDT 2008</td>\n",
+       "      <td>230000</td>\n",
+       "      <td>38.665644</td>\n",
+       "      <td>-121.259969</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>177</th>\n",
+       "      <td>251 CHANGO CIR</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95835</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2218</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Mon May 19 00:00:00 EDT 2008</td>\n",
+       "      <td>311328</td>\n",
+       "      <td>38.682370</td>\n",
+       "      <td>-121.539147</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>217</th>\n",
+       "      <td>301 OLIVADI WAY</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95834</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1250</td>\n",
+       "      <td>Condo</td>\n",
+       "      <td>Mon May 19 00:00:00 EDT 2008</td>\n",
+       "      <td>232500</td>\n",
+       "      <td>38.644406</td>\n",
+       "      <td>-121.549049</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>589</th>\n",
+       "      <td>7204 THOMAS DR</td>\n",
+       "      <td>NORTH HIGHLANDS</td>\n",
+       "      <td>95660</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1152</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Mon May 19 00:00:00 EDT 2008</td>\n",
+       "      <td>158000</td>\n",
+       "      <td>38.697898</td>\n",
+       "      <td>-121.377687</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>696</th>\n",
+       "      <td>8306 CURLEW CT</td>\n",
+       "      <td>CITRUS HEIGHTS</td>\n",
+       "      <td>95621</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1280</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Mon May 19 00:00:00 EDT 2008</td>\n",
+       "      <td>167293</td>\n",
+       "      <td>38.715781</td>\n",
+       "      <td>-121.298519</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>602</th>\n",
+       "      <td>7349 FLETCHER FARM DR</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95828</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1587</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Mon May 19 00:00:00 EDT 2008</td>\n",
+       "      <td>127500</td>\n",
+       "      <td>38.490690</td>\n",
+       "      <td>-121.382619</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>229</th>\n",
+       "      <td>312 RIVER ISLE WAY</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95831</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1375</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Mon May 19 00:00:00 EDT 2008</td>\n",
+       "      <td>232000</td>\n",
+       "      <td>38.490260</td>\n",
+       "      <td>-121.550527</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>456</th>\n",
+       "      <td>5651 OVERLEAF WAY</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95835</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1910</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Tue May 20 00:00:00 EDT 2008</td>\n",
+       "      <td>300500</td>\n",
+       "      <td>38.677454</td>\n",
+       "      <td>-121.494791</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>526</th>\n",
+       "      <td>6344 LAGUNA MIRAGE LN</td>\n",
+       "      <td>ELK GROVE</td>\n",
+       "      <td>95758</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1112</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Mon May 19 00:00:00 EDT 2008</td>\n",
+       "      <td>213697</td>\n",
+       "      <td>38.423963</td>\n",
+       "      <td>-121.428875</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>531</th>\n",
+       "      <td>648 SANTA ANA AVE</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95838</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1211</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Tue May 20 00:00:00 EDT 2008</td>\n",
+       "      <td>135000</td>\n",
+       "      <td>38.658478</td>\n",
+       "      <td>-121.450409</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>252</th>\n",
+       "      <td>3409 VIRGO ST</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95827</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1320</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Fri May 16 00:00:00 EDT 2008</td>\n",
+       "      <td>115500</td>\n",
+       "      <td>38.563402</td>\n",
+       "      <td>-121.327747</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>227</th>\n",
+       "      <td>3108 DELWOOD WAY</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95821</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2053</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Fri May 16 00:00:00 EDT 2008</td>\n",
+       "      <td>450000</td>\n",
+       "      <td>38.621566</td>\n",
+       "      <td>-121.370882</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>567</th>\n",
+       "      <td>6943 WOLFGRAM WAY</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95828</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1176</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Mon May 19 00:00:00 EDT 2008</td>\n",
+       "      <td>247234</td>\n",
+       "      <td>38.489215</td>\n",
+       "      <td>-121.419546</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>162</th>\n",
+       "      <td>2361 LA LOMA DR</td>\n",
+       "      <td>RANCHO CORDOVA</td>\n",
+       "      <td>95670</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1115</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Fri May 16 00:00:00 EDT 2008</td>\n",
+       "      <td>116000</td>\n",
+       "      <td>38.593680</td>\n",
+       "      <td>-121.316054</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>240</th>\n",
+       "      <td>3240 S ST</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95816</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1269</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Tue May 20 00:00:00 EDT 2008</td>\n",
+       "      <td>245000</td>\n",
+       "      <td>38.562296</td>\n",
+       "      <td>-121.467489</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75</th>\n",
+       "      <td>1401 STERLING ST</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95822</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>810</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Fri May 16 00:00:00 EDT 2008</td>\n",
+       "      <td>108000</td>\n",
+       "      <td>38.520319</td>\n",
+       "      <td>-121.504727</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>388</th>\n",
+       "      <td>4901 MILLNER WAY</td>\n",
+       "      <td>ELK GROVE</td>\n",
+       "      <td>95757</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1843</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Wed May 21 00:00:00 EDT 2008</td>\n",
+       "      <td>254200</td>\n",
+       "      <td>38.386920</td>\n",
+       "      <td>-121.447349</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>657</th>\n",
+       "      <td>8 LA ROCAS CT</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95823</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1217</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Thu May 15 00:00:00 EDT 2008</td>\n",
+       "      <td>151087</td>\n",
+       "      <td>38.466160</td>\n",
+       "      <td>-121.448283</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>279</th>\n",
+       "      <td>3729 BAINBRIDGE DR</td>\n",
+       "      <td>NORTH HIGHLANDS</td>\n",
+       "      <td>95660</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>901</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Wed May 21 00:00:00 EDT 2008</td>\n",
+       "      <td>125000</td>\n",
+       "      <td>38.701499</td>\n",
+       "      <td>-121.376220</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>601</th>\n",
+       "      <td>7344 BUTTERBALL WAY</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95842</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1503</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Tue May 20 00:00:00 EDT 2008</td>\n",
+       "      <td>245000</td>\n",
+       "      <td>38.699489</td>\n",
+       "      <td>-121.361828</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>788</th>\n",
+       "      <td>9452 RED SPRUCE WAY</td>\n",
+       "      <td>ELK GROVE</td>\n",
+       "      <td>95624</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2555</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Fri May 16 00:00:00 EDT 2008</td>\n",
+       "      <td>300000</td>\n",
+       "      <td>38.404505</td>\n",
+       "      <td>-121.346938</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>303</th>\n",
+       "      <td>4008 GREY LIVERY WAY</td>\n",
+       "      <td>ANTELOPE</td>\n",
+       "      <td>95843</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1669</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Fri May 16 00:00:00 EDT 2008</td>\n",
+       "      <td>168750</td>\n",
+       "      <td>38.718460</td>\n",
+       "      <td>-121.370862</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>221</th>\n",
+       "      <td>3035 BRUNNET LN</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95833</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1522</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Tue May 20 00:00:00 EDT 2008</td>\n",
+       "      <td>225000</td>\n",
+       "      <td>38.624762</td>\n",
+       "      <td>-121.522775</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>100 TOURMALINE CIR</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95834</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3076</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Mon May 19 00:00:00 EDT 2008</td>\n",
+       "      <td>240000</td>\n",
+       "      <td>38.634370</td>\n",
+       "      <td>-121.510779</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>203</th>\n",
+       "      <td>2901 PINTAIL WAY</td>\n",
+       "      <td>ELK GROVE</td>\n",
+       "      <td>95757</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3070</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Tue May 20 00:00:00 EDT 2008</td>\n",
+       "      <td>495000</td>\n",
+       "      <td>38.398488</td>\n",
+       "      <td>-121.473424</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>273</th>\n",
+       "      <td>3692 PAYNE WAY</td>\n",
+       "      <td>NORTH HIGHLANDS</td>\n",
+       "      <td>95660</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>957</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Tue May 20 00:00:00 EDT 2008</td>\n",
+       "      <td>129000</td>\n",
+       "      <td>38.666540</td>\n",
+       "      <td>-121.378298</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    street             city    zip state  beds  baths  sq__ft  \\\n",
+       "185       2610 PHYLLIS AVE       SACRAMENTO  95820    CA     2      1     804   \n",
+       "210         294 SPARROW DR             GALT  95632    CA     4      3    2214   \n",
+       "487    6000 BIRCHGLADE WAY   CITRUS HEIGHTS  95621    CA     4      2    1351   \n",
+       "267        361 MAHONIA CIR       SACRAMENTO  95835    CA     4      3    2175   \n",
+       "674   8164 CHENIN BLANC LN        FAIR OAKS  95628    CA     2      2    1315   \n",
+       "177         251 CHANGO CIR       SACRAMENTO  95835    CA     4      2    2218   \n",
+       "217        301 OLIVADI WAY       SACRAMENTO  95834    CA     2      2    1250   \n",
+       "589         7204 THOMAS DR  NORTH HIGHLANDS  95660    CA     3      2    1152   \n",
+       "696         8306 CURLEW CT   CITRUS HEIGHTS  95621    CA     4      2    1280   \n",
+       "602  7349 FLETCHER FARM DR       SACRAMENTO  95828    CA     4      2    1587   \n",
+       "229     312 RIVER ISLE WAY       SACRAMENTO  95831    CA     3      2    1375   \n",
+       "456      5651 OVERLEAF WAY       SACRAMENTO  95835    CA     4      2    1910   \n",
+       "526  6344 LAGUNA MIRAGE LN        ELK GROVE  95758    CA     2      2    1112   \n",
+       "531      648 SANTA ANA AVE       SACRAMENTO  95838    CA     3      2    1211   \n",
+       "252          3409 VIRGO ST       SACRAMENTO  95827    CA     3      2    1320   \n",
+       "227       3108 DELWOOD WAY       SACRAMENTO  95821    CA     4      2    2053   \n",
+       "567      6943 WOLFGRAM WAY       SACRAMENTO  95828    CA     4      2    1176   \n",
+       "162        2361 LA LOMA DR   RANCHO CORDOVA  95670    CA     3      2    1115   \n",
+       "240              3240 S ST       SACRAMENTO  95816    CA     2      1    1269   \n",
+       "75        1401 STERLING ST       SACRAMENTO  95822    CA     2      1     810   \n",
+       "388       4901 MILLNER WAY        ELK GROVE  95757    CA     3      2    1843   \n",
+       "657          8 LA ROCAS CT       SACRAMENTO  95823    CA     3      2    1217   \n",
+       "279     3729 BAINBRIDGE DR  NORTH HIGHLANDS  95660    CA     3      2     901   \n",
+       "601    7344 BUTTERBALL WAY       SACRAMENTO  95842    CA     3      2    1503   \n",
+       "788    9452 RED SPRUCE WAY        ELK GROVE  95624    CA     6      3    2555   \n",
+       "303   4008 GREY LIVERY WAY         ANTELOPE  95843    CA     3      2    1669   \n",
+       "221        3035 BRUNNET LN       SACRAMENTO  95833    CA     3      2    1522   \n",
+       "4       100 TOURMALINE CIR       SACRAMENTO  95834    CA     5      3    3076   \n",
+       "203       2901 PINTAIL WAY        ELK GROVE  95757    CA     4      3    3070   \n",
+       "273         3692 PAYNE WAY  NORTH HIGHLANDS  95660    CA     3      1     957   \n",
+       "\n",
+       "            type                     sale_date   price   latitude   longitude  \n",
+       "185  Residential  Mon May 19 00:00:00 EDT 2008  120000  38.531050 -121.479574  \n",
+       "210  Residential  Fri May 16 00:00:00 EDT 2008  278000  38.258976 -121.321266  \n",
+       "487  Residential  Mon May 19 00:00:00 EDT 2008  158000  38.701660 -121.323249  \n",
+       "267  Residential  Mon May 19 00:00:00 EDT 2008  261000  38.676172 -121.509761  \n",
+       "674  Residential  Tue May 20 00:00:00 EDT 2008  230000  38.665644 -121.259969  \n",
+       "177  Residential  Mon May 19 00:00:00 EDT 2008  311328  38.682370 -121.539147  \n",
+       "217        Condo  Mon May 19 00:00:00 EDT 2008  232500  38.644406 -121.549049  \n",
+       "589  Residential  Mon May 19 00:00:00 EDT 2008  158000  38.697898 -121.377687  \n",
+       "696  Residential  Mon May 19 00:00:00 EDT 2008  167293  38.715781 -121.298519  \n",
+       "602  Residential  Mon May 19 00:00:00 EDT 2008  127500  38.490690 -121.382619  \n",
+       "229  Residential  Mon May 19 00:00:00 EDT 2008  232000  38.490260 -121.550527  \n",
+       "456  Residential  Tue May 20 00:00:00 EDT 2008  300500  38.677454 -121.494791  \n",
+       "526  Residential  Mon May 19 00:00:00 EDT 2008  213697  38.423963 -121.428875  \n",
+       "531  Residential  Tue May 20 00:00:00 EDT 2008  135000  38.658478 -121.450409  \n",
+       "252  Residential  Fri May 16 00:00:00 EDT 2008  115500  38.563402 -121.327747  \n",
+       "227  Residential  Fri May 16 00:00:00 EDT 2008  450000  38.621566 -121.370882  \n",
+       "567  Residential  Mon May 19 00:00:00 EDT 2008  247234  38.489215 -121.419546  \n",
+       "162  Residential  Fri May 16 00:00:00 EDT 2008  116000  38.593680 -121.316054  \n",
+       "240  Residential  Tue May 20 00:00:00 EDT 2008  245000  38.562296 -121.467489  \n",
+       "75   Residential  Fri May 16 00:00:00 EDT 2008  108000  38.520319 -121.504727  \n",
+       "388  Residential  Wed May 21 00:00:00 EDT 2008  254200  38.386920 -121.447349  \n",
+       "657  Residential  Thu May 15 00:00:00 EDT 2008  151087  38.466160 -121.448283  \n",
+       "279  Residential  Wed May 21 00:00:00 EDT 2008  125000  38.701499 -121.376220  \n",
+       "601  Residential  Tue May 20 00:00:00 EDT 2008  245000  38.699489 -121.361828  \n",
+       "788  Residential  Fri May 16 00:00:00 EDT 2008  300000  38.404505 -121.346938  \n",
+       "303  Residential  Fri May 16 00:00:00 EDT 2008  168750  38.718460 -121.370862  \n",
+       "221  Residential  Tue May 20 00:00:00 EDT 2008  225000  38.624762 -121.522775  \n",
+       "4    Residential  Mon May 19 00:00:00 EDT 2008  240000  38.634370 -121.510779  \n",
+       "203  Residential  Tue May 20 00:00:00 EDT 2008  495000  38.398488 -121.473424  \n",
+       "273  Residential  Tue May 20 00:00:00 EDT 2008  129000  38.666540 -121.378298  "
+      ]
+     },
+     "execution_count": 94,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# select random sample (small size)\n",
+    "\n",
+    "np.random.seed(123)\n",
+    "\n",
+    "small_sacramento = sacramento.sample(n=30)\n",
+    "small_sacramento"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot\n",
+    "plt.scatter(small_sacramento[\"sq__ft\"], small_sacramento['price'])\n",
+    "\n",
+    "# Add a vertical line at 2,000 square feet\n",
+    "plt.axvline(x=2000, color='red', linestyle='--', label='2000 sqft')\n",
+    "\n",
+    "# Add labels and legend\n",
+    "plt.xlabel(\"House size (square feet)\")\n",
+    "plt.ylabel('Price (USD)')\n",
+    "plt.title('Scatter Plot of House size vs Price')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "185    1196\n",
+       "210     214\n",
+       "487     649\n",
+       "267     175\n",
+       "674     685\n",
+       "177     218\n",
+       "217     750\n",
+       "589     848\n",
+       "696     720\n",
+       "602     413\n",
+       "229     625\n",
+       "456      90\n",
+       "526     888\n",
+       "531     789\n",
+       "252     680\n",
+       "227      53\n",
+       "567     824\n",
+       "162     885\n",
+       "240     731\n",
+       "75     1190\n",
+       "388     157\n",
+       "657     783\n",
+       "279    1099\n",
+       "601     497\n",
+       "788     555\n",
+       "303     331\n",
+       "221     478\n",
+       "4      1076\n",
+       "203    1070\n",
+       "273    1043\n",
+       "Name: dist, dtype: int64"
+      ]
+     },
+     "execution_count": 97,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# abs difference between our house and the data point (observation)\n",
+    "\n",
+    "small_sacramento[\"dist\"] = (2000 - small_sacramento[\"sq__ft\"]).abs()\n",
+    "small_sacramento[\"dist\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>street</th>\n",
+       "      <th>city</th>\n",
+       "      <th>zip</th>\n",
+       "      <th>state</th>\n",
+       "      <th>beds</th>\n",
+       "      <th>baths</th>\n",
+       "      <th>sq__ft</th>\n",
+       "      <th>type</th>\n",
+       "      <th>sale_date</th>\n",
+       "      <th>price</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>dist</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>227</th>\n",
+       "      <td>3108 DELWOOD WAY</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95821</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2053</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Fri May 16 00:00:00 EDT 2008</td>\n",
+       "      <td>450000</td>\n",
+       "      <td>38.621566</td>\n",
+       "      <td>-121.370882</td>\n",
+       "      <td>53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>456</th>\n",
+       "      <td>5651 OVERLEAF WAY</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95835</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1910</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Tue May 20 00:00:00 EDT 2008</td>\n",
+       "      <td>300500</td>\n",
+       "      <td>38.677454</td>\n",
+       "      <td>-121.494791</td>\n",
+       "      <td>90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>388</th>\n",
+       "      <td>4901 MILLNER WAY</td>\n",
+       "      <td>ELK GROVE</td>\n",
+       "      <td>95757</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1843</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Wed May 21 00:00:00 EDT 2008</td>\n",
+       "      <td>254200</td>\n",
+       "      <td>38.386920</td>\n",
+       "      <td>-121.447349</td>\n",
+       "      <td>157</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>267</th>\n",
+       "      <td>361 MAHONIA CIR</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95835</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2175</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Mon May 19 00:00:00 EDT 2008</td>\n",
+       "      <td>261000</td>\n",
+       "      <td>38.676172</td>\n",
+       "      <td>-121.509761</td>\n",
+       "      <td>175</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>210</th>\n",
+       "      <td>294 SPARROW DR</td>\n",
+       "      <td>GALT</td>\n",
+       "      <td>95632</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2214</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Fri May 16 00:00:00 EDT 2008</td>\n",
+       "      <td>278000</td>\n",
+       "      <td>38.258976</td>\n",
+       "      <td>-121.321266</td>\n",
+       "      <td>214</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                street        city    zip state  beds  baths  sq__ft  \\\n",
+       "227   3108 DELWOOD WAY  SACRAMENTO  95821    CA     4      2    2053   \n",
+       "456  5651 OVERLEAF WAY  SACRAMENTO  95835    CA     4      2    1910   \n",
+       "388   4901 MILLNER WAY   ELK GROVE  95757    CA     3      2    1843   \n",
+       "267    361 MAHONIA CIR  SACRAMENTO  95835    CA     4      3    2175   \n",
+       "210     294 SPARROW DR        GALT  95632    CA     4      3    2214   \n",
+       "\n",
+       "            type                     sale_date   price   latitude   longitude  \\\n",
+       "227  Residential  Fri May 16 00:00:00 EDT 2008  450000  38.621566 -121.370882   \n",
+       "456  Residential  Tue May 20 00:00:00 EDT 2008  300500  38.677454 -121.494791   \n",
+       "388  Residential  Wed May 21 00:00:00 EDT 2008  254200  38.386920 -121.447349   \n",
+       "267  Residential  Mon May 19 00:00:00 EDT 2008  261000  38.676172 -121.509761   \n",
+       "210  Residential  Fri May 16 00:00:00 EDT 2008  278000  38.258976 -121.321266   \n",
+       "\n",
+       "     dist  \n",
+       "227    53  \n",
+       "456    90  \n",
+       "388   157  \n",
+       "267   175  \n",
+       "210   214  "
+      ]
+     },
+     "execution_count": 99,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nearest_neighbors = small_sacramento.nsmallest(5, \"dist\")\n",
+    "nearest_neighbors"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x2c9116e3970>"
+      ]
+     },
+     "execution_count": 100,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Scatter plot\n",
+    "plt.scatter(small_sacramento[\"sq__ft\"], small_sacramento['price'], label='All houses')\n",
+    "\n",
+    "# Plot nearest neighbors in orange\n",
+    "plt.scatter(nearest_neighbors[\"sq__ft\"], nearest_neighbors['price'], color='orange', label='Nearest neighbors', edgecolor='black')\n",
+    "\n",
+    "# Add a vertical line at 2,000 square feet\n",
+    "plt.axvline(x=2000, color='red', linestyle='--', label='2000 sqft')\n",
+    "\n",
+    "# Add labels, title, and legend\n",
+    "plt.xlabel(\"House size (square feet)\")\n",
+    "plt.ylabel('Price (USD)')\n",
+    "plt.title('Scatter Plot of House Size vs Price')\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "308740.0"
+      ]
+     },
+     "execution_count": 101,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "prediction = nearest_neighbors[\"price\"].mean()\n",
+    "prediction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sacramento_train, sacramento_test = train_test_split(\n",
+    "    sacramento, train_size=0.75, random_state=42\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train = sacramento_train[[\"sq__ft\"]]\n",
+    "y_train = sacramento_train[\"price\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "knn_regressor = KNeighborsRegressor()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "param_grid = {\n",
+    "    \"n_neighbors\": range(1, 201, 3),        # But wait...? What is this?\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sacr_gridsearch = GridSearchCV(\n",
+    "    estimator=knn_regressor,\n",
+    "    param_grid=param_grid,\n",
+    "    cv=5,\n",
+    "    scoring=\"neg_root_mean_squared_error\" # we can also use \"R2\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-6 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-6 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-6 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-6 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-6 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-6 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-6 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-6 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=KNeighborsRegressor(),\n",
+       "             param_grid={&#x27;n_neighbors&#x27;: range(1, 201, 3)},\n",
+       "             scoring=&#x27;neg_root_mean_squared_error&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=5, estimator=KNeighborsRegressor(),\n",
+       "             param_grid={&#x27;n_neighbors&#x27;: range(1, 201, 3)},\n",
+       "             scoring=&#x27;neg_root_mean_squared_error&#x27;)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">best_estimator_: KNeighborsRegressor</label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsRegressor(n_neighbors=25)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;KNeighborsRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.neighbors.KNeighborsRegressor.html\">?<span>Documentation for KNeighborsRegressor</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsRegressor(n_neighbors=25)</pre></div> </div></div></div></div></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "GridSearchCV(cv=5, estimator=KNeighborsRegressor(),\n",
+       "             param_grid={'n_neighbors': range(1, 201, 3)},\n",
+       "             scoring='neg_root_mean_squared_error')"
+      ]
+     },
+     "execution_count": 109,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sacr_gridsearch.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mean_fit_time</th>\n",
+       "      <th>std_fit_time</th>\n",
+       "      <th>mean_score_time</th>\n",
+       "      <th>std_score_time</th>\n",
+       "      <th>param_n_neighbors</th>\n",
+       "      <th>params</th>\n",
+       "      <th>split0_test_score</th>\n",
+       "      <th>split1_test_score</th>\n",
+       "      <th>split2_test_score</th>\n",
+       "      <th>split3_test_score</th>\n",
+       "      <th>split4_test_score</th>\n",
+       "      <th>mean_test_score</th>\n",
+       "      <th>std_test_score</th>\n",
+       "      <th>rank_test_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.015950</td>\n",
+       "      <td>0.028451</td>\n",
+       "      <td>0.005170</td>\n",
+       "      <td>0.005317</td>\n",
+       "      <td>1</td>\n",
+       "      <td>{'n_neighbors': 1}</td>\n",
+       "      <td>-112075.959498</td>\n",
+       "      <td>-113997.393596</td>\n",
+       "      <td>-117026.607659</td>\n",
+       "      <td>-120574.497484</td>\n",
+       "      <td>-98703.354492</td>\n",
+       "      <td>-112475.562546</td>\n",
+       "      <td>7462.628569</td>\n",
+       "      <td>67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.001863</td>\n",
+       "      <td>0.000750</td>\n",
+       "      <td>0.002298</td>\n",
+       "      <td>0.001253</td>\n",
+       "      <td>4</td>\n",
+       "      <td>{'n_neighbors': 4}</td>\n",
+       "      <td>-87544.667287</td>\n",
+       "      <td>-83883.876555</td>\n",
+       "      <td>-83913.469660</td>\n",
+       "      <td>-104704.353254</td>\n",
+       "      <td>-85027.749120</td>\n",
+       "      <td>-89014.823175</td>\n",
+       "      <td>7957.008142</td>\n",
+       "      <td>56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.001903</td>\n",
+       "      <td>0.000803</td>\n",
+       "      <td>0.003573</td>\n",
+       "      <td>0.004661</td>\n",
+       "      <td>7</td>\n",
+       "      <td>{'n_neighbors': 7}</td>\n",
+       "      <td>-86504.059436</td>\n",
+       "      <td>-82825.683348</td>\n",
+       "      <td>-76131.355307</td>\n",
+       "      <td>-102065.903265</td>\n",
+       "      <td>-79440.331044</td>\n",
+       "      <td>-85393.466480</td>\n",
+       "      <td>9022.456430</td>\n",
+       "      <td>35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.001845</td>\n",
+       "      <td>0.000427</td>\n",
+       "      <td>0.002103</td>\n",
+       "      <td>0.000662</td>\n",
+       "      <td>10</td>\n",
+       "      <td>{'n_neighbors': 10}</td>\n",
+       "      <td>-84090.805474</td>\n",
+       "      <td>-82910.735403</td>\n",
+       "      <td>-78152.835102</td>\n",
+       "      <td>-102051.387567</td>\n",
+       "      <td>-74177.616156</td>\n",
+       "      <td>-84276.675940</td>\n",
+       "      <td>9563.818797</td>\n",
+       "      <td>27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.001809</td>\n",
+       "      <td>0.000517</td>\n",
+       "      <td>0.001903</td>\n",
+       "      <td>0.000664</td>\n",
+       "      <td>13</td>\n",
+       "      <td>{'n_neighbors': 13}</td>\n",
+       "      <td>-84904.213103</td>\n",
+       "      <td>-81367.275879</td>\n",
+       "      <td>-79773.776612</td>\n",
+       "      <td>-100931.021005</td>\n",
+       "      <td>-75241.782711</td>\n",
+       "      <td>-84443.613862</td>\n",
+       "      <td>8808.991745</td>\n",
+       "      <td>29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>62</th>\n",
+       "      <td>0.001902</td>\n",
+       "      <td>0.000802</td>\n",
+       "      <td>0.004737</td>\n",
+       "      <td>0.000988</td>\n",
+       "      <td>187</td>\n",
+       "      <td>{'n_neighbors': 187}</td>\n",
+       "      <td>-92650.337006</td>\n",
+       "      <td>-88889.235388</td>\n",
+       "      <td>-90004.888176</td>\n",
+       "      <td>-101845.246945</td>\n",
+       "      <td>-77821.033008</td>\n",
+       "      <td>-90242.148105</td>\n",
+       "      <td>7701.974316</td>\n",
+       "      <td>62</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>63</th>\n",
+       "      <td>0.001530</td>\n",
+       "      <td>0.000480</td>\n",
+       "      <td>0.004011</td>\n",
+       "      <td>0.000558</td>\n",
+       "      <td>190</td>\n",
+       "      <td>{'n_neighbors': 190}</td>\n",
+       "      <td>-92895.085263</td>\n",
+       "      <td>-89245.862528</td>\n",
+       "      <td>-90289.489219</td>\n",
+       "      <td>-102084.469772</td>\n",
+       "      <td>-78056.396280</td>\n",
+       "      <td>-90514.260612</td>\n",
+       "      <td>7699.296145</td>\n",
+       "      <td>63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>64</th>\n",
+       "      <td>0.001581</td>\n",
+       "      <td>0.000476</td>\n",
+       "      <td>0.004367</td>\n",
+       "      <td>0.001271</td>\n",
+       "      <td>193</td>\n",
+       "      <td>{'n_neighbors': 193}</td>\n",
+       "      <td>-93007.820652</td>\n",
+       "      <td>-89594.971914</td>\n",
+       "      <td>-90591.944719</td>\n",
+       "      <td>-102189.040603</td>\n",
+       "      <td>-78243.200165</td>\n",
+       "      <td>-90725.395610</td>\n",
+       "      <td>7664.535088</td>\n",
+       "      <td>64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>65</th>\n",
+       "      <td>0.002137</td>\n",
+       "      <td>0.000483</td>\n",
+       "      <td>0.005088</td>\n",
+       "      <td>0.002020</td>\n",
+       "      <td>196</td>\n",
+       "      <td>{'n_neighbors': 196}</td>\n",
+       "      <td>-93187.805582</td>\n",
+       "      <td>-89786.909552</td>\n",
+       "      <td>-90765.041431</td>\n",
+       "      <td>-102241.600388</td>\n",
+       "      <td>-78394.469000</td>\n",
+       "      <td>-90875.165190</td>\n",
+       "      <td>7635.553151</td>\n",
+       "      <td>65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>66</th>\n",
+       "      <td>0.001814</td>\n",
+       "      <td>0.000508</td>\n",
+       "      <td>0.004020</td>\n",
+       "      <td>0.000840</td>\n",
+       "      <td>199</td>\n",
+       "      <td>{'n_neighbors': 199}</td>\n",
+       "      <td>-93500.570900</td>\n",
+       "      <td>-90248.379001</td>\n",
+       "      <td>-90744.950994</td>\n",
+       "      <td>-102504.487078</td>\n",
+       "      <td>-78564.416074</td>\n",
+       "      <td>-91112.560810</td>\n",
+       "      <td>7665.721881</td>\n",
+       "      <td>66</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>67 rows × 14 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
+       "0        0.015950      0.028451         0.005170        0.005317   \n",
+       "1        0.001863      0.000750         0.002298        0.001253   \n",
+       "2        0.001903      0.000803         0.003573        0.004661   \n",
+       "3        0.001845      0.000427         0.002103        0.000662   \n",
+       "4        0.001809      0.000517         0.001903        0.000664   \n",
+       "..            ...           ...              ...             ...   \n",
+       "62       0.001902      0.000802         0.004737        0.000988   \n",
+       "63       0.001530      0.000480         0.004011        0.000558   \n",
+       "64       0.001581      0.000476         0.004367        0.001271   \n",
+       "65       0.002137      0.000483         0.005088        0.002020   \n",
+       "66       0.001814      0.000508         0.004020        0.000840   \n",
+       "\n",
+       "    param_n_neighbors                params  split0_test_score  \\\n",
+       "0                   1    {'n_neighbors': 1}     -112075.959498   \n",
+       "1                   4    {'n_neighbors': 4}      -87544.667287   \n",
+       "2                   7    {'n_neighbors': 7}      -86504.059436   \n",
+       "3                  10   {'n_neighbors': 10}      -84090.805474   \n",
+       "4                  13   {'n_neighbors': 13}      -84904.213103   \n",
+       "..                ...                   ...                ...   \n",
+       "62                187  {'n_neighbors': 187}      -92650.337006   \n",
+       "63                190  {'n_neighbors': 190}      -92895.085263   \n",
+       "64                193  {'n_neighbors': 193}      -93007.820652   \n",
+       "65                196  {'n_neighbors': 196}      -93187.805582   \n",
+       "66                199  {'n_neighbors': 199}      -93500.570900   \n",
+       "\n",
+       "    split1_test_score  split2_test_score  split3_test_score  \\\n",
+       "0      -113997.393596     -117026.607659     -120574.497484   \n",
+       "1       -83883.876555      -83913.469660     -104704.353254   \n",
+       "2       -82825.683348      -76131.355307     -102065.903265   \n",
+       "3       -82910.735403      -78152.835102     -102051.387567   \n",
+       "4       -81367.275879      -79773.776612     -100931.021005   \n",
+       "..                ...                ...                ...   \n",
+       "62      -88889.235388      -90004.888176     -101845.246945   \n",
+       "63      -89245.862528      -90289.489219     -102084.469772   \n",
+       "64      -89594.971914      -90591.944719     -102189.040603   \n",
+       "65      -89786.909552      -90765.041431     -102241.600388   \n",
+       "66      -90248.379001      -90744.950994     -102504.487078   \n",
+       "\n",
+       "    split4_test_score  mean_test_score  std_test_score  rank_test_score  \n",
+       "0       -98703.354492   -112475.562546     7462.628569               67  \n",
+       "1       -85027.749120    -89014.823175     7957.008142               56  \n",
+       "2       -79440.331044    -85393.466480     9022.456430               35  \n",
+       "3       -74177.616156    -84276.675940     9563.818797               27  \n",
+       "4       -75241.782711    -84443.613862     8808.991745               29  \n",
+       "..                ...              ...             ...              ...  \n",
+       "62      -77821.033008    -90242.148105     7701.974316               62  \n",
+       "63      -78056.396280    -90514.260612     7699.296145               63  \n",
+       "64      -78243.200165    -90725.395610     7664.535088               64  \n",
+       "65      -78394.469000    -90875.165190     7635.553151               65  \n",
+       "66      -78564.416074    -91112.560810     7665.721881               66  \n",
+       "\n",
+       "[67 rows x 14 columns]"
+      ]
+     },
+     "execution_count": 112,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results = pd.DataFrame(sacr_gridsearch.cv_results_)\n",
+    "results     # After fitting the model, we extract the cross-validation results using `cv_results_`. This output includes various metrics and parameters tested during the cross-validation process.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mean_fit_time</th>\n",
+       "      <th>std_fit_time</th>\n",
+       "      <th>mean_score_time</th>\n",
+       "      <th>std_score_time</th>\n",
+       "      <th>param_n_neighbors</th>\n",
+       "      <th>params</th>\n",
+       "      <th>split0_test_score</th>\n",
+       "      <th>split1_test_score</th>\n",
+       "      <th>split2_test_score</th>\n",
+       "      <th>split3_test_score</th>\n",
+       "      <th>split4_test_score</th>\n",
+       "      <th>mean_test_score</th>\n",
+       "      <th>std_test_score</th>\n",
+       "      <th>rank_test_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.015950</td>\n",
+       "      <td>0.028451</td>\n",
+       "      <td>0.005170</td>\n",
+       "      <td>0.005317</td>\n",
+       "      <td>1</td>\n",
+       "      <td>{'n_neighbors': 1}</td>\n",
+       "      <td>-112075.959498</td>\n",
+       "      <td>-113997.393596</td>\n",
+       "      <td>-117026.607659</td>\n",
+       "      <td>-120574.497484</td>\n",
+       "      <td>-98703.354492</td>\n",
+       "      <td>112475.562546</td>\n",
+       "      <td>7462.628569</td>\n",
+       "      <td>67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.001863</td>\n",
+       "      <td>0.000750</td>\n",
+       "      <td>0.002298</td>\n",
+       "      <td>0.001253</td>\n",
+       "      <td>4</td>\n",
+       "      <td>{'n_neighbors': 4}</td>\n",
+       "      <td>-87544.667287</td>\n",
+       "      <td>-83883.876555</td>\n",
+       "      <td>-83913.469660</td>\n",
+       "      <td>-104704.353254</td>\n",
+       "      <td>-85027.749120</td>\n",
+       "      <td>89014.823175</td>\n",
+       "      <td>7957.008142</td>\n",
+       "      <td>56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.001903</td>\n",
+       "      <td>0.000803</td>\n",
+       "      <td>0.003573</td>\n",
+       "      <td>0.004661</td>\n",
+       "      <td>7</td>\n",
+       "      <td>{'n_neighbors': 7}</td>\n",
+       "      <td>-86504.059436</td>\n",
+       "      <td>-82825.683348</td>\n",
+       "      <td>-76131.355307</td>\n",
+       "      <td>-102065.903265</td>\n",
+       "      <td>-79440.331044</td>\n",
+       "      <td>85393.466480</td>\n",
+       "      <td>9022.456430</td>\n",
+       "      <td>35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.001845</td>\n",
+       "      <td>0.000427</td>\n",
+       "      <td>0.002103</td>\n",
+       "      <td>0.000662</td>\n",
+       "      <td>10</td>\n",
+       "      <td>{'n_neighbors': 10}</td>\n",
+       "      <td>-84090.805474</td>\n",
+       "      <td>-82910.735403</td>\n",
+       "      <td>-78152.835102</td>\n",
+       "      <td>-102051.387567</td>\n",
+       "      <td>-74177.616156</td>\n",
+       "      <td>84276.675940</td>\n",
+       "      <td>9563.818797</td>\n",
+       "      <td>27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.001809</td>\n",
+       "      <td>0.000517</td>\n",
+       "      <td>0.001903</td>\n",
+       "      <td>0.000664</td>\n",
+       "      <td>13</td>\n",
+       "      <td>{'n_neighbors': 13}</td>\n",
+       "      <td>-84904.213103</td>\n",
+       "      <td>-81367.275879</td>\n",
+       "      <td>-79773.776612</td>\n",
+       "      <td>-100931.021005</td>\n",
+       "      <td>-75241.782711</td>\n",
+       "      <td>84443.613862</td>\n",
+       "      <td>8808.991745</td>\n",
+       "      <td>29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>62</th>\n",
+       "      <td>0.001902</td>\n",
+       "      <td>0.000802</td>\n",
+       "      <td>0.004737</td>\n",
+       "      <td>0.000988</td>\n",
+       "      <td>187</td>\n",
+       "      <td>{'n_neighbors': 187}</td>\n",
+       "      <td>-92650.337006</td>\n",
+       "      <td>-88889.235388</td>\n",
+       "      <td>-90004.888176</td>\n",
+       "      <td>-101845.246945</td>\n",
+       "      <td>-77821.033008</td>\n",
+       "      <td>90242.148105</td>\n",
+       "      <td>7701.974316</td>\n",
+       "      <td>62</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>63</th>\n",
+       "      <td>0.001530</td>\n",
+       "      <td>0.000480</td>\n",
+       "      <td>0.004011</td>\n",
+       "      <td>0.000558</td>\n",
+       "      <td>190</td>\n",
+       "      <td>{'n_neighbors': 190}</td>\n",
+       "      <td>-92895.085263</td>\n",
+       "      <td>-89245.862528</td>\n",
+       "      <td>-90289.489219</td>\n",
+       "      <td>-102084.469772</td>\n",
+       "      <td>-78056.396280</td>\n",
+       "      <td>90514.260612</td>\n",
+       "      <td>7699.296145</td>\n",
+       "      <td>63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>64</th>\n",
+       "      <td>0.001581</td>\n",
+       "      <td>0.000476</td>\n",
+       "      <td>0.004367</td>\n",
+       "      <td>0.001271</td>\n",
+       "      <td>193</td>\n",
+       "      <td>{'n_neighbors': 193}</td>\n",
+       "      <td>-93007.820652</td>\n",
+       "      <td>-89594.971914</td>\n",
+       "      <td>-90591.944719</td>\n",
+       "      <td>-102189.040603</td>\n",
+       "      <td>-78243.200165</td>\n",
+       "      <td>90725.395610</td>\n",
+       "      <td>7664.535088</td>\n",
+       "      <td>64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>65</th>\n",
+       "      <td>0.002137</td>\n",
+       "      <td>0.000483</td>\n",
+       "      <td>0.005088</td>\n",
+       "      <td>0.002020</td>\n",
+       "      <td>196</td>\n",
+       "      <td>{'n_neighbors': 196}</td>\n",
+       "      <td>-93187.805582</td>\n",
+       "      <td>-89786.909552</td>\n",
+       "      <td>-90765.041431</td>\n",
+       "      <td>-102241.600388</td>\n",
+       "      <td>-78394.469000</td>\n",
+       "      <td>90875.165190</td>\n",
+       "      <td>7635.553151</td>\n",
+       "      <td>65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>66</th>\n",
+       "      <td>0.001814</td>\n",
+       "      <td>0.000508</td>\n",
+       "      <td>0.004020</td>\n",
+       "      <td>0.000840</td>\n",
+       "      <td>199</td>\n",
+       "      <td>{'n_neighbors': 199}</td>\n",
+       "      <td>-93500.570900</td>\n",
+       "      <td>-90248.379001</td>\n",
+       "      <td>-90744.950994</td>\n",
+       "      <td>-102504.487078</td>\n",
+       "      <td>-78564.416074</td>\n",
+       "      <td>91112.560810</td>\n",
+       "      <td>7665.721881</td>\n",
+       "      <td>66</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>67 rows × 14 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
+       "0        0.015950      0.028451         0.005170        0.005317   \n",
+       "1        0.001863      0.000750         0.002298        0.001253   \n",
+       "2        0.001903      0.000803         0.003573        0.004661   \n",
+       "3        0.001845      0.000427         0.002103        0.000662   \n",
+       "4        0.001809      0.000517         0.001903        0.000664   \n",
+       "..            ...           ...              ...             ...   \n",
+       "62       0.001902      0.000802         0.004737        0.000988   \n",
+       "63       0.001530      0.000480         0.004011        0.000558   \n",
+       "64       0.001581      0.000476         0.004367        0.001271   \n",
+       "65       0.002137      0.000483         0.005088        0.002020   \n",
+       "66       0.001814      0.000508         0.004020        0.000840   \n",
+       "\n",
+       "    param_n_neighbors                params  split0_test_score  \\\n",
+       "0                   1    {'n_neighbors': 1}     -112075.959498   \n",
+       "1                   4    {'n_neighbors': 4}      -87544.667287   \n",
+       "2                   7    {'n_neighbors': 7}      -86504.059436   \n",
+       "3                  10   {'n_neighbors': 10}      -84090.805474   \n",
+       "4                  13   {'n_neighbors': 13}      -84904.213103   \n",
+       "..                ...                   ...                ...   \n",
+       "62                187  {'n_neighbors': 187}      -92650.337006   \n",
+       "63                190  {'n_neighbors': 190}      -92895.085263   \n",
+       "64                193  {'n_neighbors': 193}      -93007.820652   \n",
+       "65                196  {'n_neighbors': 196}      -93187.805582   \n",
+       "66                199  {'n_neighbors': 199}      -93500.570900   \n",
+       "\n",
+       "    split1_test_score  split2_test_score  split3_test_score  \\\n",
+       "0      -113997.393596     -117026.607659     -120574.497484   \n",
+       "1       -83883.876555      -83913.469660     -104704.353254   \n",
+       "2       -82825.683348      -76131.355307     -102065.903265   \n",
+       "3       -82910.735403      -78152.835102     -102051.387567   \n",
+       "4       -81367.275879      -79773.776612     -100931.021005   \n",
+       "..                ...                ...                ...   \n",
+       "62      -88889.235388      -90004.888176     -101845.246945   \n",
+       "63      -89245.862528      -90289.489219     -102084.469772   \n",
+       "64      -89594.971914      -90591.944719     -102189.040603   \n",
+       "65      -89786.909552      -90765.041431     -102241.600388   \n",
+       "66      -90248.379001      -90744.950994     -102504.487078   \n",
+       "\n",
+       "    split4_test_score  mean_test_score  std_test_score  rank_test_score  \n",
+       "0       -98703.354492    112475.562546     7462.628569               67  \n",
+       "1       -85027.749120     89014.823175     7957.008142               56  \n",
+       "2       -79440.331044     85393.466480     9022.456430               35  \n",
+       "3       -74177.616156     84276.675940     9563.818797               27  \n",
+       "4       -75241.782711     84443.613862     8808.991745               29  \n",
+       "..                ...              ...             ...              ...  \n",
+       "62      -77821.033008     90242.148105     7701.974316               62  \n",
+       "63      -78056.396280     90514.260612     7699.296145               63  \n",
+       "64      -78243.200165     90725.395610     7664.535088               64  \n",
+       "65      -78394.469000     90875.165190     7635.553151               65  \n",
+       "66      -78564.416074     91112.560810     7665.721881               66  \n",
+       "\n",
+       "[67 rows x 14 columns]"
+      ]
+     },
+     "execution_count": 113,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# absolute the result\n",
+    "results[\"mean_test_score\"]=results[\"mean_test_score\"].abs()\n",
+    "results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create the plot\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "\n",
+    "# Plot mean test scores with error bars\n",
+    "plt.plot(results['param_n_neighbors'], results['mean_test_score'], '-o', color='blue')\n",
+    "\n",
+    "# Add labels and legend\n",
+    "plt.xlabel('Number of Neighbors')\n",
+    "plt.ylabel('Accuracy estimate')\n",
+    "plt.title('K-Nearest Neighbors Regression Performance')\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'n_neighbors': 25}"
+      ]
+     },
+     "execution_count": 116,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sacr_gridsearch.best_params_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## break"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sacramento_test[\"predicted\"] = sacr_gridsearch.predict(sacramento_test[[\"sq__ft\"]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "93573.27378694214"
+      ]
+     },
+     "execution_count": 120,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rmspe = mean_squared_error(\n",
+    "    y_true=sacramento_test[\"price\"],\n",
+    "    y_pred=sacramento_test[\"predicted\"]\n",
+    ")**0.5\n",
+    "\n",
+    "rmspe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'r2_score' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[124], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m r2 \u001b[38;5;241m=\u001b[39m \u001b[43mr2_score\u001b[49m(\n\u001b[0;32m      2\u001b[0m     y_true\u001b[38;5;241m=\u001b[39msacramento_test[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mprice\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[0;32m      3\u001b[0m     y_pred\u001b[38;5;241m=\u001b[39msacramento_test[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpredicted\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m      4\u001b[0m )\n\u001b[0;32m      6\u001b[0m r2\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'r2_score' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "r2 = r2_score(\n",
+    "    y_true=sacramento_test[\"price\"],\n",
+    "    y_pred=sacramento_test[\"predicted\"]\n",
+    ")\n",
+    "\n",
+    "r2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\tinti\\miniconda3\\envs\\dsi_participant\\lib\\site-packages\\sklearn\\base.py:493: UserWarning: X does not have valid feature names, but KNeighborsRegressor was fitted with feature names\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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/oEqVKujdu7dC/0JCQiASiTT274MPPsDjx49x/Phx+W3btm2DSCSSZwHq1q0Ld3d3zJw5E5s3b0ZCQgKnPsvk5ubKz+WqVati0qRJ6NGjBw4cOKDQrkmTJqhfv77G4x09ehT169dH165dVbbR933p0qULzp8/j/z8fDx48AD37t3DwIEDERISgujoaADSrFitWrVQr149zW+CCjweD71791a4rUmTJgqZfC5GjhyJv//+Gzdu3MCWLVtQp04dtG/fvly73NxcXLp0CQMGDFAYFre1tcWwYcPw+PFj+Tl56tQpdOnSBdWqVVNoJ8uQyej7N6KOSCQCn8+Hu7s7hg4diubNmyMqKgoODg7yNu7u7ujcubPC47h+Pp86dQoAMGTIEIXHDx48WGPfLly4gJycHIwdO9ZgCwK0/YyLjIyEra2t/N9NmjQB8HqIlstnnKnREKSFa9myJVq2bAlAmqKfOXMm1qxZg+XLl2P58uXyORylU/llSSQSdOvWDSkpKZg7dy4aN24MZ2dnSCQStGnTBvn5+Xr18enTp7h3757KIdLS88wA1SlnVerUqYM9e/aAx+PBwcEBAQEBGieWZmZmws7OrtxEUR6PB5FIhMzMTK36YOzjAtK5fbLftbL7yvZD2Vw1X19f+f3a4PF4OHHiBBYtWoTly5dj6tSp8PDwwJAhQ/Dll1/C1dUVT58+BQD5UJSmPqrSsGFDNGzYEIB0SHft2rWYMmUK5s6di19++UXtY1NSUhAREYEaNWpg//79sLe3l9/39OlTvHjxQuG20sqeh2X16NEDPj4+2LZtG7p164asrCwcPHgQEydOlH+wC4VCnD59Gl9++SVmz56NrKws+Pj4YNSoUZgzZ47GBSGOjo44c+YMAGlA6ufnp3RojOvfSEZGBmrVqqW2jb7vS9euXbFw4UKcO3cODx48gJeXF5o1a4auXbvi+PHjWLx4MU6cOKE2COTCyclJIZgApO8R1xIdMu3bt0e9evXw7bff4pdffsGkSZOUBgVZWVlgjCl9r8v+HWVmZkIkEpVrV/Y2Q/2NKHP8+HEIhULw+XzUqFEDnp6e5dooey1cP59ln21lj6vsdZfF5TqkLW0/48r2WzaVRHZ94/IZZ2oUgFkRPp+P+fPnY82aNfJxdVkg8PjxY9SsWVPp4+Lj43Ht2jVs374dw4cPl9+ubMKxLry8vODo6IitW7eqvL80bb8hOTg4qAxMVPH09ERJSQkyMjIUgiXGGNLS0lR+QJrruLr0IzU1tdztKSkpAF6/57ILWtmJzMouun5+fvKFG3fu3MEvv/yCBQsWoKioCJs3b5Yf89dff5VnT/XF4/EwefJkLFq0SH5Oq5KTk4OePXtCIpHgyJEj8nkdMl5eXvD09ERUVJTSx2v6gJVlPr755hu8ePECP//8MwoLC/HBBx8otGvcuDH27NkDxhiuX7+O7du3Y9GiRXB0dMRnn32m9jnUBdmlcf0bqVq1qsbJ//q+L61bt4aLiwuOHz+O5ORkdOnSBTweD126dMGqVatw5coVPHz4UO8AzJBk8694PJ7CZ15p7u7usLGx4fR35OnpibS0tHLtyt5mjL8RmaZNm5b7LC1L2XnD9fNZ9tmWmZmpEMwoe91llb4OGQrXzzhtaPqMMzUagrRQyk484PVwoexbQLdu3WBra4tNmzapPJbsj7Ls5PJvv/22XNuy3xrK3qfs9l69euH+/fvw9PSUZ+xK/5ijoKZsGfrOnTsVbt+3bx9yc3MVlqmrel36HteYunTpgoSEBMTGxirc/tNPP4HH46FTp04AIH/vr1+/rtDu4MGDao9fv359zJkzB40bN5Y/R/fu3WFnZ4f79+8r/T1rCixUndMpKSnIycmRn9PKFBUVoV+/fkhOTsbRo0eVftPu1asXMjMzIRaLlfYtMDBQbf8A6YW7oKAAu3fvxvbt2xEaGooGDRoobcvj8dC0aVOsWbMGVapUKfe7MIUePXrgzp07aoec9X1f+Hw+2rdvj+joaJw8eRLh4eEAgDfffBN2dnbyQEfTua/us8XQhg8fjt69e2P69OkqS3s4OzujdevW2L9/v0KfJBIJdu7ciRo1asiHgTt16oQTJ07IM1yAdIFG2dpt+v6NGAPXz2fZZ4as5qKMsoUGZbVt2xZCoRCbN29WWghXRptzgOtnnK6UfcaZGmXALFT37t1Ro0YN9O7dW76SKS4uDqtWrYKLi4u8SKW/vz9mz56NxYsXIz8/H4MGDYJQKERCQgKePXuGhQsXokGDBqhTpw4+++wzMMbg4eGBQ4cOyedvlNa4cWMAwNdff43hw4eDz+cjMDAQrq6u8m/+e/fulVcyb9y4MSZNmoR9+/ahffv2mDx5Mpo0aQKJRIKHDx/i2LFjmDp1Klq3bm3S9y88PBzdu3fHzJkzkZOTg7CwMPlqxWbNmmHYsGEKr1nZ69L3uMY0efJk/PTTT4iMjMSiRYvg5+eHw4cPY+PGjRgzZoz8wiESidC1a1csXboU7u7u8PPzw4kTJ7B//36F412/fh3jx4/HO++8g3r16sHe3h4nT57E9evX5Vkdf39/LFq0CJ9//jn+/fdfREREwN3dHU+fPsXly5fh7OwsL5egzMcff4wXL17g7bffRnBwMGxtbXHr1i2sWbMGNjY2mDlzptrXe/LkSSxZsgSvXr1SWF5etWpV1KlTBwMHDsSuXbvQs2dPTJw4Ea1atQKfz8fjx49x6tQp9O3bF/369VP7vjZo0AChoaFYunQpHj16hO+++07h/j/++AMbN27EW2+9Ja+6Lit7IAtMTGnSpEnYu3cv+vbti88++wytWrVCfn4+Tp8+jV69eqFTp04GeV+6dOki34FDlulydHRE27ZtcezYMTRp0kTjauY6derA0dERu3btQsOGDeHi4gJfX1+1gbeufH198dtvv2lst3TpUoSHh6NTp06YNm0a7O3tsXHjRsTHx2P37t3yL69z5szBwYMH0blzZ8ybNw9OTk7YsGFDuTIO+v6NGAPXz+du3bqhffv2mDFjBnJzc9GyZUucP38eO3bs0PgcLi4uWLVqFT766CN07doVo0aNQrVq1XDv3j1cu3YN69evB/D6+rJs2TL06NEDtra2aNKkidLhca6fcVxx+YwzOXPN/ifq7d27lw0ePJjVq1ePubi4MD6fz2rVqsWGDRvGEhISyrX/6aef2BtvvMEcHByYi4sLa9asmcJqn4SEBBYeHs5cXV2Zu7s7e+edd9jDhw/LrW5hjLFZs2YxX19fZmNjo7CCLjk5mXXr1o25uroyAAqr1F69esXmzJnDAgMDmb29PRMKhaxx48Zs8uTJCiuHALBx48Zxfh9Kr25SR9mqufz8fDZz5kzm5+fH+Hw+8/HxYWPGjGFZWVkK7dS9LmW4HlfbVZDq2jo7OyusEGKMsQcPHrDBgwczT09PxufzWWBgIFuxYoXCKj7GGEtNTWUDBgxgHh4eTCgUsqFDh8pX88nOkadPn7IRI0awBg0aMGdnZ+bi4sKaNGnC1qxZo7CyiDHGfvvtN9apUyfm5ubGBAIB8/PzYwMGDGDHjx9X+xr//PNPNnLkSBYUFMSEQiGzs7NjPj4+rH///iwmJqbc+1H69yBb4aXsp/T7UlxczFauXMmaNm0q/1to0KAB++STT9jdu3fV9k/mu+++YwCYo6Mjy87OVrjv1q1bbNCgQaxOnTrM0dGRCYVC1qpVK7Z9+3aNx+V6Pvj5+bHIyEiV95U9D7KystjEiRNZrVq1GJ/PZ97e3iwyMpLdunVL3kbf9+XatWsMAKtXr57C7bIVclOmTOHU1927d7MGDRowPp+v8Nmj6r3hulKQy+eEqlWYZ8+eZZ07d2bOzs7M0dGRtWnTRr7qtbTz58+zNm3aMIFAwEQiEZs+fbr8XCm7apzL34i2qyAzMjLUtlP3HnD9fH7x4gUbOXIkq1KlCnNycmLh4eHs1q1bGldByhw5coR16NCBOTs7MycnJxYUFMSWLVsmv7+wsJB99NFHrGrVqozH4ykcQ9n5wuUzTrYKcsWKFeVed+l+a/MZZyq8/zpJCCGEEEJMhOaAEUIIIYSYGAVghBBCCCEmRgEYIYQQQoiJUQBGCCGEEGJiFIARQgghhJgYBWCEEEIIISZGhVhNTCKRICUlBa6urgbbtJQQQgghxsUYw8uXL+Hr66vXvp4yFICZWEpKiso9GwkhhBBi2R49emSQjccpADMx2ca3jx49gpubm5l7QwghhBAucnJyULNmTY0b2HNFAZiJyYYd3dzcKAAjhBBCrIyhpg/RJHxCCCGEEBOjAIwQQgghxMQoACOEEEIIMTEKwAghhBBCTIwCMEIIIYQQE6MAjBBCCCHExCgAI4QQQggxMQrACCGEEEJMjAIwQgghhBATo0r4hBBCKgWxhOFy0nOkvyyAt6sDWgV4wNbGMFXNCdEWBWCEEEIqvKj4VCw8lIDU7AL5bT5CB8zvHYSIYB8z9oxUVjQESQghpEKLik/FmJ2xCsEXAKRlF2DMzlhExaeaqWekMqMAjBBCSIUlljAsPJQApuQ+2W0LDyVALFHWghDjoQCMEEJIhXU56Xm5zFdpDEBqdgEuJz03XacIAQVghBBCKrD0l6qDL13aEWIoFIARQgipsLxdHQzajhBDoQCMEEJIhdUqwAM+QgeoKjbBg3Q1ZKsAD1N2ixAKwAghhFRctjY8zO8dBADlgjDZv+f3DqJ6YMTkKAAjhBBSoUUE+2DT0OYQCRWHGUVCB2wa2pzqgBGzoEKshBBCKryIYB+EB4moEj6xGBSAEUIIqRRsbXgIreNp7m4QAoCGIAkhhBBCTI4CMEIIIYQQE6MAjBBCCCHExCgAI4QQQggxMQrACCGEEEJMjFZBEqInsYTR0nZCCCFaoQCMED1Exadi4aEEpGa/3sjXR+iA+b2DqLgjIYQQlWgIkhAdRcWnYszOWIXgCwDSsgswZmcsouJTzdQzQgghlo4CMEJ0IJYwLDyUAKbkPtltCw8lQCxR1oIQQkhlRwEYITq4nPS8XOarNAYgNbsAl5Oem65ThBBCrAYFYIToIP2l6uBLl3aEEEIqFwrACNGBt6uDQdsRQgipXCgAI0QHrQI84CN0gKpiEzxIV0O2CvAwZbcIIYRYCQrACNGBrQ0P83sHAUC5IEz27/m9g6geGCGEEKUoACNERxHBPtg0tDlEQsVhRpHQAZuGNqc6YIQQQlSiQqyE6CEi2AfhQSKqhE8IIUQrFIARoidbGx5C63iauxuEEEKsCA1BEkIIIYSYGAVghBBCCCEmRgEYIYQQQoiJUQBGCCGEEGJiZg3ASkpKMGfOHAQEBMDR0RG1a9fGokWLIJFI5G0YY1iwYAF8fX3h6OiIjh074ubNmwrHKSwsxKeffgovLy84OzujT58+ePz4sUKbrKwsDBs2DEKhEEKhEMOGDcOLFy8U2jx8+BC9e/eGs7MzvLy8MGHCBBQVFSm0uXHjBjp06ABHR0dUr14dixYtAmO04TIhhBBCuDNrALZs2TJs3rwZ69evR2JiIpYvX44VK1Zg3bp18jbLly/H6tWrsX79ely5cgUikQjh4eF4+fKlvM2kSZNw4MAB7NmzB+fOncOrV6/Qq1cviMVieZvBgwcjLi4OUVFRiIqKQlxcHIYNGya/XywWIzIyErm5uTh37hz27NmDffv2YerUqfI2OTk5CA8Ph6+vL65cuYJ169Zh5cqVWL16tZHfKUIIIYRUKMyMIiMj2ciRIxVu69+/Pxs6dChjjDGJRMJEIhH76quv5PcXFBQwoVDINm/ezBhj7MWLF4zP57M9e/bI2zx58oTZ2NiwqKgoxhhjCQkJDAC7ePGivE1MTAwDwG7dusUYY+zIkSPMxsaGPXnyRN5m9+7dTCAQsOzsbMYYYxs3bmRCoZAVFBTI2yxdupT5+voyiUTC6TVnZ2czAPJjEkIIIcTyGfr6bdYMWLt27XDixAncuXMHAHDt2jWcO3cOPXv2BAAkJSUhLS0N3bp1kz9GIBCgQ4cOuHDhAgDg6tWrKC4uVmjj6+uL4OBgeZuYmBgIhUK0bt1a3qZNmzYQCoUKbYKDg+Hr6ytv0717dxQWFuLq1avyNh06dIBAIFBok5KSguTkZKWvsbCwEDk5OQo/hBBCCKnczFqIdebMmcjOzkaDBg1ga2sLsViML7/8EoMGDQIApKWlAQCqVaum8Lhq1arhwYMH8jb29vZwd3cv10b2+LS0NHh7e5d7fm9vb4U2ZZ/H3d0d9vb2Cm38/f3LPY/svoCAgHLPsXTpUixcuFDzm0EIIYSQSsOsGbC9e/di586d+PnnnxEbG4sff/wRK1euxI8//qjQjsdT3NaFMVbutrLKtlHW3hBt2H8T8FX1Z9asWcjOzpb/PHr0SG2/CSGEEFLxmTUDNn36dHz22WcYOHAgAKBx48Z48OABli5diuHDh0MkEgGQZpd8fF5vbJyeni7PPIlEIhQVFSErK0shC5aeno62bdvK2zx9+rTc82dkZCgc59KlSwr3Z2Vlobi4WKGNLBtW+nmA8lk6GYFAoDBkSQghhBBi1gxYXl4ebGwUu2BraysvQxEQEACRSITo6Gj5/UVFRTh9+rQ8uGrRogX4fL5Cm9TUVMTHx8vbhIaGIjs7G5cvX5a3uXTpErKzsxXaxMfHIzU1Vd7m2LFjEAgEaNGihbzNmTNnFEpTHDt2DL6+vuWGJgkhhBBCVDLIVH4dDR8+nFWvXp398ccfLCkpie3fv595eXmxGTNmyNt89dVXTCgUsv3797MbN26wQYMGMR8fH5aTkyNvM3r0aFajRg12/PhxFhsbyzp37syaNm3KSkpK5G0iIiJYkyZNWExMDIuJiWGNGzdmvXr1kt9fUlLCgoODWZcuXVhsbCw7fvw4q1GjBhs/fry8zYsXL1i1atXYoEGD2I0bN9j+/fuZm5sbW7lyJefXTKsgCSGEEOtj6Ou3WQOwnJwcNnHiRFarVi3m4ODAateuzT7//HNWWFgobyORSNj8+fOZSCRiAoGAtW/fnt24cUPhOPn5+Wz8+PHMw8ODOTo6sl69erGHDx8qtMnMzGRDhgxhrq6uzNXVlQ0ZMoRlZWUptHnw4AGLjIxkjo6OzMPDg40fP16h5ARjjF2/fp29+eabTCAQMJFIxBYsWMC5BAVjFIARQggh1sjQ128eY1TG3ZRycnIgFAqRnZ0NNzc3c3eHEEIIIRwY+vpNe0ESQgghhJgYBWCEEEIIISZGARghhBBCiImZtQ4YIYQQ0xNLGC4nPUf6ywJ4uzqgVYAHbG3UF7cmhBgWBWCEEFKJRMWnYuGhBKRmF8hv8xE6YH7vIEQE+6h5JCHEkGgIkhBCKomo+FSM2RmrEHwBQFp2AcbsjEVUfKqKRxJCDI0CMEIIqQTEEoaFhxKgrO6Q7LaFhxIgllBlIkJMgQIwQgipBC4nPS+X+SqNAUjNLsDlpOem6xQhlRgFYIQQUgmkv1QdfOnSjhCiHwrACCGkEvB2dTBoO0KIfigAI4SQSqBVgAd8hA5QVWyCB+lqyFYBHqbsFiGVFgVghBBSCdja8DC/dxAAlAvCZP+e3zuI6oERYiIUgBFCSCUREeyDTUObQyRUHGYUCR2waWhzqgNGiAlRIVZCCKlEIoJ9EB4kokr4hJgZBWCEEFLJ2NrwEFrH09zdIKRSoyFIQgghhBATowCMEEIIIcTEKAAjhBBCCDExCsAIIYQQQkyMAjBCCCGEEBOjAIwQQgghxMQoACOEEEIIMTEKwAghhBBCTIwCMEIIIYQQE6MAjBBCCCHExCgAI4QQQggxMQrACCGEEEJMjDbjJoQYnVjCcDnpOdJfFsDb1QGtAjxga8Mzd7cIIcRsKAAjhBhVVHwqFh5KQGp2gfw2H6ED5vcOQkSwjxl7Rggh5kNDkIQQo4mKT8WYnbEKwRcApGUXYMzOWETFp5qpZ4QLsYQh5n4mfo97gpj7mRBLmLm7REiFQRkwQohRiCUMCw8lQNklmwHgAVh4KAHhQSIajrRAlLkkxLgoA0YIMYrLSc/LZb5KYwBSswtwOem56TpFOKHMJSHGRwEYIcQo0l+qDr50aUdMQ1PmEpBmLmk4khD9UABGCDEKb1cHg7YjpkGZS0JMg+aAEUKMolWAB3yEDkjLLlCaTeEBEAmlJSmMiUpgaIcyl4SYBgVghFQQlhZo2NrwML93EMbsjAUPUAjCZL2a3zvIqH1UNpFc5CbAoFa14O/lbBHvk6WhzCUhpkEBGCEVgKWuWIsI9sGmoc3LB0Em6JtsInnZ7FtaTiHWHL8r/7clvE+WxFIyl4RUdDzGGM2kNKGcnBwIhUJkZ2fDzc3N3N0hFYCqQEOW09k0tLnZgwtTZ+fEEoZ2y06qncskY0nvk6WQnVOA8swlvVekMjL09Zsm4RNixaxlxZqtDQ+hdTzRN6Q6Qut4Gn3IT9NE8tIs6X2ylMKnssylSKg4zCgSOlDwRYiB0BAkIVZMmxVroXU8TdcxM9N2grglvE+WNowcEeyD8CCRRc0rJKQioQCMECtGK9aU03WCuLneJ5Xz1f4rfGqurJMsc0kIMTwagiTEipljxZqlDJOpI5tIrm2uxhwr+6xlGJkQYliUASPEipl6xZqlDZOpoq4EhjLmXNlHw8iEVE6UASPEiskCDQDlsj2GrrVlbfsDqppIXpapapKpQsPIhFROlAEjxMqZotaWpmEyHqTDZOFBIouapF12InnyszzsvvwQaTmmrUmmDhU+JaRyogCMkArA2CvWrHmYrOxE8vGd61rUyj4qfEpI5UQBGCEVhDFXrFWkYTJLW9lnCVs2EUJMj+aAEUI0omEy46LCp4RUPpQBI4RoRMNkxkeFTwmpXCgAI4RoRMNkpmFpw6OEEOOhIUhCCCc0TEYIIYZDGTBCCGfmGCYTSxgNyxFCKhwKwAghWjHlMJm1VN4nhBBt0RAkIcQiWVvlfUII0QYFYIQQi0MbVBNCKjoKwAghFkebyvuEEGKNKAAjhFicilR5nxBClKEAjBBicajyPiGkoqMAjBBicWSV91UVm+BBuhqSKu8TQqwVBWCEEIsjq7wPoFwQRpX3zUcsYYi5n4nf454g5n4mLYIgRA9UB4wQYpFklffL1gETUR0ws6CabIQYFo8xRl9hTCgnJwdCoRDZ2dlwc3Mzd3cIsXhUCV83hnzfZDXZyl4sZEejrahIZWDo6zdlwAghFo02qNaeIbNVmmqy8SCtyRYeJKLAmBAt0BwwQgipQAy9gwDVZCPEOCgAI4TohCZkWx5j7CBANdkIMQ6zB2BPnjzB0KFD4enpCScnJ4SEhODq1avy+xljWLBgAXx9feHo6IiOHTvi5s2bCscoLCzEp59+Ci8vLzg7O6NPnz54/PixQpusrCwMGzYMQqEQQqEQw4YNw4sXLxTaPHz4EL1794azszO8vLwwYcIEFBUVKbS5ceMGOnToAEdHR1SvXh2LFi0CTaMjlU1UfCraLTuJQd9fxMQ9cRj0/UW0W3aS9mc0M2Nkq7jWWvNyFnA+JiHEzAFYVlYWwsLCwOfzcfToUSQkJGDVqlWoUqWKvM3y5cuxevVqrF+/HleuXIFIJEJ4eDhevnwpbzNp0iQcOHAAe/bswblz5/Dq1Sv06tULYrFY3mbw4MGIi4tDVFQUoqKiEBcXh2HDhsnvF4vFiIyMRG5uLs6dO4c9e/Zg3759mDp1qrxNTk4OwsPD4evriytXrmDdunVYuXIlVq9ebdw3ihALQptkWy5jZKs01WSTmfq/a/S7J0QLZl0F+dlnn+H8+fM4e/as0vsZY/D19cWkSZMwc+ZMANJsV7Vq1bBs2TJ88sknyM7ORtWqVbFjxw689957AICUlBTUrFkTR44cQffu3ZGYmIigoCBcvHgRrVu3BgBcvHgRoaGhuHXrFgIDA3H06FH06tULjx49gq+vLwBgz549GDFiBNLT0+Hm5oZNmzZh1qxZePr0KQQC6be9r776CuvWrcPjx4/B42megEqrIIk1E0sY2i07qTLLwoO0TMS5mZ1pQrYZxNzPxKDvL2pst3tUG60WNsiCbgBKhzcBWhFJKj5DX7/NmgE7ePAgWrZsiXfeeQfe3t5o1qwZvv/+e/n9SUlJSEtLQ7du3eS3CQQCdOjQARcuXAAAXL16FcXFxQptfH19ERwcLG8TExMDoVAoD74AoE2bNhAKhQptgoOD5cEXAHTv3h2FhYXyIdGYmBh06NBBHnzJ2qSkpCA5OVnpaywsLEROTo7CDyHWiiZkWzZj7SAgq8lWzU31MKOuc8wIqazMGoD9+++/2LRpE+rVq4c///wTo0ePxoQJE/DTTz8BANLS0gAA1apVU3hctWrV5PelpaXB3t4e7u7uatt4e3uXe35vb2+FNmWfx93dHfb29mrbyP4ta1PW0qVL5fPOhEIhatasqeFdIcRy0YRsy2bMHQQign2w6t0QtW0oACeEO7MGYBKJBM2bN8eSJUvQrFkzfPLJJxg1ahQ2bdqk0K7s0B5jTONwX9k2ytoboo1sBFdVf2bNmoXs7Gz5z6NHj9T2mxBLRptkWz5ZtkokVPwdiIQOeg8PPntVyKkdBeCEaGbWQqw+Pj4ICgpSuK1hw4bYt28fAEAkEgGQZpd8fF5/aKSnp8szTyKRCEVFRcjKylLIgqWnp6Nt27byNk+fPi33/BkZGQrHuXTpksL9WVlZKC4uVmhTNtOVnp4OoHyWTkYgECgMWRJiyTRVT5cNcaVlFyidCySbA0abZJtXRLAPwoNEBt9BgAJwQgzHrBmwsLAw3L59W+G2O3fuwM/PDwAQEBAAkUiE6Oho+f1FRUU4ffq0PLhq0aIF+Hy+QpvU1FTEx8fL24SGhiI7OxuXL1+Wt7l06RKys7MV2sTHxyM19fUqnmPHjkEgEKBFixbyNmfOnFEoTXHs2DH4+vrC39/fEG8JIWbDpbQEbZJtPWQ7CPQNqY7QOp6cfieaarsZa44ZIZWRWVdBXrlyBW3btsXChQvx7rvv4vLlyxg1ahS+++47DBkyBACwbNkyLF26FNu2bUO9evWwZMkS/PXXX7h9+zZcXV0BAGPGjMEff/yB7du3w8PDA9OmTUNmZiauXr0KW1tbAECPHj2QkpKCb7/9FgDw8ccfw8/PD4cOHQIgLUMREhKCatWqYcWKFXj+/DlGjBiBt956C+vWrQMAZGdnIzAwEJ07d8bs2bNx9+5djBgxAvPmzVMoV6EOrYIklkjbvf5oY+aKh+vvVNWKSFoFSSo6Q1+/zb4Z9x9//IFZs2bh7t27CAgIwJQpUzBq1Cj5/YwxLFy4EN9++y2ysrLQunVrbNiwAcHBwfI2BQUFmD59On7++Wfk5+ejS5cu2Lhxo8KE9+fPn2PChAk4ePAgAKBPnz5Yv369Qs2xhw8fYuzYsTh58iQcHR0xePBgrFy5UmEI8caNGxg3bhwuX74Md3d3jB49GvPmzeNUggKgAMxYaMNm3elaWoLec26s4X2iAJwQzSpcAFbZUABmeHQx0I+xakcR6zg3jR2AW0MASggXhr5+m3USPiH6UvXNXVaVnYZDNKPSEsZhLeemNrXdSgfgsjlm6lhDAEqIuZh9L0hCdGWMjYcrI1rZZnjWdG4aKwCnLasIUY8CMGK1qCq7YdDKNsOzpnPTGAG4NQWghJgLBWDEatHQmWFQaQnDs6Zz0xgBuDUFoISYCwVgxGrR0JnhGLN6emVkTeemMQJwawpACTEXmoRPrBZVZTcsY1VPr4ys7dyUBeBlJ8yLdJwwb00BKCHmQgEYsVqyb+5jdsaCB+VFIWnoTDtcVrYRzazx3DRkAG5tASgh5kBDkMSq0dAZsVTWeG7qsn2RquPQvEJC1KNCrCZGhViNg4o9EktVmc9NqgNGKhKqhG/lKAAzj8p8ESTEnOhvj1QUVAmfEC3Rt3BCzIfmFRKiHM0BIxUaVeMmhBBiiSgAIxUWVeM2P7GEIeZ+Jn6Pe4KY+5n0XhNCyH9oCJJUWLpuMkwMg4Z+CSFENcqAkQqLqnGbDw39EkKIehSAkQqLqnGbBw39EkKIZloNQWZnZ+PAgQM4e/YskpOTkZeXh6pVq6JZs2bo3r072rZta6x+EqI1qsZtHhVt6JfKKBBCjIFTAJaamop58+Zh165dEIlEaNWqFUJCQuDo6Ijnz5/j1KlTWLlyJfz8/DB//ny89957xu43IRpZ43YwFUFFGvrVdh4bBWuEEK44BWBNmzbF+++/j8uXLyM4OFhpm/z8fPz2229YvXo1Hj16hGnTphm0o4TowtCbDBPNKsrQr2weW9nsqWweW9nthGjRASFEG5wq4WdkZKBq1aqcD6pt+8qEKuGbB2UmTEcsYWi37KTGod9zMztb7O9A9hpUDaWWfQ2qgjXZq7PUvR8JIdwZ+vrNaRK+tsEUBV/E0hhqk2GiWUXYiFmbeWy06IAQogut64CdPHkS+/fvR3JyMng8HgICAjBgwAC0b9/eGP0jhFghax/61WYeW0VbdEAIMQ2tArDRo0fju+++g7u7O+rXrw/GGC5cuIANGzZg7NixWLdunbH6SQixMhHBPggPElnl0K8289gq0qIDQojpcA7ADhw4gG3btmHr1q0YPnw4eDzph6hEIsH27dsxZswYhIeHo0+fPkbrLCHEuljrRszalDC5nPSc0zGNteiA5jcSYp04B2Dbtm3DlClTMGLECIXbbWxsMHLkSNy+fRtbtmyhAIxYFLo4EV1oU8LEnPXmaOUlIdaLcyX82NhY9OvXT+X9b7/9Nq5evWqQThFiCFHxqWi37CQGfX8RE/fEYdD3F9Fu2UnaBodwIpvHJhIqZq5EQgeFVY3mWnRA2z0RYt04laEAAAcHB9y/fx/Vq1dXev+TJ09Qt25d5OfnG7SDFQ2VoTCNilQWgLJ45sX1/TdlNkrbMhmEEP0Z+vrNeQiyqKgI9vb2qg9kZ4eioiK9O0SIvjSVBeBBWhYgPEhk8RcnGmIyP67z2Ey56IBWXhJi/bRaBTl37lw4OTkpvS8vL88gHSJEXxXl4qRtJXZifJqyYaZadEArLwmxfpwDsPbt2+P27dsa2xBibhXh4lSRsngVhSVlIyvKdk+EVGacA7C//vrLiN0gxHAqwsWpomTxKgpLyEaWzr55uQggchPgaU6hyVdeEkIMQ+tK+GWVlJSgoKAALi4uhugPIXozZ1kAQ6kIWbyKwhKykcqyb1Wc+PLnV1cmgxBimTiXoThy5Ah27NihcNuXX34JFxcXVKlSBd26dUNWVpbBO0iItkxVFkAsYYi5n4nf454g5n6mQff6qwhZPGWM+Z4ZizbZSGNQVW4iO68YACB04ivcXrZMBiHEMnHOgK1cuRJvv/22/N8XLlzAvHnzsGjRIjRs2BCff/45Fi9ejNWrVxulo4Row9h7ERp7PpCmLB4A2PCArFzrWXlsSXOotGHObCSX7Jsj3xYbPmyOZ7mFVKaEECvCuQ6Yt7c3/vzzTzRr1gwAMGXKFCQkJCAqKgqANEM2ceJE3L1713i9rQCoDphpGaOGlqlqjKl6nrLPaQ3ZDmuuyxZzPxODvr+osd3uUW0MPh/PnM9NCFFk6Os35yHIly9fwtPz9R/4uXPn0LlzZ/m/GzVqhJSUFL07RIghycoC9A2pjtA6ngYZdlSXkQCk84EMMbQWEeyDDYObQVOXDfV8xmLK98wYZNlIVb8GHqSZPGPMKaS5gIRUXJwDMF9fXyQmJgIAXr16hWvXriEsLEx+f2ZmpsoaYYRUFKaeD+TuLIC6uMTY848MwdxzqPRla8PD3MiGKhd0AMab8F5R5wISQrSYAzZgwABMmjQJs2fPxpEjRyASidCmTRv5/X///TcCAwON0klCLIWpMxIVIQNi7a8hKj4Viw8nKr3PUHMKVbH0Fb20TRYhuuMcgM2fPx8pKSmYMGECRCIRdu7cCVtbW/n9u3fvRu/evY3SSUIshakzEubOgBjiAmvu16APTfPw5kYadwGBbEXvmJ2xFlduwloXVRBiKThPwieGQZPwrZtsE2RNGQlDbYJs6ucrzVAXWHO+Bn1Y0obXlhbsWPOiCkJ0ZbbNuAkhps9ImCsDYsjK75acxVHH3LsRlM0+np7eCVcfZJl2uC8vD8jIKNevb3+6AN+XhUofwgPw7U+nED6mrcX9TkklUqsWwLPs849zABYQEACekhcjFAoRGBiIadOmoWXLlgbtHCGWyNg1xsz9fMao/G7o12CKuUfmnLumLuPVN6S6wZ9PqfR0oFEj4NkzhZttARzg8vgVxugUIRwVFgL29ubuhVqcA7BJkyYpvf3Fixe4cuUKQkNDcezYMXTq1MlQfSPEYkUE+yA8SGSyCci6PJ+uQYqxMj/6vmey1xOdkIbf4lLwvFQRWmMMx5lr7pol7DsJAPjmG2nwZWurcCErYQzFJZpnrvDteLDj8cAASBjAwMADDza88jtUEFIZGWwO2OLFi3H8+HGcPn3aEIersGgOGDEFfeYM/R73BBP3xGl8jq8HhpgsG6Ps9ZRmjLlH5pi7ZjHzzl6+lA7hvHgB7NsH9O8vv0ub4rDZ+UUWNXeNEH2YrRCrJgMGDMDNmzcNdThCiI5U7R0oy6BExaeqfby2mR9j7++o6vWUZoyCrqbaU7Q0i6mZ9v330uCrfn2gb1+Fu7gWps3KLdLrPCSkojNYAEYIMT9DVJ3XpvJ7VHwq2i07iUHfX8TEPXEY9P1FtFt2UuuLq6ogTt3rKcsYwYls7ppIqBiUGmvDa4uomVZUBKxZI/3/6dOlQ5ClcAlM50Y2xOLDxt39QNk5Y42bvZPKy2CrIH/99VcEBwcb6nCEEB0YYv4W11WL0QlpBpmrpG64VOhor/b1KGPo4MSU8/0somba7t3A48eASAQMHaq0iaZFFZp+b/quIFV2zlRx4gMAXuQVy2+j4U5iyTgHYN98843S27Ozs3HlyhUcPXoUf/75p8E6Rog2KnNF7tKv/U+OmSdNQYqmC2x4kAjtlp3Ue6WkpgnnI8P8Ob2e0koPjRrqnJDtKaovTX0ye+V7iQRYvlz6/5MmAQ6qAz11genvcU84PZ0uwbKqc6Z04CVj8oULhGiBcwC2RpaSLsPNzQ0NGjTAuXPn0Lp1a4N1jBCuLK1IpSlpmpyuCpcMiroLbMz9TL0zHFzKXRzgeCEHFIMTSzwnuPRJp5ppd+4At28Db7whzVrp4/BhICEBcHUFRo/W2FxVYGqsTJ42Q9KA7mVTCDEFzgFYUlKSMftBiE4sZsm+GWjaJkcVT2d7zhkUVRdYQ8xV4jJc+jy3GB7O9sjKLVL7OkvPPVp/8i7WHL9bro05zwltzlOtaqadOQP07Ank5kr/Xbs20LYtEBoKeJb5vfH5gIcH4OUl/fH0lN5Wmiz7NXo0IBTq/HqNlcnTdM4oY+yCuYToiirhE6tljIKh1kLbTEBpfUN89X4/DJHh4BrEvRXii23nk8tlhEoTCR3Qp6kPFv2RiLQc5cc11zmhy3nKad7ZiRNA795Afj7g7S2tWP/vv9KfnTu5dc7HR7rSMTAQcHcHzp2T1vxSUfeRK2PtfqDP/D5L3eydVF6cArCvvvoKn376KZydnTW2vXTpEp49e4bIyEi9O0eIOubeKsacdMkEyIQH6TlMBcNkOLgGceFBIrQK8CiXEfJw5qNfSHV0DRIhK7cI437WnA00xzmh63mqdt5ZVBTQrx9QUAD06AHs3y+t/H3pEnDhAnDlijQwK62oCMjMlBZXff5cOt8rNVX6U7p+47BhgK+vHq9Yyhg7OOiz+MASN3snlRunACwhIQF+fn5455130KdPH7Rs2RJVq1YFAJSUlCAhIQHnzp3Dzp07kZqaip9++smonSYEsJAl+2ai62vyMdAEbkNkOLQJ4mxteCozQrLipdpkA015Thj8PD10CBgwQBpQ9ekD/PILIBBIJ8x36yb90UQikQZhSUnS+WOyeWT5+cDChdz6wYGhV5BqOmeUMfrCBUJ0xCkA++mnn3D9+nVs2LABQ4YMQXZ2NmxtbSEQCJCXlwcAaNasGT7++GMMHz4cAoHAqJ0mBLCQJftqGHNlpravyRiFQ/XNcGgbxKnKCOmSDTTlOWHQ8zQ6WlqVvqQEePtt4OefOe13p/RclM0Fe+MNTv3TlaFWkMqOpeqcUcaSN3snROutiBhjuH79OpKTk5Gfnw8vLy+EhITAy8vLWH2sUGgrIsMxx1YxXHFZ8aZPgKbptZdlzBWA+gaa+q5Y5Lp1EmCec8Jg5+nTp0CTJtJNst99F9i1C7DT/B3aEleE6ovqgBFzMPT122B7QRJuKAAzLNnqMkB5BkXXFW/6BBWqVryV7hMAvS+Kml77pK714e/lZBV10fR5v7nuTQhI3xtTrIIs+3qycgsx7ud/AOh2nopLxHjZuRuqnD2JvPoNIYj9G7bOThr7weVctNbgRNk5A6DS1gMkxkcBmJWjAMzwDP0NX5/jcdlMuYoTH1lKikZyuSiWv7AXYfHhipXd0BbXbKDITYAFfRoZ/X1Rdf70aeqDg9dStf5dRcWn4vbMRZh4ZDMK7OzR+/01eFU3UOPjLGZjb0IqCArArBwFYMZhqPlW+mYMtMnGKKPuoqjqwj43siHcnQVG+9ZvDbsMqMoGykzuWh/jO9c1er81nT8bBjeHu7M95/cyKj4VG1b+gn07psFeUoLPu43FrmY9OZ2PXM/F3aPaKMzRsobfNyHmYOjrN9UBIxWCISb6GqKumL6r61SVJFBXyHPcz/9g09Dm6BtSXa/nVsZa5g+pWhBgyr5yOX8WH07gnHESSxiW/3oVPxxcDntJCaLqh2JXSA+F46k7H3VZfWktv29CKgIKwEiFpMu3eEPUFTPU6rrSF0VdAkNDZDFUBX2p2QUYvTMWH4b5o+t/NbosIUNiyk2zldH7/Ll6Fdi4UV6/K+tVIVb/HY/aWSlIcfXCzIgJAI/H+Xjarr6szLtKEGIOOgdg9+7dw/3799G+fXs4OjqCMQYez/wfwoTo+i3eEPWadKlTpEzpi6e2F3ZDZDG4VNrfcj4ZW84nW1SGxJAlD7Sl8/nDGLB5s7T6fFGR/Gav/37EPBtM7jUV2Y6uWj2vNnXWKvOuEoSYi422D8jMzETXrl1Rv3599OzZE6mpqQCAjz76CFOnTjV4BwnRhuxbfNmARfYtPio+VeVjDVGvSVanCHg970cbPJQvlqrNhV3V65dlrY5cT+F0LG1qa3F5bysDnc6f3Fxp5fmxY6XBV+/ewJo1wJo1SPr8CyzqPApD3/sCl2o11vp51Z2LZetjaRPkE0IMQ+sAbPLkybCzs8PDhw/h5PR6GfR7772HqKgog3aOEG1o+hYPSL/FiyXK8zqyjIGqwElZcKSMbD6SSKjbcGTZopFcL+xeLgKNWavxu//BkeuaAyVt5rJxeW8NTSxhiLmfid/jniDmfqbJnledVh62eCclFvOPf4vff5yM9b8vwxuP4qUZLig5f27dAlq3ltbzsrUFVqwAfv9dmgmbNAm1Fs3G0a7v4aJfE6XPx+V8VHUuioQOCkOKlXlXCULMReshyGPHjuHPP/9EjRo1FG6vV68eHjx4YLCOEaItfefgGHID4dLzkY7Gp+KnGM1/G1Uc+fjq7cblhvK4DiWBQWPWSsKAsT/HYrON+vk82s5lM+Uei2afKH71KnD3rrQwalqa9L83b8L277+xQiKRN2uadhe9bp1FgncAfmzeG3/WD8U3tXNg+9lM4Phx4B9pXTCIRMDevUD79gpPY6jzkcvcOEvfVYKQikjrACw3N1ch8yXz7Nkz2oKImJUhvsUbcgNhWxseWgV4ICElm1P7DUOaI6xu+R0luF6In+UWcu6bpkn7LfzcdZrLpm+GRNPiAbNPFP/lF+C991TfX78+HjYLxRbmi8Dbseh38y8EpSdhWdQ3WBb1Tfn23bsD27dLgzAlDHU+apobZ4jN1Qkh2tF6CLJ9+/YKm23zeDxIJBKsWLECnTp10rkjS5cuBY/Hw6RJk+S3McawYMEC+Pr6wtHRER07dsTNmzcVHldYWIhPP/0UXl5ecHZ2Rp8+ffD48WOFNllZWRg2bBiEQiGEQiGGDRuGFy9eKLR5+PAhevfuDWdnZ3h5eWHChAkoKjUhFgBu3LiBDh06wNHREdWrV8eiRYtAZdQsh6G+xUcE++DczM7YPaoNvh4Ygt2j2uDczM5aX9ij4lPRbtlJLD6cqLadbCipTW3VF0hNQ0nhQSI8e8k9ACs9n0fWz0HfX8TEPXEY9P1FdFhxCn2a+sj7x1XyszwtWitS1o92y07K55bpO8Sst7w8YNo06f+HhEgDsQkTgC+/BHbuBB49Am7fRq092zFv95cI2LcLp47/jQcz54P5+UkfV6MG8MEH0mHHtDQgKkpl8CVjqPNRHW3mixFCDEPrDNiKFSvQsWNH/P333ygqKsKMGTNw8+ZNPH/+HOfPn9epE1euXMF3332HJk0U5zosX74cq1evxvbt21G/fn188cUXCA8Px+3bt+HqKl0RNGnSJBw6dAh79uyBp6cnpk6dil69euHq1auwtbUFAAwePBiPHz+Wz1H7+OOPMWzYMBw6dAgAIBaLERkZiapVq+LcuXPIzMzE8OHDwRjDunXrAEgLsIWHh6NTp064cuUK7ty5gxEjRsDZ2ZkWH1gIQ36L13c1napMjbI+AfoNJUUnpKmteK5K6Un7yjJK351JwsftA8pVb1dn7fE7CBS56BSsaspsCR3tdRpilmXV0rLz8Ty3CB4uAojcdChRsXKlNMiqVQu4cAFwdFTZVH7+1PEEOgQDX84Fnj0DvL0hZv9tl5NaAO9XmZz6YYrVnYbM/hJCNNOpEn5aWho2bdqEq1evQiKRoHnz5hg3bhx8fLT/A3316hWaN2+OjRs34osvvkBISAjWrl0Lxhh8fX0xadIkzJw5E4A021WtWjUsW7YMn3zyCbKzs1G1alXs2LED7/03LJCSkoKaNWviyJEj6N69OxITExEUFISLFy+idevWAICLFy8iNDQUt27dQmBgII4ePYpevXrh0aNH8PX1BQDs2bMHI0aMQHp6Otzc3LBp0ybMmjULT58+lQ+1fvXVV1i3bh0eP37MuQQHVcI3LmPtDakNTVvAlKbv3CWugZ4yuz5sjWm/XtO4Vc3p6Z1w9UEWohPSsPV8stpj6rK9Ddctc2Z0D8TkX65pPN7XA0PkRWmVzReT0eq9f/wYCAyUZsH27FE/DKmGvttcmaLGGVXCJ0Q5i6iELxKJsHDhQr2fHADGjRuHyMhIdO3aFV988YX89qSkJKSlpaFbt27y2wQCATp06IALFy7gk08+wdWrV1FcXKzQxtfXF8HBwbhw4QK6d++OmJgYCIVCefAFAG3atIFQKMSFCxcQGBiImJgYBAcHy4MvAOjevTsKCwtx9epVdOrUCTExMejQoYPCPLfu3btj1qxZSE5ORkBAgNLXV1hYiMLC10NDOTk5+r1hRC1zfYsvfdF69rKQU/A1N7IhRoQF6Hxx41KrSxn5pH2e+kn7sozS1QdZCK3jidA6nhA68rHm+F2Nj9FmMj7XxRPPc4tUtilNU2FRmVRt5o3NmiUNvsLCgHff5dSPsvSZv2bKhQfmrKVGSGWidQC2bds2uLi44J133lG4/X//+x/y8vIwfPhwzsfas2cPYmNjceXKlXL3paWlAQCqVaumcHu1atXkqy3T0tJgb28Pd3f3cm1kj09LS4O3t3e543t7eyu0Kfs87u7usLe3V2jj7+9f7nlk96kKwJYuXWqwYJVwY+qK6OqyLOp4uQoU+qRt5kGbWl0yCpP2X3GbM1Z6Yr2/l7PWjzFUWw8XgUEKi5alscDopUvSOV4AsHatQjV6rvQpdGr2hQeEEKPQehL+V199BS+v8iu1vL29sWTJEs7HefToESZOnIidO3fCwUH1pOiyQ3tcKu6XbaOsvSHayEZv1fVn1qxZyM7Olv88evRIbd+JYci+xfcNqY7QOp5GDb6UFT7lovRiAE0T0JXRZcWhh7O9/IKty6IFY5Qr4NpW5OZgsMKiMhoLjDImrcsFACNGAC1bcuprWboWOjX7wgNCiNFonQF78OCB0myPn58fHj58yPk4V69eRXp6Olq0aCG/TSwW48yZM1i/fj1u374NQJpdKj23LD09XZ55EolEKCoqQlZWlkIWLD09HW3btpW3efr0abnnz8jIUDjOpUuXFO7PyspCcXGxQhtZNqz08wDls3SlCQQCKs9RQek7BChbDKBrhkOXmkxzIhvKj6XLogVjlCvQ5pi2NjxOQ8zaBqe5NxOBo3HSyfUODoC3N1C1KpCRAVy8CDg7S1c76kjXEimG2J+UEGKZtA7AvL29cf369XLDcdeuXYOnJ/cPgC5duuDGjRsKt33wwQdo0KABZs6cidq1a0MkEiE6OhrNmjUDABQVFeH06dNYtmwZAKBFixbg8/mIjo7Gu//Ny0hNTUV8fDyWL18OAAgNDUV2djYuX76MVq1aAQAuXbqE7OxseZAWGhqKL7/8EqmpqfJg79ixYxAIBPIAMTQ0FLNnz0ZRURHs7e3lbXx9fcu9F8T6cRkO1GUIEJBeNOdGSjM1+gxN6bLvpEj4euWeLoU+DVmsVtdjGqqwaMvHN9E78Qw6/BsL/2UadgeYPRsoNUdUW1zLc5Ttt7617WhCPSGWS+sAbODAgZgwYQJcXV3R/r/KzadPn8bEiRMxcOBAzsdxdXVFcHCwwm3Ozs7w9PSU3z5p0iQsWbIE9erVQ7169bBkyRI4OTlh8ODBAAChUIgPP/wQU6dOhaenJzw8PDBt2jQ0btwYXbt2BQA0bNgQERERGDVqFL799lsA0jIUvXr1QmBgIACgW7duCAoKwrBhw7BixQo8f/4c06ZNw6hRo+QrHQYPHoyFCxdixIgRmD17Nu7evYslS5Zg3rx5tAm5CZnigsJ1wrM+RUcXH06AjQ10Lq0AqA9cVMkqU6xVl0ULxljooO0xuRYWVfbeVnv5DJ+f2oo+iWfktzE+H7x27YAuXaRzvDIygPR06X99fIDJk7V+TTJiCcPuy5pHB0RugnKZQ32GfM2+YwAhRC2tA7AvvvgCDx48QJcuXWBnJ324RCLB+++/r9UcMC5mzJiB/Px8jB07FllZWWjdujWOHTsmrwEGAGvWrIGdnR3effdd5Ofno0uXLti+fbu8BhgA7Nq1CxMmTJCvluzTpw/Wr18vv9/W1haHDx/G2LFjERYWBkdHRwwePBgrV66UtxEKhYiOjsa4cePQsmVLuLu7Y8qUKZgyZYpBXzNRzRQXFG2GA/XZlkV2vJFh/pzaqwr2VAUuqiw+nIjuwT4KQasuixaMsdDBkMcsHZzKfpd24hIMv3oIk8//DJeifIh5NjjQqBNqjxqC5h8MAEp9ruhC1ZeDy0nPkZaj+XczqFUtrbOcqoZ8aeI+IZZPpzpgAHDnzh1cu3YNjo6OaNy4MfxklZ6JWlQHTDeqLiiGrO/FtR6VrMaVWMLwxpfReJ5brNPz8QC4O/M5PX73qDZqMz5iCcP280kaq+4D+pe/sCayoN078RqWHf0GDZ5JV1D/4xOItf0nYdDovgYJRNR9OSgskWDinjiNxyhdv6zssbWpbafteUwI4cbQ12+tV0HK1K9fH++88w569epFwRcxKlOtBNNlpdqwNrqf+wzA89xieDjbq9zuR7ZNkaZJ7bY2PHi5clvssfhwosYVltZALGGIuZ+J3+OeIOZ+ptLff0SDqjhffB6//TwDDZ49QIHQHbFzl6Pg9Fls/eYTgwVfylbCyrJNyc9yOR2ndEa19GsTOtpjw+BmKrehKvsadF1xSQgxLU5DkFOmTMHixYvh7Oysccht9erVBukYITKmWgmmzYRnXWt/KfNWiC+2nU/We1K7NkOiljYUpe3cPk7D0UlJwLBhsJFtkfbee3DYsAHNtVgsxKXfmhZR7L78ECI3BzzN4TaMqOq1zY0Mgruzvcb3yBCb0hNCjI9TAPbPP/+guFg6TBIbG6ty0jlNRifGYKoLCtcAJvlZLtYev6vT9j/KhAeJ0CrAQ+9J7dqsitS0wtKUtJ3bp25+08wfzkD4hjNCM+4B8+YBL19K53Zt2AAMHapTEVV1uHw5SMspxOSu9bH2+B2NQba61zbuZ2nArGyYsjRj1GojhBgepwDs1KlT8v//66+/jNUXQpQy1QWF64Tn3ZcfGiT44gGo5iaAhDEUlkiwckBTgAc8e1UILxcBwIBnuYU4f++Z/P/VZT60XRVpCTWktJ0sXjbj5JWbhf7xJ9H5/hXUef4YVXNfKB4oLAzYsQNQsVOFvrgG/f5eThpXeepTkqQ0Y9RqI4QYnlarIEtKSuDg4IC4uLhyJSQIMRZTXVC41KMa+EYtrDl+R6/nkWEACkokGPLD6yLAPkIH9Gnqg4PXUlVmVtRlh7RdFQlwCyKMUf5Dl4DjctJzpGflovO/V/He9WPofP8K+BKxwmNTXTzh2KQRqrzdF5gwAbDTactbTrT5chBax1PtKk9DDbUbo1YbIcTwtPpksrOzg5+fH8RisebGhBiIKS8omupRFZZI9H6O0l7kKa6ATM0uwLdnktQ+RtP8LVk5B66rIu8+fYWY+5kqgypjlf/QJeDIepSKo1s/Rf3M13W1Yn0DsS+4C6751EeSuy9yBU4qVxQamrZfDtTVLzPkULu5NqUnhHCn9VfDOXPmYNasWdi5cyc8PCiFTUzDlBcUdfWoYu5nGux5dMVlOMrWhocRYQH44VySxjlh60/dw/pT95QGVcasJ6VLwBEU/Rv8Mx8iW+CM/zXuir1NuuFu1fIrUU01v8mQXw4MPdRu6k3pCSHa0ToA++abb3Dv3j34+vrCz88Pzs7OCvfHxsYarHOElGbKC4qqTIUu2/8YA5fhqNLBARep2QUYvTMWm/8Lqgw1J0kVXQIOv6gDAICV7d/HjuaRStt7OPORllOgNqtXlj5DrIb6cmCMoXZNOwYQQsxH6wCsb9++tNqRmI25LyiyoGY0x6DG2DRlkSKCffBx+wB8fzYJXMukfbb/hjzQNWb5D60DjsRE8GJjIbGzw+EG7VQuNHieW4zJe+MAKA6Vqgqy9B1iFUsYhI72mNE9EM9zi+DhIoDITfsvBzR3i5DKResAbMGCBUboBiHWIzxIhCpO/HLzt8whKj5NbcYmKj4V351J0ipb9yKvGOtP3oO/lxOn9rqW/9C0arP0puUAgF27AAA2ERFY8nEnTgsNZEOlH7cPKLewQbbgQdn7w3WIVV3wpkugRHO3CKk8OG9FlJeXh+nTp+O3335DcXExunbtim+++QZeXl7G7mOFQlsRWb+Y+5kY9P1Fc3dDgbKMjaYtadSp4sTHuoHNMGzrZY1td33UGmF1df8cUFfUVv66gqoBdeoAycnAnj3Ae+/JM1pp2flYfDgRz3OLtHpeTaU6NG3ZY8ztsUyx6TwhRDtm24po/vz52L59OyIjIzFw4EBER0djzJgxeneAEGtjiRXEZRmb0tsLaRpCVOdFXjFupb3k1FYi1rwlkDoRwT6YG9lQ6X2y13Vx50Fp8OXqCvTuDeD1cLRI6Kh18AVoVyetLGNvjyV7bX1DqiO0jicFX4RUQJyHIPfv348tW7Zg4MCBAIChQ4ciLCwMYrEYtra2RusgqZwsOQPAdfL43MiGSHmRjwNxTxQ23BY62CG7oMSgfVI2KV7fQPH8vYxyt/GYBE1S7+KZszueCL0BAJ/u+Qcv8l+/Pm1LVIglTGW5DNnrStmwRXpD//6Ak+LQqLEDYmXHN9X2WISQiotzAPbo0SO8+eab8n+3atUKdnZ2SElJQc2aNY3SOVI5GavulKG0CvDQOAfMR+iAEWEBsLXhYXZkkEIwGf8kG18e0VyfS1tlL/r6lmL4+8F/mR/G0OjpffRJPINeiWdR/WUGcuyd0Gf4GiR7VFcIvgDtS1RoCmbsxMXodP209B9DhwJQDNCfvSzU7QVypOx9pP0WCSH64hyAicVi2NvbKz7Yzg4lJYb9Jk8Mw5IzSOoYs+6UoUQnpGmcgN+nqbSPMfcz5b+DXk18YWvDQ1oO94uyk70t8ovEWk2il1309S2Z8apQgrapiVj8x1rUef5E4T63ojxsPrAE/YatQr69YoCibYkKTUFKh39j4V7wEgVe1eDQqZPSAN2GB86rPLlSV/aB9lskhOiLcwDGGMOIESMgEAjktxUUFGD06NEKtcD2799v2B4SrVl6BkkVfepOGSLg5HIMWR81+fZMEvb+/VghUJP9DkRu3C/Kn7SvjbXH73La21FGdtHXtg5YWbYSMZYeXAW/F2kosLPH8TqtcCioPRK8a2P/zmlo8OwBlv65DpN6TSu3ybUsG7cm+g7C6nqhhZ87rj7IUvreagpS3rop3Yv2eZ/+uJ6YrjRAVxV8afO+lX0coLrsA+23SAjRF+cAbPjw4eVuG/rfcACxHNaQQVJF13k1hgg4uRxDLGHYfj6J88T2slky2e9gw+BmELk5aMyEidwEGN+5HgJFrlhw8CbSctQPtSm76OtSB0ymV+IZ+L1IQ6ajGzp9/B1yHFzk943r+xl+3j0bbyWcRqxvA/zUorfSY8iq7JfNUJV+b7NyC2HDA9zyctAs5Taeungi0dsfjGcD18JchN+T7pVZdfSHKgN0mbLPIypVagJQXltLWYkKTWUfqGYXIURfnAOwbdu2GbMfxACMXbnc2HSZV2OIgJPLMQBotcG1MrLfweLDiZjXKwhjf1afmVrQpxEA4HbaK+QXq9+DUtVFX5c6YIB0sv3Yi/8DAGx54y2F4AsALtcMxtJOIzH35A+Ye/IHxFeri9gaylcyAqWCIsYgEBcj7+krzN8UjcwaYrw4eBQHkuPQOO0ebP7r6QsHF1yp0Qiv7B0hEBfjVe16uOHuh9TsNLX9ljDp4gcvV4FCpq1ZLXe1tbVmRDTUOoNKNbsIIfrQuhArsVzWvjJL23k1hgg4uRxj1v4byDJQ0VXZ78Dd2R6bhzbHZ/tvlMuUVXHi46v+jQEALb6I5lTwVdlFX91r0yT87iUEPnuIHHsn7GzWU2mbLS37olnKbfS6dRabfl+KOd3G4pqoHtJdX59b7nnZ6Hz/b3S9dwltH1yDa2GePMhS5r5HdVR79RxVCl7JM18A4PLhCKS/4jbZ3stVUG4jbmNtY0X7LRJCdEUBWAVi7SuztJ1XY4iAk8sxDBV8lZb+sgB9Q6ojPEiEi/9m/rfJN0NobS+0qeOJ6IQ0TtsdVXHiY8Og5mijpFaUznXAGMPYi78AAH5q0atc9kuOx8PMiE8RmJGMepmP8P3+L6Svzdkd10V14VqYh5ZPEmHLlGfvxDwbZDhXwQW/pjjr3wzn/EOQ4eIBW4kYjZ7eR+uH8Wjz6AZaVHNElY8/hnc2t6BGVSCvahsrfYewTb09Vtm5iurm1xFCLBcFYBWIta/M0nZeDddAMjohTekFUixhSmtdmULpifJhdb0UKslznegPSOeZ2djwlF5wdQ202yXHIST1LvLtBNjasq/atrkCJwx/dyE+Pb8HzVJuo17mI3jnZqHr/SvyNgneAYiu2xon67yBFDdvFPDtUWBnj2Ibu3KT9wFAbGOL6z71cd2nPr5v3R9fDwxBXy8vtPJgBp/4bm1zJrmsALWGBTeEEArAKpSKsDJLm3k1XAPJreeT0SrAQ2GoKCkjFz9dfKBTBXV9cPkdaJu5SsspUCh3IcuA6Bpoj/sv+7W7aXc8dxJqbJ/i5o1ZPSYAAByLCtAwPQmNn96D2MYWp2q3lBds1ZWyVZ2GmPhuqDmTpir5oipYLLu4wlKDR0KIIgrAKpCKsjKL67waWcCpKViRzePispLQFDT9DrTNXC3+46ZCpX1ZBiQ8SKSxDhiPB5TeDbb540SEPryBIhs7fNeqv1b9AIB8ewfE1miodkJ+aTb/PT/XLwyGnPhuiCFsU5V80WY+nzUsuCGEaLEXJLEOsguUSKiY/RAJHazqGzGXvfBkAacmsnlcugZfTvaqt9riAajmag93J77G4/go+R2IJeX3UdQ2c1U6+AJeZ0CiE9Lk74+qSzArc0WXZb/2BXdGmpviBtuGvIzz/vsZ9WaA0mOr+8IQEeyDczM7Y/eoNvh6YAh2j2qDczM7a31u6ztnUpaRKhvEKduXU1/aZkXV7WNJCLEMlAGrgCrTyqyIYB98GOaPLeeTDX5s2dyavCKx0vtl7+bCvsEAIC94qixLMblrPYzvXK9ciQhl2ZO5kUF6VbAvnQE5N7OzPGNkn/wvnjlVQa7Aqdxj3ApeYcbpH9Hl/hWIeTb4tvXbSo/LVdnMWlnC/1Z6RgT7aCwRoYwhJr7rM2fS1CVfdJ3PZ6kLbgghFIBVWKZemWVOXYNERgnANBUuLRskKBsaUzUcpW7y97ifYxHZxAd/XFedQXER2OFVoeptwEpnQCKCfdDtwh+wmf0xShydcDCoA7Y16oYbPvUAxtA34S/MObkFVfNeAAA2tHkHyR7VVR6bC3XBFwA48m0RHiQC8PoLw8X7mYj59xkA6bnbprZu5y/XOVn6zJk0dckXXefzWeqCG0IIBWCkAtB3z0NtCR3tML5TPXi5CiB0tIdYwmBrw+OcedSUPQGgMvhy4tuiZ2MRQmt7Yuqv1zX2Nf1lAXDmDGzGjQUA2OXnof/Vo+h/9ShuVKuDXHtHtHkUDwC461kTc7qNxaVajTm/F7oqG5xEJ6QpBK/rT93TaS6VNnOy9JkzaeqSL9qe49aw4IaQyo4CMGLxNGU01F1IjSE7vwRfHkmU/1vk5oBBrWrB38uJ03CvzvW5AOQVi/Fr7BNE3XzKqX3NF0+Bd94GSkqAd9/FufB38GzNevS4fR6Nn94HABTY2eObtgPxfat+KLbVPJfNUNJfFkAsYVh/8i7WHL9b7n5tV/PpUlJC10n9pi75os05bk0LbgipzHiMaRosIIaUk5MDoVCI7OxsuLm5mbs7Fo9rRkN2If/2zL8q52yZiqbMze9xTzBxT5zR+1HHQYLj+2aDFx8P1qIFLm8/gLNPcrH+1H2452Xj7fgT8M9Kxbet38ajKiKj96esSV3qYc+VR2r3xJRlcs7N7KyxFES7ZSdVBraajlM6yPdyEQAMeJZbqDaL2W7ZSY3Dl5r6rS2qA0aI+Rj6+k0BmIlRAMadqoyG7HImy2gouyiZU9n+AYoX+GcvC7H4cKLqAxiAjUSM42e/Ru2LJ1FQtRreHfk1rkNFRXsDkr12oRMf2XnFBstG7h7VRu1cqpj7mRj0/UW9j6PNEKbs/ASUD18aa9UxVcInxDwMff2mIUhikcQShgUHb2pcZSaRAON+Lh+kmVPZVXB/xqdhzu/xCkVfy2Yt9BGSchtbf10IO3EJXgmc8PK/VY61nz2E2F6AgREzTBJ8Aa+H7gAYdEhY01wqQ8zJ0nYI01ybcStbYFNZFtwQUpFQAEYs0vqT99TW7ZKtMpvze7xFBV8ysv5N3POP0gn1hgq+AGDoP0fgkZ8DAHArygNevr5v0VtTEOcbaLgnU2F8pzoIq1tVIfuyaWhzgxW/1TSXSt85WbqWlahMJV8IIYZFARgxOm23aomKT8Wa43c4HdvUWwlpS10pCUBzvSxNbCVidP5v38UJvafjX4/qcC3Mg0tRHuq0aYIfM8vX/DKGetVcy2VhIoJ94OrAx5AfLul8XK6r+fTdhkufshKVqeQLIcRwKAAjRqXtVi3abERdETAGtK/nhTN3n+n0+BZPEuGRn4MsB1ccbtAOYpvXVfujMw3VS81UZZaevdI/+8VlNZ++23CZuqyEKZlqr0pCiHYoACNGo0tZAG1KNHg488ttw2ONrj1+AXcnPl69zMOG35ehQXqSwv35fAHmdBuLKzWDyz02/K504vnJOi0Vgi9T0ZRZ0qcMg8hNgAV9GnGeS6XPnCxTl5UwFVPtVUkI0R4FYMQodJ1To02G4Yu+wVh8ONFkBViNJTu/BBGNqqHGtr3odlf5Sr4Zp3/CO0OXK97IGMLvSof3ouu1MVr/VE2k55JZ4rphelmTu9bH+M51tc7U6Doni0uhU3cnvlUVNtXlCxAhxHRoM25iFNrMqSmNa4Zhctd66NnEV+Nm09bCKy8bE87vAQAs6zAc/YauRL+hKzHkvS9QbGOLN54koFHaPYXH1Hv2EP4vUlFoy8eZgOZG65uqgKSKEx+bhjZHeJCo3IbiMrY2PPRpyv0ib8MDPmkfgIld62ms+6XuOTVt5F6WbAhTXSCflVeM6IQ0zq/FnLjstrDwUILC+0YIMS3KgBGj0HVODZdMhI/QAeM71wPwethpwcEEtQU9Ld2Ag9/DrSgP8dXqYHPrt8F4r78bHQlsh76JpzHi6h+YHjlJfnv4PWn267xfU+TZO5q6yxDY2UAiQbkCqKWHuMQShoPX1C9EKE3CgO/OJKFZLXeV2RljDauFB4lQxYmPF3nKh7UNvcG2MZl6r0pCiPYoA0aMgmsmK/lZnsK/ZZkIoHxWi/ffT9khr4hgH6x6p6kevTUfHoB2eSloGvU/AMDCLqMUgi8A+LFFLwBAn8TT8MjLlt8uG648ZsThR3XScgox9ufYchd62RBXVHyqztsuqcrOyIbV1D2nri4nPVcZfAGqs7aWqCIvKiCkoqAAjBiFLJOlKU+w9vidchdNWVZLJFQM4kRCB5XzVp7l6r/aztR4AMAYvr70I3gSCU4Et1c60T7WtwGuiepBIC7GwGt/AgC8X2YiJPUOJODhRN3Wuj2/kZI4pYe4Ul/k6/R4ZYGOsYfVKlLQUlEXFRBSkVAARoyCy5waGWUXzYhgH5yb2Rm7R7XB1wNDsHtUG5yb2VnlEJOXi8AAvTYtd2c+vrJLgufl85DYCxA/8XMASuaz8XjY3qI3AGBY7GHYiUvQ9f5lAECcb31kuLhr9bweznxsHtoctxf3wFshvvq+DKVkQdQ/j7J0PkbZQEfXeYVcVaSgRdMXIB6kw7bWtKiAkIqGAjBiNBHBPpjctZ7aNuoumlwnU0fFp2LqL3EG6LFpvcrOQ+uNSwEAG1v0xZp7yoe/bHjA4QZvIsOpCnxeZaL7nRh0uyMdfoyu1wbVXO3h7sTn/Lxze0lLO9jb2eCdFjX1fyFq6DPFu2ygw3UCvK4ZqooUtGgayge41VcjhBgPBWDEqPy9nDm102YCfekVcF8fv4PRO2PVbncju8T0biLi/BymMPLv3+H/IhXpzu7Y1HqA/HZZ0PJhmD8md60HVwc7FNnx8XNIBADgk8v7EPrwGgAgum4bDG7thyw1c5fKErlJA5uo+FRM/d81rfutzSU7wJPb77/s8csGOmIJw29xKZwe/+xlodLVkZpUtKBFl6F8Qojp0CpIYlRch2sW/3ETjnwbjRcFZSvgNBEJHTA3siEWH07k/Bhja5j+Lyaf2wkAWN5hOHIF5bcM2nPlEXKLxPJ/7wrpgbEX/4cm/5WjeOBZHdMm9kGhmHuQIQtsouJTMXpnrNq2svpfZVcGln4/NW39MyzUHz+cS+L8+5I9Z49gaS0vWQ2vy0nPOW07xeNB4fes7epIc22wbSy0VyUhlosCMGJUXMpKAMDz3GKNxSFVFZbUZOWAprCx4em0Gs8YBMWF+PrgSgjEJYiu2xq/BndR2q508AUA6a6eOBoYhj6JZwAA54LaYmAjH63mPOUXi/FnfBpm/3ZDY9tq/1WiV3UBt7Hhadz6x97ORr5FEJffm2xvzK3nk7H1fLI8gCoskXB6fWX31dSl6GhFC1por0pCLBMNQRKjUjeso4yqVWzqVsBpcuH+MxzVozyBoc36axvqZz5EurM7ZvaYoNVyRNlkfAA4UKulPEvEZcUpAGTnFWPsz7Fqyy3IrHo3BBHBPirn4nEd4pK18ynTzkfogI2Dm2P3qDb4MMwfgLQOWGmyAKpsuRKudF0dqUsxV0II0QZlwIjRyS7Asw/Eqx1GUlcc8uK/mTpnsDb8dV+nxxlDx/tXMCL2DwDAtJ6T8NxJqNXjY30bYGuLPnApykOsbwNEJ6QhtI6nyo2oy9ImgOWykTbXbJG6dmIJwxQViyhk21btufIQIjcBnuYUah2EU9FRQoglogwYMTqxhEHoaI/IxtwmwZddxRYVn4pxu9TPV7IGXrlZWHHkawDAlpZ9caZ2C+0PwuNhUdePMaPnJEhsbPF7XArEEqYyG6UPrvP3VGWLlG0XVCKW4NStdGw7/y+2nktCUYmEc3mJQa1qAdB92ylrqN9FCKk8KANGjEqXSfOlL/y6zvsyJWH+SyyL+gZ/1gvFgeDOyhsxhuVHvkbVvBdIrOqP5R2GG+S5M3OL5JkdWZZpTfQdrD91T/OD1ajiqN/G01x+78cS0rHkaCI6BVbldEx/L2elE+Q9nPl4nqt5SNUa6ncRQioPCsCI0WgbPMlWzsku/PrM+zKlgdf/RMSdGHS9ewkPqvggtkbDcm0+vrwfnf/9G4W2fEzsPQ2FdvYGe/7z9zLkw3m2NjyE1fXSOwD7IMyf07wnsYSVG1aMTkjj/HtnDDh5K4NTn569LMSIsIByQ5kt/NzRYcUpjSsyDVG/S9nrpflhhBBdUABGjELb4ElZnSVd9xE0tR63zwMA7JgE3xxajsgR3yDb0VV+f+iD65h5+kcAwKIuo3Cnqr9Bn3/9qfvYF/tEXiaB68pTVZwFtvLNztVRluUSuTmgoERslKB58eFE/HAuSWk5CFVz4AxZv8tYm4ATQionmgNGjELb4ElZcUhrmLNTI/spQlLvQsyzwSNhNdTIycDyo1/L6yFUe/kM3xxcDlsmwa/BXbArpIdR+lF6M+rSK091YceTLnpQV8xU5abYOQWcVliqoilEUrXptrGLjhpzE3BCSOVEGTBiFFyDp/dD/dAj2Act/NxxJfk5Vv55GwBDaG0veDlb/v6OEf9lvy7XbIQvOn2I/Tunofvdixge+wd+DonAxt++QtW8F0jwDsCcbmOMtgO2bLXgwkMJCA8SISLYBx+3D8C3Z5K0PlZ2gRhDfrgk/7fQkY/wht4Iq1cVIjfpkJ8xhoY71vfC7aevNE7IL/06S2e1jFW/S9Mm4Kr6Qwgh6lAARtTSdc4L1wnPPYJ9kJ1fhFZLjitkTtafuo8qjvxyVdgtTc//ArAjgWG4KaqLpR1HYsGJ7zD71BaEPbiGFim3kCNwxui3ZqOAb9xJ4LLVgmuibyO0jhd+57h1jybZ+cX4NfYJfo19AoD7pHdtvVmvKraMaIXt55PU7lqgrqyEMYqOarMJOJW5IIRwRQEYUUmfOS+a5iHJJkZn5RZi7M//KD3Gi3zLDbwAwCcnA81TbkMCHqLqtwUgLZTa9uF1dLt7Ed3uSjfMntRrKh66m26O0PpT97H+lPFqnxkj+LLhAcNCpRP/vVy5ZT5NNUTN9XmsYcicEGI5aA4YUUrfOS9c5iHNjQzCoj8sZ39GbfW4fQEAcKVGEDJc/lthx+Nheo+JeOwmLa2wLvQ9nKzbylxdtBqj3gyAvZ3044hr9tRUZSUsrT+EkIqBAjBSjqY5LwC3rV1k85DKjlja8IBODari5K10pOVYb9ZAtvrxaGCYwu3Zjq546/3VGDFgPla/OcQcXbMaPB7wSfsAzOr5OljXtLUSD683FTcFS+sPIaRioCFIUo6h5rxExacqnQQu0aL2k7k1e3ILi6M3YU+TbtjZPFJ+e7WXz/DGkwQAkA8/lvbM2R1/1XnDZP20VHMjG8LLVSCv13Xp30zsi32MvKISvOHvieFt/eWZLxlZ9tTYZSW4srT+EEIqBgrASDm6znkpPWHfy0WAmfuuG6N7JuNamIt1B5ehRk4GvojehEI7Pv7XpBsAoPudGADAVd8GSHPzMmc3LZJsjt+IsACFwOTN+lXxZn3Nle9lZSXK1RkzU90tS+sPIcT6UQBGytFlzosuWw5ZuvnHv0ONnAzk2wngWFKIr6LWI9vBFcfqh75e/dignZl7aT7O9rbILRKXu91QWSFjlZWoKP0hhFg3CsBIOVxXMMrmvFjDfo3airh9HgPiT0DMs8Gw9xbhnevH8d6NaKw7uBxTIqeg1aObAICjgeWHHy0JD9LK9q8KywdK+pIFXzY86bCyjCGzQsYoK6EPS+sPIcR60SR8Uk7pFYxlv9uXzW5Yy36N2qj66jmW/LkBALC59dv4u0YjzI4Yj+i6rSEQF2PDwWWwAUOcT32kuHmbubeaLX+7CZzsbTm1DQ/S/vX8V/QfI8P8sXtUG5yb2ZmG5AghRAMKwEg5YgmD0NEeH4T5w91ZcdNoD2fp7UJHe/mcL1MPO3bTIUjgjDEsP/o1PPJzEF+tDta2GwwAENvYYnyfGbhUM1je9EiZ1Y+Wxue/bXh6NvFFT44BkchN+1IKsmrwR+PTaEiOEEI4oiFIokDZXC4PZz6a1ayCfx69QGZuEbaeT8bW88nwETqgZ7DI5H08c/eZ0Y49NO4oOv17FYW2fEzqNRXFtnz5fYV8AT56ey527pmDgOdPcLBhB6P1Q1+Tu9bD+M715MFQWF1P/Br7WOPjQmpUwXFhutYbeVM1eEII0Q4FYERO1Vyu57nFOKGkbERadgG2nE82Sd9KKyiWGOW4/eJPYv7xbwEAX3UcgXtetcq1eSlwRr9hK2EvLua0tZCzvQ1yi4zTX2VU7VQgEjpyeryvu5PKkgtcUDV4QgjhxqxDkEuXLsUbb7wBV1dXeHt746233sLt27cV2jDGsGDBAvj6+sLR0REdO3bEzZs3FdoUFhbi008/hZeXF5ydndGnTx88fqz4bT8rKwvDhg2DUCiEUCjEsGHD8OLFC4U2Dx8+RO/eveHs7AwvLy9MmDABRUVFCm1u3LiBDh06wNHREdWrV8eiRYvAmPXPgNJlLpesrQ2v/FwxS+OVm4XPT/6AKWd2oEp+juKdjGHMxf9hzeHV4EvE+C2oA7a36K3yWBIbW877Og58o3wQZ0yf92iodP6VbGGFOrJiorKSCyIN7ZWhavCEEMKNWQOw06dPY9y4cbh48SKio6NRUlKCbt26ITc3V95m+fLlWL16NdavX48rV65AJBIhPDwcL1++lLeZNGkSDhw4gD179uDcuXN49eoVevXqBbH49cqvwYMHIy4uDlFRUYiKikJcXByGDRsmv18sFiMyMhK5ubk4d+4c9uzZg3379mHq1KnyNjk5OQgPD4evry+uXLmCdevWYeXKlVi9erWR3ynj02cul4S9ngdkaQTFhRgb8wv++u5jjLryGybE7MXZzR9i8tldcC3MhY1EjIXHN2Pm6R8BAN+90Q+Te00F4+n3p2HDAyZ2qYuU7HxDvAzO5h2KV7pDgWxhhbrf0dzIhvIhy4hgH5yb2Rm7PmqNKo58NY+SomrwhBCiHR6zoPRNRkYGvL29cfr0abRv3x6MMfj6+mLSpEmYOXMmAGm2q1q1ali2bBk++eQTZGdno2rVqtixYwfee+89AEBKSgpq1qyJI0eOoHv37khMTERQUBAuXryI1q1bAwAuXryI0NBQ3Lp1C4GBgTh69Ch69eqFR48ewdfXFwCwZ88ejBgxAunp6XBzc8OmTZswa9YsPH36FAKBdMPgr776CuvWrcPjx4/B42kOQXJyciAUCpGdnQ03NzdjvI06+T3uCSbuidP58SPD/HE0Ps1i6oDxmAS9E89g5l8/ovpL6fDpNVE92DIJgp9KN6p+4eCCO1610OpxAiTg4YvOH2HrG33N2W2D2D2qjcp5WOrqtSkbvoy5n4lB31/k9Lybhzan1Y+EkArL0Ndvi1oFmZ2dDQDw8JB+i05KSkJaWhq6desmbyMQCNChQwdcuCDdCPnq1asoLi5WaOPr64vg4GB5m5iYGAiFQnnwBQBt2rSBUChUaBMcHCwPvgCge/fuKCwsxNWrV+VtOnToIA++ZG1SUlKQnJxsyLfC5PQdOgoPEuHczM4Y16mOgXqkn6lnd+KbQytR/WUGnrhWxcReU/HW+6vQe/gajH5rFu561kSVgldo9TgBhbZ2+LTPjAoRfAHq52FFBPtgbqTyTdKVbbTOdU7XyDB/Cr4IIUQLFjMJnzGGKVOmoF27dggOli71T0tLAwBUq1ZNoW21atXw4MEDeRt7e3u4u7uXayN7fFpaGry9y5cu8Pb2VmhT9nnc3d1hb2+v0Mbf37/c88juCwgIKPcchYWFKCwslP87JyenXBtLoKn4qjqyoSdbGx48nOw1P8DIquTnYOTfvwMAvgl9DxtC30Uh/3XQHBUYhmP12qBP4hl0vxODrS374Eqp8hLWTl0wLZYwLD6coPQ+2TDywkMJCA8SwdaGxzkwDw8y/WpYQgixZhaTARs/fjyuX7+O3bt3l7uv7NAeY0zjcF/ZNsraG6KNbARXVX+WLl0qn/gvFApRs2ZNtf02F3XFVzXp09RHPnfIw9n8AdjQf47AqbgQN71rY/WbQxWCLxmJjS1+a9QJY/rNrlDBl6Z5WNpstA68DsxVnRM094sQQnRjEQHYp59+ioMHD+LUqVOoUaOG/HaRSPqtWpaBkklPT5dnnkQiEYqKipCVlaW2zdOnT8s9b0ZGhkKbss+TlZWF4uJitW3S09MBlM/SycyaNQvZ2dnyn0ePHql5J8xL1eo3TXU1D15LlU/85lruwFgEJUUYfvUPAMC3rfsDHOblVSSa9l/UdqN1bXZFIIQQwp1ZAzDGGMaPH4/9+/fj5MmT5YbwAgICIBKJEB0dLb+tqKgIp0+fRtu20j34WrRoAT6fr9AmNTUV8fHx8jahoaHIzs7G5cuX5W0uXbqE7OxshTbx8fFITX09/+XYsWMQCARo0aKFvM2ZM2cUSlMcO3YMvr6+5YYmZQQCAdzc3BR+LJls9dvuUW3w9cAQzI1sCCWL6hQoy5iYS//4k6ia9wKP3ariSGDl2Sjb3YnPaRK8LhutqwrMRf9V2qe5X4QQoj2zzgEbN24cfv75Z/z+++9wdXWVZ5eEQiEcHR3B4/EwadIkLFmyBPXq1UO9evWwZMkSODk5YfDgwfK2H374IaZOnQpPT094eHhg2rRpaNy4Mbp27QoAaNiwISIiIjBq1Ch8+6200ObHH3+MXr16ITAwEADQrVs3BAUFYdiwYVixYgWeP3+OadOmYdSoUfKgafDgwVi4cCFGjBiB2bNn4+7du1iyZAnmzZvHaQWktSi94fDvcU84PaZsxmT0zlij9U8VHpPgoysHAABbW76FEluLmeJoNM4CW4xqF4BPu9TnlIXSdqN1mYhgH4QHiXA56TnSXxbA29WBth0ihBA9mDUDtmnTJmRnZ6Njx47w8fGR/+zdu1feZsaMGZg0aRLGjh2Lli1b4smTJzh27BhcXV3lbdasWYO33noL7777LsLCwuDk5IRDhw7B1vb1BsS7du1C48aN0a1bN3Tr1g1NmjTBjh075Pfb2tri8OHDcHBwQFhYGN5991289dZbWLlypbyNUChEdHQ0Hj9+jJYtW2Ls2LGYMmUKpkyZYuR3ynx0zZhM7lrPWF2CQ3EB6mQ+er0L9H/C715CnedPkC1wxp6m3VQ8umLJKxTj6xP3EJ2Qprkx9BtSlAXmfUOqI7SOJwVfhBCiB4uqA1YZWGodMFXEEoZ2y05qzJicm9lZfkEWSxjWRN/B+lP3jNKn7/ctQvi9yzhavy0+7z4Oz52EAIBfd05HyyeJ2NDmHazoMNwoz22JlP0ONFFWD0zVNkaEEEIMf/2u+GM0RC+yjImyvQGVZUzUFfo0hDqZjxB+TzqXr8edC3jj8U3M7j4ez5yqoOWTRBTa2qndRqgi0mUj7IhgH3RuUA07YpLx4Hke/DycMCzUH/Z2FrEuhxBCKjwKwIhGsknYZQMrUZmMiarNvA1pyD9HAQBXfRvAuSgfDZ49wHcHvkSGUxUAwIFGnZHhUjlLImizEbayQPmHc0mUASOEEBOhAIxwomkStljCsOCgdpt5a8uxqAAD4k8AAL4JG4SYWk0w+dwufHx5P6rmvQAAfP9GPyP2wLJxna+nKlCWVcKnlY2EEGJ8FIARzkqvjixr/cm7SMsx7j6QvRPPwK0wFw+qiHAmoBkYzwbLOo5AdN3WmHnmR1yu0Qj3vSyz0K0pZOUWaWwjljAsPKQ8UFZWCZ8QQohxUABG9BYVn4o1x+8a90kYw7B/DgMAdoX0AOO9nqsUW6Mh3hv8lXGf38hcHWzxskCs1zEWH05A92D1gZM2lfC5zifTRCxhVL6CEELKoACMaK30BdXLWYAFB5XvLaiMwI6HwhLtByqbpt5B46f3UWjLx/8ad9X68ZbM09kes3s2xNT/XdPrOFwCJ20r4euLVlsSQohyFIARrei7ylGX4AsAhv1zBADwR4N2yPqv7ERF8WW/YAgdDbOHpqbASZe6brqiuWaEEKIarTknnMkuqMYqMaGKMP8let06CwDY2SzSpM9tbAOaV0dEsI/GTa+50hQ4mWpzbU1zzQDpXDOxpn2uCCGkgqIAjKglljDE3M/EgX+eYPaBG3qtcvTKzUL17HStHzfgxnE4lBQhvlod/OMbqEcPLE9YXS8A6ivUc8E1cDLV5trazDUjhJDKiIYgSTmyOV7HE9JwIO4JnucW631MO3EJfv9xCqrmZmFa5GQcDOrA6XE8JsHQOOnw486QHkAF2nMTAERCR/n/q6q35u7EBwPwIk/570HbwIlrXTd9mHquGSGEWBsKwIgCY1Wyb/0oHtVfZgAAvjm0Ah552djeso/Gx310+TcEZKUix94Jvwd1NGifzE1ZxkpVvTVAmlWKTkjDb3EpeF6q5IQugZOxN9c25VwzQgixRhSAETljVrLvcfs8ACDd2R3euVlYcOI7eOZlY9WbQ1VmtT66vB+f/7UVALCh7bvIt684F2seNG96XVZoHU+E1vHE55FBBgmc1NV105dsrpmmPUT1nWtGCCHWiuaAEQDqJ03ry0YiRve7MQCA6T0nYeWbQwEAn8bsxdKodbCVlK9/9fGlfZhzShp8fd12IL5t9bYRemYeHs58zisAZXPwfo97gpj7mRBLmDxw6htSHaF1PC2yppap5poRQoi1ogwYAaB50rQ+mqfcQtXcF8gROOOCXxOcrt0CmU5V8MWxjRh0/Ri63L+C6HqtcaxeKGJqNcEHV3/HrL+2AwDWhg3C2nZDjNIvc/B0tkfMrC6cNr229hpapphrRggh1ooCMALAuJOhI25fAAAcr9sKxbZ8AMDukAg8d3LDsqPfwDs3C0PiojAkLgq5fAc4F0v7siZsML5uN9ho/TKHAS2qcw6+KkINLWPPNSOEEGtFARgBoN1kaB4AD2d7zIlsiOe5RVh8OFF1Y8YQcUc6/BhVv63CXX/Wb4tTtd9Am4fX0e3uRYTfu4Rqr6RlCVa1G4J1YYO0fh2W7rszSWhWy11t8FTR9ms05lwzQgixVhSAEQCaJ02XxgAMa+OHPiHVAQA/nEtS+bjgp/dRIycdeXwBzgQ0K3d/kR0fZ2q3wJnaLTC32xg0Tb0Lh5JCXKzVRO/XZKk0BU/m2K+REEKIadEkfAJA+0Kga0/cRYsvohGdkKb2cbLVj6dqt0QBX32WjfFsEOcbWKGDLy4FSKmGFiGEVHwUgBG58CARJnWtD6Ejn1P7F3nFGL0zFgDwcfuA8hEYY4i4I53/9WeZ4Udr4Mi3waed6mDXh60xMswfLgLNCeNmNatwOra64IlqaBFCSMVHQ5CVhKy6vaqJ0MpW3Akd+SgWi5FXJFF77Fn7byBLSZX2es8eos7zJyi0tcPJOm8Y7sWYSH6xBL/GPkGj6kJ8HhmEw9dT8KpQ/WP+efSC07HVBU/a1tDS9LslhBBieSgAqwQ0lTNQteIuO5/bFkTKgi8A8uzXWf9meCVw0qnv5iZbdTipa308fVmk+QGQ1pVlKibScSlAKhsOHrMzFjxA4fdStoaWtZeqIISQyooCsIpi+HCguHwglPIiH4XJWfhMyUMKdwBPanviuGdzMJ8Qg3ephxUPP8rIVh1uu5DE/TH/RUyagid1uNTQqiilKgghpDLiMabquzoxhpycHAiFQmRnZ8PNzc1wB3ZwAAo1jI+p8XXbQVjbbhAYzzDTAmtlpeLMd6NQwrNBy0934oWjAV+rFfgwzB9H4tP0zkypGl4USxjaLTupcrWkLNN2bmZnGo4khBADMPT1mzJgFcWKFYBYcUufpGe52BHzQO3D6j17gEHXj2Hihd0IfJaMKZFTkGfvqHd3Im+fAwBcrNW4wgRfQgc7ZBeUcGrbNUiE2QbYs1FVDS0qVUEIIdaNArCK4tNPy910Pe4JtpbEaXzo1RpB+PLP9Yi4EwO/rOkY9fZcPBZW07krthIxhvxzBADwe1AHnY9jaUa2C8Ca43c1tvMRvg62jBX8UKkKQgixblSGogLjWqYgqmUEBg1cigznKmiYkYzD2yZgzaGVGBQXhTqZj1TPKFch4vYF1MjJwDMnIQ4GddSh55bH09ke4zvXw+ahzeFkb6uyHQ+m2WSaSlUQQoh1owCsApOVM1AVCvAAVHHi41VhCWJrNESf99fgRrU6EBbmol/CX1j653qc+GEM/l4/FDP/2g47Mbfht4+u/AYA2NmsJwrt7A3yWsytb4gvbG14iAj2wY0F3TGpSz24CBQDMR+hg8kmvnP53fpoWG1JCCHEfGgSvokZbRK+CrKVcgCU1pRyEdjhVeHrwMpOXIJWj+LR+tFNvPH4Jpqn3IJDibT8wqUajTDurc/wzNld5fM1f5KI/Tuno9DWDmFjtqlta012j2pTbjix7AT5Fn7uuPogy2T1uFT9bmXPSKsgCSHEcAx9/aYAzMRMHYAByuuAcWVfUozwuxexNGod3IrykOriidH9ZuOab6DS9ut/+wq9bp/D3sbhmNlzor5dtwg+HFYTmqseF9UBI4QQ06AAzMqZIwADpNma9SfvYc3xOzo9vnbmY3y3/wvUff4YhbZ2mNNtLP7XpJtCmxrZT3H621GwZRJ0H7ket6v6G6Dn5sM1k6SqHpepMlFUCZ8QQozP0NdvmgNWiWhTTLSsfz1r4K33V+PPem0gEJdgxdFvsPzIWjgV5cvbDL96CLZMgjP+zaw++AKkdbQ0BU9iCcPCQwlKh3dlty08lACxxHjfc2SrLfuGVEdoHU8KvgghxApQAFZJrD95Dy9UbBnE1SuBE0b3m42Vbw6FmGeDd28cx6EfJyPo6b9wKczDe9eOAQC2tuxriC6b1dzIhjg3s7PGzJU29bgIIYQQGaoDVgmIJQzbzuue/SqN8Wywvu1AXKnRCGsPrUSd549xYMcUXKzVBG5FebjrWROnazc3yHOZk5ergFMmydrrcYnFYhQr2cKKEEvB5/Nha6u69Ash1ooCsErgctJzvOC4sTZXl2o1Ro+R67D86DfodvciOiRJV+NtbdnXYNsZmZOh62xZWj0uxhjS0tLw4sULc3eFEI2qVKkCkUgEHo+G10nFQQFYJWCs7MsLRzd83O9zDPvnMOac3IKnLh7Y36iTUZ7LVGR7KHKtnyWrx5WWXaB0Hpi2xzMVWfDl7e0NJycnurARi8QYQ15eHtLT0wEAPj60spdUHBSAVQJcsy8t/Krg6oMX2h2cx8OO5r3w+38V7wv5Au0eb0FkIYg2lextbXiY3zsIY3bGggfl9bhMURlfG2KxWB58eXrSPpHEsjk6SvemTU9Ph7e3Nw1HkgrD+seKiEaaqqYDgLsTH7980haftA+ALrFCjoMLchxcdO6jJeCy6lGZiGAfbBraHCKhYqCr6/GMTTbny8nJycw9IYQb2blK8xVJRUIZsEpAXZYGkGZqlvZvDFsbHmb1DEJw9Sr4dPc/ZuipafkIHbByQFM8yy3Uu35WRLAPwoNEVlWPi4YdibWgc5VURJQBqyRUZWnK7l8oljAsOZJoji6alGzT7LB6Xgarn0X1uAiPx8Nvv/1m8ON27NgRkyZNMvhxCSHmQxmwSoRLlkZTXauKwN2Jj6X9G1vc0CDh7sKFC3jzzTcRHh6OqKgorR7r7++PSZMmmSWgGTFiBH788UcAgJ2dHWrWrIn+/ftj4cKFcHZ2Vvm4/fv3g8/nm6qbhBAToACskpFlaVSx1HpVhlDFkY8PwvwxvnM9yk5Zua1bt+LTTz/FDz/8gIcPH6JWrVrm7hJnERER2LZtG4qLi3H27Fl89NFHyM3NxaZNm8q1LS4uBp/Ph4eHZa2iJYToj4YgKyGxhCHmfiZ+j3uCmPuZCtvkWFq9Kq7KhlOyf0/uWg9fDwzB7lFtcHVuOCZ2rU/BlwGpO5eMJTc3F7/88gvGjBmDXr16Yfv27eXaHDx4EC1btoSDgwO8vLzQv39/ANKhvAcPHmDy5Mng8XjyuUULFixASEiIwjHWrl0Lf39/+b+vXLmC8PBweHl5QSgUokOHDoiNjdW6/wKBACKRCDVr1sTgwYMxZMgQ+bClrB9bt25F7dq1IRAIwBgrNwRZWFiIGTNmoGbNmhAIBKhXrx62bNkivz8hIQE9e/aEi4sLqlWrhmHDhuHZs2fy+3/99Vc0btwYjo6O8PT0RNeuXZGbm6v1ayGE6I4yYJXMkespmPN7PJ7nvl5N5CN0wPzeQYgI9tFY18oSuTvxIbCzQVpOofw2UanXRIwjKj4VCw8lKAxZ+5jgfd+7dy8CAwMRGBiIoUOH4tNPP8XcuXPlwdThw4fRv39/fP7559ixYweKiopw+PBhANKhvKZNm+Ljjz/GqFGjtHrely9fYvjw4fjmm28AAKtWrULPnj1x9+5duLq66vx6HB0dFVb33bt3D7/88gv27dunsuTC+++/j5iYGHzzzTdo2rQpkpKS5AFWamoqOnTogFGjRmH16tXIz8/HzJkz8e677+LkyZNITU3FoEGDsHz5cvTr1w8vX77E2bNnwZi1/MUTUjFQAFaJLD2SgG/PlN+SKDW7AGN2xson46tbMWmJsvKKseuj1rDh8Qy+AlEsYVa1stFUouJTMWZnbLnzI63MuWQMW7ZswdChQwFIh/NevXqFEydOoGvXrgCAL7/8EgMHDsTChQvlj2natCkAwMPDA7a2tnB1dYVIJNLqeTt37qzw72+//Rbu7u44ffo0evXqpdNruXz5Mn7++Wd06dJFfltRURF27NiBqlWrKn3MnTt38MsvvyA6Olr+mmvXri2/f9OmTWjevDmWLFkiv23r1q2oWbMm7ty5g1evXqGkpAT9+/eHn58fAKBx48Y69Z8QojsKwCqJI9dTlQZfMgzAwkMJCA8SyVdMls1uWLJnrwrRN6S6QY9prgyPpRNLGBYeSlAanDNIh39l55Khg9Xbt2/j8uXL2L9/PwDpRPb33nsPW7dulQcjcXFxWme3uEhPT8e8efNw8uRJPH36FGKxGHl5eXj48KFWx/njjz/g4uKCkpISFBcXo2/fvli3bp38fj8/P5XBFyB9fba2tujQoYPS+69evYpTp07BxaV8Xb779++jW7du6NKlCxo3bozu3bujW7duGDBgANzd3bV6HYQQ/VAAVgmIJQxzfo/X2C41uwCXk54jtI6nfMXkymO3sOmvf03QS/0Yeu6aOTM8lk7TSlkGxXPJkLZs2YKSkhJUr/462GaMgc/nIysrC+7u7vLK6dqwsbEpNwRXtujniBEjkJGRgbVr18LPzw8CgQChoaEoKirS6rk6deqETZs2gc/nw9fXt9zqRnWrIQFofH0SiQS9e/fGsmXLyt3n4+MDW1tbREdH48KFCzh27BjWrVuHzz//HJcuXUJAQIBWr4UQojuahF8BaJoIfTnpOZ7ncrtIlF4FaWvDQ/t63gbtq6HxIM1KGXKvRU0ZHkCa4THFhHNLxHWlrKFX1JaUlOCnn37CqlWrEBcXJ/+5du0a/Pz8sGvXLgBAkyZNcOLECZXHsbe3h1gsVritatWqSEtLUwjC4uLiFNqcPXsWEyZMQM+ePdGoUSMIBAKFie1cOTs7o27duvDz89OptETjxo0hkUhw+vRppfc3b94cN2/ehL+/P+rWravwIwvueDwewsLCsHDhQvzzzz+wt7fHgQMHtO4LIUR3lAGzclyGybS5EJbNJMkm5ZtzKFI2F81Uey2aM8NjDbhmGw2dlfzjjz+QlZWFDz/8EEKhUOG+AQMGYMuWLRg/fjzmz5+PLl26oE6dOhg4cCBKSkpw9OhRzJgxA4C0DtiZM2cwcOBACAQCeHl5oWPHjsjIyMDy5csxYMAAREVF4ejRo3Bzc5M/R926dbFjxw60bNkSOTk5mD59uk7ZNn35+/tj+PDhGDlypHwS/oMHD5Ceno53330X48aNw/fff49BgwZh+vTp8PLywr1797Bnzx58//33+Pvvv3HixAl069YN3t7euHTpEjIyMtCwYUOTvxZCKjPKgFkx2TBZ2WBBNkwWFZ8KgPuF0MOZXy6TJNvGyJzTzjcMbobNJtxr0VwZHmuhaW9RY2QlAenwY9euXcsFXwDw9ttvIy4uDrGxsejYsSP+97//4eDBgwgJCUHnzp1x6dIledtFixYhOTkZderUkc+1atiwITZu3IgNGzagadOmuHz5MqZNm6bwHFu3bkVWVhaaNWuGYcOGYcKECfD2Nk+GeNOmTRgwYADGjh2LBg0aYNSoUfIyEr6+vjh//jzEYjG6d++O4OBgTJw4EUKhEDY2NnBzc8OZM2fQs2dP1K9fH3PmzMGqVavQo0cPs7wWQiorHqO1xyaVk5MDoVCI7OxshW/X2hJLGNotO6kyU8ODNEA5N1O6cktdW5mNg5uhZxNfpfcpy7QZmg0PKD2qVzaTZ6oViTH3MzHo+4sa2+0e1cYqM2AFBQVISkpCQEAAHBx0y1LJgn9AeVayMs+RI4ZniHOWEH0Z6votQ0OQVkrbYTJZaQlV0fYn7QNUBl/A622Mtp9PwuLD+u8V6SN0wNzIILg728sDqhZ+7rj6IEtlgKWpir+haKqFJgtuDZ3hsSaqVspS/TVCCOGGAjArpe0wmaoLpqezPRb3DUbPJpovmLY2PIwIC8C6U/fwIq9YZTtnextsHtoSz3KL8PxVITyc7eHt5gAw4FluodrslSVklGTDrspqoRlr3pk14rK3KCGEEOUoALNSukyENtUF097OFm3reln1hZgyPNyYKitJCCEVDQVgVkrXYTJ9L5iXk56rzX4B0sr0xlghaOqq9JThIYQQYiwUgFkpcw2TmWuFoLmq0lOGhxBCiDFQGQorJhsmM1V5BsA8NaC4ltsghBBCrAVlwKycqYfJTL1C0Jz7DhJCCCHGQhmwCkA2TNY3pDpC63gaNRCRDX0CKFeI0xhDn9qU2yCEEEKsBQVgRGumHPqkqvSEEEIqIgrAiE4ign1wbmZn7B7VBl8PDMHuUW1wbmZng887M9e+g6Ty+uuvv8Dj8fDixQvOj/H398fatWuN1idtbN++HVWqVJH/e8GCBQgJCdHrmIY4BiFEEQVgRGemGPo0176DxDKNGDECPB4Po0ePLnff2LFjwePxMGLECNN3TIMFCxaAx+OBx+PB1tYWNWvWxEcffYSMjAyjP/e0adNw4sQJzu15PB5+++03vY5BCNGMAjBi0Uw954xYvpo1a2LPnj3Iz8+X31ZQUIDdu3ejVq1aZuyZeo0aNUJqaioePnyITZs24dChQ3j//feVthWLxZBIJAZ5XhcXF3h66ldKxRDHIIQoogCMWDxzlNsglqt58+aoVasW9u/fL79t//79qFmzJpo1a6bQtrCwEBMmTIC3tzccHBzQrl07XLlyRaHNkSNHUL9+fTg6OqJTp05ITk4u95wXLlxA+/bt4ejoiJo1a2LChAnIzc3Vqt92dnYQiUSoXr06evXqhQkTJuDYsWPIz8+XDxv+8ccfCAoKgkAgwIMHD1BUVIQZM2agevXqcHZ2RuvWrfHXX38pHHf79u2oVasWnJyc0K9fP2RmZircr2z4cOvWrWjUqBEEAgF8fHwwfvx4ANKhVADo168feDye/N9ljyGRSLBo0SLUqFEDAoEAISEhiIqKkt+fnJwMHo+H/fv3o1OnTnByckLTpk0RExMjb/PgwQP07t0b7u7ucHZ2RqNGjXDkyBGt3lNCrBkFYMQqmGrOWaXFGJCba54fpmqLeNU++OADbNu2Tf7vrVu3YuTIkeXazZgxA/v27cOPP/6I2NhY1K1bF927d8fz59JVs48ePUL//v3Rs2dPxMXF4aOPPsJnn32mcIwbN26ge/fu6N+/P65fv469e/fi3Llz8qBFV46OjpBIJCgpKQEA5OXlYenSpfjhhx9w8+ZNeHt744MPPsD58+exZ88eXL9+He+88w4iIiJw9+5dAMClS5cwcuRIjB07FnFxcejUqRO++OILtc+7adMmjBs3Dh9//DFu3LiBgwcPom7dugAgD063bduG1NTUcsGqzNdff41Vq1Zh5cqVuH79Orp3744+ffrI+yXz+eefY9q0aYiLi0P9+vUxaNAg+esdN24cCgsLcebMGdy4cQPLli2Di4uL7m8oIdaGEZPKzs5mAFh2dra5u0Iqqfz8fJaQkMDy8/Nf3/jqFWPSUMj0P69ece778OHDWd++fVlGRgYTCAQsKSmJJScnMwcHB5aRkcH69u3Lhg8f/t9LesX4fD7btWuX/PFFRUXM19eXLV++nDHG2KxZs1jDhg2ZRCKRt5k5cyYDwLKyshhjjA0bNox9/PHHCv04e/Yss7Gxkb+Hfn5+bM2aNSr7PX/+fNa0aVP5vxMTE1ndunVZq1atGGOMbdu2jQFgcXFx8jb37t1jPB6PPXnyROFYXbp0YbNmzWKMMTZo0CAWERGhcP97773HhEKhyuf29fVln3/+ucq+AmAHDhxQ239fX1/25ZdfKrR544032NixYxljjCUlJTEA7IcffpDff/PmTQaAJSYmMsYYa9y4MVuwYIHKfpSm9JwlpIwSsYRduPeM/fbPY3bh3jNWIpZofpAWDH39pkKsOti4cSNWrFiB1NRUNGrUCGvXrsWbb75p7m4RUml4eXkhMjISP/74IxhjiIyMhJeXl0Kb+/fvo7i4GGFhYfLb+Hw+WrVqhcTERABAYmIi2rRpAx7v9RzC0NBQheNcvXoV9+7dw65du+S3McYgkUiQlJSEhg0bcurzjRs34OLiArFYjMLCQnTs2BHfffed/H57e3s0adJE/u/Y2FgwxlC/fn2F4xQWFsrnYyUmJqJfv34K94eGhioMB5aWnp6OlJQUdOnShVOflcnJyUFKSorC+woAYWFhuHbtmsJtpV+Pj4+PvA8NGjTAhAkTMGbMGBw7dgxdu3bF22+/rdCeEG2Ya7s6fVAApqW9e/di0qRJ2LhxI8LCwvDtt9+iR48eSEhIsOgJwISo5eQEvHplvufWwciRI+XDgBs2bCh3P/tvaLN0cCW7XXabrI06EokEn3zyCSZMmFDuPm3+5gMDA3Hw4EHY2trC19cXAoFA4X5HR0eFvkokEtja2uLq1auwtbVVaCsbquPS/7LPYSjq3lcZPp9frr1sccFHH32E7t274/Dhwzh27BiWLl2KVatW4dNPPzVYH0nlINuuruxfg2y7OkudK0xzwLS0evVqfPjhh/joo4/QsGFDrF27FjVr1sSmTZvM3TVCdMfjAc7O5vnh6baCNSIiAkVFRSgqKkL37t3L3V+3bl3Y29vj3Llz8tuKi4vx999/y7NWQUFBuHjxosLjyv67efPmuHnzJurWrVvux97ennN/7e3tUbduXQQEBJQLvpRp1qwZxGIx0tPTyz2vSCTi3P/SXF1d4e/vr7akBJ/Ph1gsVnm/m5sbfH19Fd5XQLpQgWs2UKZmzZoYPXo09u/fj6lTp+L777/X6vGEaNquDpBuVyeWaD/X1NgoANNCUVERrl69im7duinc3q1bN1y4cMFMvSKkcrK1tUViYiISExPLZYgAwNnZGWPGjMH06dMRFRWFhIQEjBo1Cnl5efjwww8BAKNHj8b9+/cxZcoU3L59Gz///DO2b9+ucJyZM2ciJiYG48aNQ1xcHO7evYuDBw8aPVNTv359DBkyBO+//z7279+PpKQkXLlyBcuWLZOvFpwwYQKioqKwfPly3LlzB+vXr1c5/CizYMECrFq1Ct988w3u3r2L2NhYrFu3Tn6/LEBLS0tDVlaW0mNMnz4dy5Ytw969e3H79m189tlniIuLw8SJEzm/vkmTJuHPP/9EUlISYmNjcfLkSa0DOEKsebs6CsC08OzZM4jFYlSrVk3h9mrVqiEtLU3pYwoLC5GTk6PwQwgxDDc3N7i5uam8/6uvvsLbb7+NYcOGoXnz5rh37x7+/PNPuLu7A5AOIe7btw+HDh1C06ZNsXnzZixZskThGE2aNMHp06dx9+5dvPnmm2jWrBnmzp0rn9NkTNu2bcP777+PqVOnIjAwEH369MGlS5dQs2ZNAECbNm3www8/YN26dQgJCcGxY8cwZ84ctcccPnw41q5di40bN6JRo0bo1auXwurFVatWITo6WmlZD5kJEyZg6tSpmDp1Kho3boyoqCgcPHgQ9erV4/zaxGIxxo0bh4YNGyIiIgKBgYHYuHEj58cTAlj3dnU8pu0kgkosJSUF1atXx4ULFxQm6n755ZfYsWMHbt26Ve4xCxYswMKFC8vdnp2drfbCQYixFBQUICkpCQEBAXBwoC2ciOWjc5aoEnM/E4O+Vz3sLrN7VBuE1tGvmHBOTg6EQqHBrt+UAdOCl5cXbG1ty2W70tPTy2XFZGbNmoXs7Gz5z6NHj0zRVUIIIaTCs+bt6igA04K9vT1atGiB6Ohohdujo6PRtm1bpY8RCATyYRJNwyWEEEII4c6at6ujAExLU6ZMwQ8//ICtW7ciMTER/2/v7oOqqvM/gL8vcr0gDxdUEBDCZxJBUEnBLERF8Skc1yQDxcpaKjE1Hddag2yVHtbVmkZK2kUzGyuVwhEFXRV1QG1BVnxY1lYLJeia8hgC4v38/ujHGY+AAsIF8f2auTPc7/dzzvmeD3e4H875nnOWLFmC/Pz8Bh8OTERERG3rQX1cHe8D1kxhYWG4du0aVq9ejcLCQnh5eSElJQXu7u7tPTQiIqKHUoiXM4I9nXDy0nUYyqvgaPP7aceOeOSrDguwFnjllVfwyiuvtPcwiIiI6P91MdPc90R7U+IpSKKHFC+ApgcFP6vUGbEAI3rI1D0eprKysp1HQtQ0dZ/V2x9tRPSg4ylIoodMly5dYGdnB4PBAADo1q1bvWf4EXUEIoLKykoYDAbY2dk1+MQDogcVCzCih1DdswTrijCijszOzk75zBJ1FizAiB5CGo0Gzs7OcHR0xM2bN9t7OESN0mq1PPJFnRILMKKHWJcuXfjlRkTUDjgJn4iIiMjEWIARERERmRgLMCIiIiIT4xwwE6u7oWBZWVk7j4SIiIiaqu57u7VuDMwCzMTKy8sBAG5ubu08EiIiImqu8vJy6PX6+16PRviMB5MyGo34+eefYWNj81Df/LKsrAxubm64fPkybG1t23s4Dzzms/Uxp62L+Wx9zGnrulc+RQTl5eVwcXGBmdn9z+DiETATMzMzg6ura3sPo8OwtbXlH45WxHy2Pua0dTGfrY85bV13y2drHPmqw0n4RERERCbGAoyIiIjIxFiAUbvQ6XSIiYmBTqdr76F0Csxn62NOWxfz2fqY09Zl6nxyEj4RERGRifEIGBEREZGJsQAjIiIiMjEWYEREREQmxgKMiIiIyMRYgFGLHDlyBNOnT4eLiws0Gg2+/fZbVb+IIDY2Fi4uLrC0tMTYsWNx9uxZVUx1dTWio6PRs2dPWFlZ4amnnsKVK1dUMcXFxZg7dy70ej30ej3mzp2LkpKSNt4704uLi8Njjz0GGxsbODo6YsaMGcjLy1PFMKfNEx8fj6FDhyo3VQwICMDevXuVfubz/sTFxUGj0WDx4sVKG3PaPLGxsdBoNKqXk5OT0s98Nl9BQQEiIiLQo0cPdOvWDb6+vsjKylL6O1ROhagFUlJS5M0335SdO3cKAElKSlL1v/vuu2JjYyM7d+6U3NxcCQsLE2dnZykrK1NioqKipHfv3rJ//37Jzs6WoKAg8fHxkdraWiUmJCREvLy8JCMjQzIyMsTLy0umTZtmqt00mUmTJkliYqKcOXNGcnJyZOrUqfLII49IRUWFEsOcNk9ycrLs2bNH8vLyJC8vT9544w3RarVy5swZEWE+78fJkyelT58+MnToUHnttdeUdua0eWJiYmTIkCFSWFiovAwGg9LPfDbP9evXxd3dXebPny8nTpyQS5cuyYEDB+SHH35QYjpSTlmA0X27swAzGo3i5OQk7777rtJWVVUler1ePvnkExERKSkpEa1WK9u3b1diCgoKxMzMTPbt2yciIufOnRMAcvz4cSUmMzNTAMh//vOfNt6r9mUwGASApKeniwhz2lrs7e3ls88+Yz7vQ3l5uQwcOFD2798vgYGBSgHGnDZfTEyM+Pj4NNjHfDbfihUrZMyYMY32d7Sc8hQktbpLly6hqKgIEydOVNp0Oh0CAwORkZEBAMjKysLNmzdVMS4uLvDy8lJiMjMzodfrMWrUKCXG398fer1eiemsSktLAQDdu3cHwJzer1u3bmH79u347bffEBAQwHzeh1dffRVTp07FhAkTVO3MactcuHABLi4u6Nu3L5555hlcvHgRAPPZEsnJyfDz88PTTz8NR0dHDBs2DAkJCUp/R8spCzBqdUVFRQCAXr16qdp79eql9BUVFaFr166wt7e/a4yjo2O99Ts6OioxnZGIYOnSpRgzZgy8vLwAMKctlZubC2tra+h0OkRFRSEpKQmenp7MZwtt374d2dnZiIuLq9fHnDbfqFGj8PnnnyM1NRUJCQkoKirC6NGjce3aNeazBS5evIj4+HgMHDgQqampiIqKwqJFi/D5558D6HifUfOm7xpR82g0GtV7EanXdqc7YxqKb8p6HmQLFy7E6dOncezYsXp9zGnzeHh4ICcnByUlJdi5cyciIyORnp6u9DOfTXf58mW89tprSEtLg4WFRaNxzGnTTZ48WfnZ29sbAQEB6N+/P7Zs2QJ/f38AzGdzGI1G+Pn5Ye3atQCAYcOG4ezZs4iPj8e8efOUuI6SUx4Bo1ZXdxXPnf8JGAwG5T8PJycn1NTUoLi4+K4xv/zyS731X716td5/MJ1FdHQ0kpOTcejQIbi6uirtzGnLdO3aFQMGDICfnx/i4uLg4+ODDz/8kPlsgaysLBgMBowYMQLm5uYwNzdHeno6PvroI5ibmyv7y5y2nJWVFby9vXHhwgV+RlvA2dkZnp6eqrbBgwcjPz8fQMf7O8oCjFpd37594eTkhP379yttNTU1SE9Px+jRowEAI0aMgFarVcUUFhbizJkzSkxAQABKS0tx8uRJJebEiRMoLS1VYjoLEcHChQuxa9cuHDx4EH379lX1M6etQ0RQXV3NfLbA+PHjkZubi5ycHOXl5+eH8PBw5OTkoF+/fszpfaqursb58+fh7OzMz2gLPP744/Vu3/Pf//4X7u7uADrg39EmT9cnuk15ebmcOnVKTp06JQDkb3/7m5w6dUp++uknEfn9Ul+9Xi+7du2S3NxcmTNnToOX+rq6usqBAwckOztbxo0b1+ClvkOHDpXMzEzJzMwUb2/vTnn59Msvvyx6vV4OHz6suiS9srJSiWFOm2flypVy5MgRuXTpkpw+fVreeOMNMTMzk7S0NBFhPlvD7VdBijCnzfX666/L4cOH5eLFi3L8+HGZNm2a2NjYyI8//igizGdznTx5UszNzWXNmjVy4cIF2bZtm3Tr1k2++OILJaYj5ZQFGLXIoUOHBEC9V2RkpIj8frlvTEyMODk5iU6nkyeffFJyc3NV67hx44YsXLhQunfvLpaWljJt2jTJz89XxVy7dk3Cw8PFxsZGbGxsJDw8XIqLi020l6bTUC4BSGJiohLDnDbP888/L+7u7tK1a1dxcHCQ8ePHK8WXCPPZGu4swJjT5qm7B5VWqxUXFxeZOXOmnD17VulnPptv9+7d4uXlJTqdTh599FHZtGmTqr8j5VQjItL042VEREREdL84B4yIiIjIxFiAEREREZkYCzAiIiIiE2MBRkRERGRiLMCIiIiITIwFGBEREZGJsQAjIiIiMjEWYETUaWk0Gnz77bdtuo28vDw4OTmhvLy8TbfTnmJjY9GrV682z6fBYICDgwMKCgrabBtEHQVvxEr0kJs/fz5KSkrqfbEePnwYQUFBKC4uhp2dXbuM7X4VFRXB3t4eOp2uzbYxa9Ys+Pj4YNWqVW22jfZ0/vx5eHp6IikpCf7+/q2Wz8Y+d0uXLkVZWRk+++yz+94GUUfGI2BE1Gk5OTm1afF15coVJCcn47nnnmuzbbSWmzdvtmi5//3vfwCA0NDQNs8nADz33HPYtm0biouL23Q7RO2NBRgRNdnOnTsxZMgQ6HQ69OnTB+vWrVP1N3SKys7ODps3bwYA1NTUYOHChXB2doaFhQX69OmDuLg4Jba0tBQvvfQSHB0dYWtri3HjxuHf//53o+O51/puH09sbCw0Gk29V93YRATvv/8++vXrB0tLS/j4+GDHjh13zcfXX38NHx8fuLq6Km0//fQTpk+fDnt7e1hZWWHIkCFISUlR+lNSUjBo0CBYWloiKCgImzdvhkajQUlJiTJOX19f1XY2bNiAPn36KO+///57BAcHo2fPntDr9QgMDER2drZqGY1Gg08++QShoaGwsrLCX/7yFwDA7t27MWLECFhYWKBfv354++23UVtb2+D+xcbGYvr06QAAMzMzaDQapS8xMRGDBw+GhYUFHn30UWzcuFG1bEFBAcLCwmBvb48ePXogNDQUP/74o7LeLVu24LvvvlN+D4cPHwYAeHt7w8nJCUlJSXfNPdGDjgUYETVJVlYWZs+ejWeeeQa5ubmIjY3FqlWrlAKmKT766CMkJyfj66+/Rl5eHr744gulsBARTJ06FUVFRUhJSUFWVhaGDx+O8ePH4/r1681e352WLVuGwsJC5fXXv/4V3bp1g5+fHwDgz3/+MxITExEfH4+zZ89iyZIliIiIQHp6eqP7c+TIEWX5Oq+++iqqq6tx5MgR5Obm4r333oO1tTUA4PLly5g5cyamTJmCnJwcLFiwAH/605+anL865eXliIyMxNGjR3H8+HEMHDgQU6ZMqTcPLSYmBqGhocjNzcXzzz+P1NRUREREYNGiRTh37hw+/fRTbN68GWvWrGk0Z4mJiQCg5A0AEhIS8Oabb2LNmjU4f/481q5di1WrVmHLli0AgMrKSgQFBcHa2hpHjhzBsWPHYG1tjZCQENTU1GDZsmWYPXs2QkJClPWOHj1a2e7IkSNx9OjRZueF6IHSzAeNE1EnExkZKV26dBErKyvVy8LCQgBIcXGxiIg8++yzEhwcrFp2+fLl4unpqbwHIElJSaoYvV4viYmJIiISHR0t48aNE6PRWG8c//znP8XW1laqqqpU7f3795dPP/20wbHfbX2NjUdEJDMzUywsLOSrr74SEZGKigqxsLCQjIwMVdwLL7wgc+bMaXDdIiI+Pj6yevVqVZu3t7fExsY2GL9y5UoZPHiwarwrVqxQ5TkmJkZ8fHxUy61fv17c3d0bHUdtba3Y2NjI7t27lTYAsnjxYlXcE088IWvXrlW1bd26VZydnRtdd1JSktz5VeHm5iZffvmlqu2dd96RgIAAERH5+9//Lh4eHqr9rK6uFktLS0lNTRWR3z93oaGhDW5zyZIlMnbs2EbHRNQZmLdn8UdEHUNQUBDi4+NVbSdOnEBERITy/vz58wgNDVXFPP7449iwYQNu3bqFLl263HM78+fPR3BwMDw8PBASEoJp06Zh4sSJAH4/wlZRUYEePXqolrlx44YyD6k562tMfn4+ZsyYoRyFAYBz586hqqoKwcHBqtiamhoMGzas0XXduHEDFhYWqrZFixbh5ZdfRlpaGiZMmIA//OEPGDp0KIDfc+jv7686lRcQEHDX8TbEYDDgrbfewsGDB/HLL7/g1q1bqKysRH5+viruzqNzWVlZ+P7771VHvG7duoWqqipUVlaiW7du99z21atXcfnyZbzwwgt48cUXlfba2lro9XplOz/88ANsbGxUy1ZVVTX6u7ydpaUlKisr7xlH9CBjAUZEsLKywoABA1RtV65cUb0XEVXhUNd2O41GU6/t9snfw4cPx6VLl7B3714cOHAAs2fPxoQJE7Bjxw4YjUY4Ozsrc4Fu19hVmHdbX0N+++03PPXUUwgICMDq1auVdqPRCADYs2cPevfurVrmbpPOe/bsWW+y+IIFCzBp0iTs2bMHaWlpiIuLw7p16xAdHV0vNw0xMzO7aw6B3wvPq1evYsOGDXB3d4dOp0NAQABqampUcVZWVqr3RqMRb7/9NmbOnFlvu3cWko2py1VCQgJGjRql6qsrwo1GI0aMGIFt27bVW97BweGe27h+/XqT4ogeZCzAiKhJPD09cezYMVVbRkYGBg0apHzxOjg4KPOEAODChQv1jmTY2toiLCwMYWFhmDVrFkJCQnD9+nUMHz4cRUVFMDc3b3QeV0MaW1/37t1VcSKCiIgIGI1GbN26VVVMenp6QqfTIT8/H4GBgU3e9rBhw3Du3Ll67W5uboiKikJUVBRWrlyJhIQEREdHw9PTs95FCsePH1e9d3BwQFFRkargzcnJUcUcPXoUGzduxJQpUwD8Prfs119/ved4hw8fjry8vHrFdnP06tULvXv3xsWLFxEeHt7odr766ivlYoqGdO3aFbdu3Wqw78yZMxg7dmyLx0j0IGABRkRN8vrrr+Oxxx7DO++8g7CwMGRmZuLjjz9WXf02btw4fPzxx/D394fRaMSKFSug1WqV/vXr18PZ2Rm+vr4wMzPDN998AycnJ9jZ2WHChAkICAjAjBkz8N5778HDwwM///wzUlJSMGPGjHqn0+61vjvFxsbiwIEDSEtLQ0VFBSoqKgAAer0eNjY2WLZsGZYsWQKj0YgxY8agrKwMGRkZsLa2RmRkZIM5mTRpEhYsWKA6Bbt48WJMnjwZgwYNQnFxMQ4ePIjBgwcDAKKiorBu3TosXboUf/zjH5GVlVXvIoaxY8fi6tWreP/99zFr1izs27cPe/fuVRUyAwYMwNatW+Hn54eysjIsX74clpaW9/wdvvXWW5g2bRrc3Nzw9NNPw8zMDKdPn0Zubq5ylWRTxMbGYtGiRbC1tcXkyZNRXV2Nf/3rXyguLsbSpUsRHh6ODz74AKGhoVi9ejVcXV2Rn5+PXbt2Yfny5XB1dUWfPn2QmpqKvLw89OjRA3q9HlqtFpWVlcjKysLatWubPB6iB1I7zj8jog6gscnQhw4dUk0OFxHZsWOHeHp6ilarlUceeUQ++OAD1TIFBQUyceJEsbKykoEDB0pKSopqEv6mTZvE19dXrKysxNbWVsaPHy/Z2dnK8mVlZRIdHS0uLi6i1WrFzc1NwsPDJT8/v8Gx32t9uG0SfmBgoACo96obm9FolA8//FA8PDxEq9WKg4ODTJo0SdLT0xvNXW1trfTu3Vv27duntC1cuFD69+8vOp1OHBwcZO7cufLrr78q/bt375YBAwaITqeTJ554Qv7xj3/Uy3N8fLy4ubmJlZWVzJs3T9asWaOahJ+dnS1+fn6i0+lk4MCB8s0334i7u7usX7++wX2/3b59+2T06NFiaWkptra2MnLkSNm0aVOj+9jQJHwRkW3btomvr6907dpV7O3t5cknn5Rdu3Yp/YWFhTJv3jzp2bOn6HQ66devn7z44otSWloqIiIGg0GCg4PF2tpaAMihQ4dEROTLL78UDw+PRsdD1FnwTvhERPdh48aN+O6775Camtqi5TvDEwda08iRI7F48WI8++yz7T0UojbFU5BERPfhpZdeQnFxMcrLy+td9UfNYzAYMGvWLMyZM6e9h0LU5ngEjIioHfEIGNHDiQUYERERkYnxUUREREREJsYCjIiIiMjEWIARERERmRgLMCIiIiITYwFGREREZGIswIiIiIhMjAUYERERkYmxACMiIiIyMRZgRERERCb2fzYsjt/9GCHgAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Generate a range of house sizes for prediction\n",
+    "sizes = np.linspace(sacramento[\"sq__ft\"].min(), sacramento[\"sq__ft\"].max(), 100).reshape(-1, 1)\n",
+    "\n",
+    "# Predict house prices for these sizes using the best model from GridSearchCV\n",
+    "predicted_prices = sacr_gridsearch.predict(sizes)\n",
+    "\n",
+    "# Plot the original data\n",
+    "plt.scatter(sacramento[\"sq__ft\"], sacramento[\"price\"], label=\"Actual Prices\")\n",
+    "\n",
+    "# Plot the model predictions as a line\n",
+    "plt.plot(sizes, predicted_prices, color='red', label=\"Model Predictions\")\n",
+    "\n",
+    "# Add labels and legend\n",
+    "plt.xlabel(\"House size (square feet)\")\n",
+    "plt.ylabel(\"Price (USD)\")\n",
+    "plt.title(\"Scatter Plot of House Size vs Price with Model Predictions\")\n",
+    "plt.legend()\n",
+    "plt.show();"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "dsi_participant",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/01_materials/notebooks/Classification-2.ipynb b/01_materials/notebooks/Classification-2.ipynb
index 96db650b8..fa1a27830 100644
--- a/01_materials/notebooks/Classification-2.ipynb
+++ b/01_materials/notebooks/Classification-2.ipynb
@@ -21,7 +21,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -38,7 +38,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -424,7 +424,7 @@
        "[569 rows x 32 columns]"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -444,7 +444,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -453,7 +453,7 @@
        "array(['Malignant', 'Benign'], dtype=object)"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -478,7 +478,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -864,7 +864,7 @@
        "[569 rows x 32 columns]"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -956,7 +956,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -978,7 +978,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -986,7 +986,7 @@
      "output_type": "stream",
      "text": [
       "<class 'pandas.core.frame.DataFrame'>\n",
-      "Int64Index: 426 entries, 164 to 284\n",
+      "Index: 426 entries, 164 to 284\n",
       "Data columns (total 32 columns):\n",
       " #   Column                   Non-Null Count  Dtype  \n",
       "---  ------                   --------------  -----  \n",
@@ -1033,18 +1033,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
+       "diagnosis\n",
        "Benign       0.626761\n",
        "Malignant    0.373239\n",
-       "Name: diagnosis, dtype: float64"
+       "Name: proportion, dtype: float64"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1070,19 +1071,423 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div>"
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;&nbsp;KNeighborsClassifier<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></label><div class=\"sk-toggleable__content \"><pre>KNeighborsClassifier()</pre></div> </div></div></div></div>"
       ],
       "text/plain": [
        "KNeighborsClassifier()"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1094,19 +1499,423 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div>"
+       "<style>#sk-container-id-2 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-2 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-2 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-2 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;KNeighborsClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsClassifier()</pre></div> </div></div></div></div>"
       ],
       "text/plain": [
        "KNeighborsClassifier()"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1124,7 +1933,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -1242,7 +2051,7 @@
        "[143 rows x 3 columns]"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1277,7 +2086,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -1286,7 +2095,7 @@
        "0.9230769230769231"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1363,7 +2172,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -1418,7 +2227,7 @@
        "Malignant       9         44"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1488,7 +2297,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -1497,7 +2306,7 @@
        "0.9565217391304348"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1523,7 +2332,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -1532,7 +2341,7 @@
        "0.8301886792452831"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1592,7 +2401,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -1601,7 +2410,7 @@
        "0.8785046728971962"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1671,7 +2480,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -1703,32 +2512,32 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>0.002303</td>\n",
-       "      <td>0.004767</td>\n",
+       "      <td>0.011669</td>\n",
+       "      <td>0.005997</td>\n",
        "      <td>0.930233</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>0.001559</td>\n",
-       "      <td>0.003668</td>\n",
+       "      <td>0.001999</td>\n",
+       "      <td>0.009697</td>\n",
        "      <td>0.894118</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>0.001143</td>\n",
-       "      <td>0.002115</td>\n",
+       "      <td>0.001734</td>\n",
+       "      <td>0.000000</td>\n",
        "      <td>0.870588</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>0.001001</td>\n",
-       "      <td>0.001845</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.011973</td>\n",
        "      <td>0.952941</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>0.000851</td>\n",
-       "      <td>0.001786</td>\n",
+       "      <td>0.002000</td>\n",
+       "      <td>0.004999</td>\n",
        "      <td>0.917647</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -1737,14 +2546,14 @@
       ],
       "text/plain": [
        "   fit_time  score_time  test_score\n",
-       "0  0.002303    0.004767    0.930233\n",
-       "1  0.001559    0.003668    0.894118\n",
-       "2  0.001143    0.002115    0.870588\n",
-       "3  0.001001    0.001845    0.952941\n",
-       "4  0.000851    0.001786    0.917647"
+       "0  0.011669    0.005997    0.930233\n",
+       "1  0.001999    0.009697    0.894118\n",
+       "2  0.001734    0.000000    0.870588\n",
+       "3  0.000000    0.011973    0.952941\n",
+       "4  0.002000    0.004999    0.917647"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1768,7 +2577,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -1800,14 +2609,14 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
-       "      <td>0.001371</td>\n",
-       "      <td>0.002836</td>\n",
+       "      <td>0.003480</td>\n",
+       "      <td>0.006533</td>\n",
        "      <td>0.913105</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>sem</th>\n",
-       "      <td>0.000261</td>\n",
-       "      <td>0.000593</td>\n",
+       "      <td>0.002081</td>\n",
+       "      <td>0.002061</td>\n",
        "      <td>0.014264</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -1816,11 +2625,11 @@
       ],
       "text/plain": [
        "      fit_time  score_time  test_score\n",
-       "mean  0.001371    0.002836    0.913105\n",
-       "sem   0.000261    0.000593    0.014264"
+       "mean  0.003480    0.006533    0.913105\n",
+       "sem   0.002081    0.002061    0.014264"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1884,7 +2693,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1903,7 +2712,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1916,7 +2725,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -1964,10 +2773,10 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>0.001236</td>\n",
-       "      <td>0.000532</td>\n",
-       "      <td>0.001921</td>\n",
-       "      <td>0.001362</td>\n",
+       "      <td>0.002365</td>\n",
+       "      <td>0.002850</td>\n",
+       "      <td>0.004009</td>\n",
+       "      <td>0.002898</td>\n",
        "      <td>1</td>\n",
        "      <td>{'n_neighbors': 1}</td>\n",
        "      <td>0.953488</td>\n",
@@ -1986,10 +2795,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>0.000739</td>\n",
-       "      <td>0.000039</td>\n",
+       "      <td>0.002970</td>\n",
+       "      <td>0.004753</td>\n",
        "      <td>0.001109</td>\n",
-       "      <td>0.000077</td>\n",
+       "      <td>0.001382</td>\n",
        "      <td>6</td>\n",
        "      <td>{'n_neighbors': 6}</td>\n",
        "      <td>0.930233</td>\n",
@@ -2008,10 +2817,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>0.000642</td>\n",
-       "      <td>0.000028</td>\n",
-       "      <td>0.000983</td>\n",
-       "      <td>0.000054</td>\n",
+       "      <td>0.003535</td>\n",
+       "      <td>0.005796</td>\n",
+       "      <td>0.002741</td>\n",
+       "      <td>0.003816</td>\n",
        "      <td>11</td>\n",
        "      <td>{'n_neighbors': 11}</td>\n",
        "      <td>0.906977</td>\n",
@@ -2030,10 +2839,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>0.000591</td>\n",
-       "      <td>0.000042</td>\n",
-       "      <td>0.000902</td>\n",
-       "      <td>0.000028</td>\n",
+       "      <td>0.001804</td>\n",
+       "      <td>0.005148</td>\n",
+       "      <td>0.004924</td>\n",
+       "      <td>0.006816</td>\n",
        "      <td>16</td>\n",
        "      <td>{'n_neighbors': 16}</td>\n",
        "      <td>0.906977</td>\n",
@@ -2052,10 +2861,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>0.000560</td>\n",
-       "      <td>0.000055</td>\n",
-       "      <td>0.000879</td>\n",
-       "      <td>0.000042</td>\n",
+       "      <td>0.001850</td>\n",
+       "      <td>0.003662</td>\n",
+       "      <td>0.004817</td>\n",
+       "      <td>0.006118</td>\n",
        "      <td>21</td>\n",
        "      <td>{'n_neighbors': 21}</td>\n",
        "      <td>0.906977</td>\n",
@@ -2074,10 +2883,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
-       "      <td>0.000539</td>\n",
-       "      <td>0.000011</td>\n",
-       "      <td>0.000888</td>\n",
-       "      <td>0.000066</td>\n",
+       "      <td>0.001878</td>\n",
+       "      <td>0.004291</td>\n",
+       "      <td>0.003432</td>\n",
+       "      <td>0.006023</td>\n",
        "      <td>26</td>\n",
        "      <td>{'n_neighbors': 26}</td>\n",
        "      <td>0.906977</td>\n",
@@ -2096,10 +2905,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
-       "      <td>0.000553</td>\n",
-       "      <td>0.000030</td>\n",
-       "      <td>0.000899</td>\n",
-       "      <td>0.000049</td>\n",
+       "      <td>0.002092</td>\n",
+       "      <td>0.004207</td>\n",
+       "      <td>0.002637</td>\n",
+       "      <td>0.004826</td>\n",
        "      <td>31</td>\n",
        "      <td>{'n_neighbors': 31}</td>\n",
        "      <td>0.906977</td>\n",
@@ -2118,10 +2927,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
-       "      <td>0.000532</td>\n",
-       "      <td>0.000011</td>\n",
-       "      <td>0.000890</td>\n",
-       "      <td>0.000015</td>\n",
+       "      <td>0.000296</td>\n",
+       "      <td>0.000625</td>\n",
+       "      <td>0.005729</td>\n",
+       "      <td>0.007880</td>\n",
        "      <td>36</td>\n",
        "      <td>{'n_neighbors': 36}</td>\n",
        "      <td>0.906977</td>\n",
@@ -2140,10 +2949,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
-       "      <td>0.000541</td>\n",
-       "      <td>0.000023</td>\n",
-       "      <td>0.000918</td>\n",
-       "      <td>0.000048</td>\n",
+       "      <td>0.002190</td>\n",
+       "      <td>0.004474</td>\n",
+       "      <td>0.003729</td>\n",
+       "      <td>0.005384</td>\n",
        "      <td>41</td>\n",
        "      <td>{'n_neighbors': 41}</td>\n",
        "      <td>0.906977</td>\n",
@@ -2162,10 +2971,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
-       "      <td>0.000551</td>\n",
-       "      <td>0.000041</td>\n",
-       "      <td>0.000936</td>\n",
-       "      <td>0.000028</td>\n",
+       "      <td>0.003755</td>\n",
+       "      <td>0.005865</td>\n",
+       "      <td>0.001121</td>\n",
+       "      <td>0.002985</td>\n",
        "      <td>46</td>\n",
        "      <td>{'n_neighbors': 46}</td>\n",
        "      <td>0.906977</td>\n",
@@ -2184,10 +2993,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
-       "      <td>0.000554</td>\n",
-       "      <td>0.000036</td>\n",
-       "      <td>0.000965</td>\n",
-       "      <td>0.000057</td>\n",
+       "      <td>0.001470</td>\n",
+       "      <td>0.003812</td>\n",
+       "      <td>0.004952</td>\n",
+       "      <td>0.007163</td>\n",
        "      <td>51</td>\n",
        "      <td>{'n_neighbors': 51}</td>\n",
        "      <td>0.906977</td>\n",
@@ -2206,10 +3015,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
-       "      <td>0.000553</td>\n",
-       "      <td>0.000047</td>\n",
-       "      <td>0.000977</td>\n",
-       "      <td>0.000046</td>\n",
+       "      <td>0.001652</td>\n",
+       "      <td>0.003419</td>\n",
+       "      <td>0.005267</td>\n",
+       "      <td>0.008048</td>\n",
        "      <td>56</td>\n",
        "      <td>{'n_neighbors': 56}</td>\n",
        "      <td>0.906977</td>\n",
@@ -2228,10 +3037,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
-       "      <td>0.000552</td>\n",
-       "      <td>0.000017</td>\n",
-       "      <td>0.001015</td>\n",
-       "      <td>0.000091</td>\n",
+       "      <td>0.001976</td>\n",
+       "      <td>0.003919</td>\n",
+       "      <td>0.003366</td>\n",
+       "      <td>0.006761</td>\n",
        "      <td>61</td>\n",
        "      <td>{'n_neighbors': 61}</td>\n",
        "      <td>0.906977</td>\n",
@@ -2250,10 +3059,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
-       "      <td>0.000573</td>\n",
-       "      <td>0.000072</td>\n",
-       "      <td>0.001017</td>\n",
-       "      <td>0.000080</td>\n",
+       "      <td>0.004449</td>\n",
+       "      <td>0.006554</td>\n",
+       "      <td>0.002174</td>\n",
+       "      <td>0.004767</td>\n",
        "      <td>66</td>\n",
        "      <td>{'n_neighbors': 66}</td>\n",
        "      <td>0.930233</td>\n",
@@ -2272,10 +3081,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
-       "      <td>0.000530</td>\n",
-       "      <td>0.000008</td>\n",
-       "      <td>0.001025</td>\n",
-       "      <td>0.000093</td>\n",
+       "      <td>0.003282</td>\n",
+       "      <td>0.005389</td>\n",
+       "      <td>0.002785</td>\n",
+       "      <td>0.005229</td>\n",
        "      <td>71</td>\n",
        "      <td>{'n_neighbors': 71}</td>\n",
        "      <td>0.930233</td>\n",
@@ -2294,10 +3103,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15</th>\n",
-       "      <td>0.000552</td>\n",
-       "      <td>0.000045</td>\n",
-       "      <td>0.001024</td>\n",
-       "      <td>0.000052</td>\n",
+       "      <td>0.001471</td>\n",
+       "      <td>0.004413</td>\n",
+       "      <td>0.003773</td>\n",
+       "      <td>0.006788</td>\n",
        "      <td>76</td>\n",
        "      <td>{'n_neighbors': 76}</td>\n",
        "      <td>0.930233</td>\n",
@@ -2316,10 +3125,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
-       "      <td>0.000528</td>\n",
-       "      <td>0.000015</td>\n",
-       "      <td>0.001038</td>\n",
-       "      <td>0.000071</td>\n",
+       "      <td>0.003095</td>\n",
+       "      <td>0.006271</td>\n",
+       "      <td>0.003519</td>\n",
+       "      <td>0.005176</td>\n",
        "      <td>81</td>\n",
        "      <td>{'n_neighbors': 81}</td>\n",
        "      <td>0.930233</td>\n",
@@ -2338,10 +3147,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
-       "      <td>0.000538</td>\n",
-       "      <td>0.000022</td>\n",
-       "      <td>0.001039</td>\n",
-       "      <td>0.000036</td>\n",
+       "      <td>0.000263</td>\n",
+       "      <td>0.000789</td>\n",
+       "      <td>0.005718</td>\n",
+       "      <td>0.006187</td>\n",
        "      <td>86</td>\n",
        "      <td>{'n_neighbors': 86}</td>\n",
        "      <td>0.906977</td>\n",
@@ -2360,10 +3169,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18</th>\n",
-       "      <td>0.000535</td>\n",
-       "      <td>0.000017</td>\n",
-       "      <td>0.001055</td>\n",
-       "      <td>0.000070</td>\n",
+       "      <td>0.004861</td>\n",
+       "      <td>0.007433</td>\n",
+       "      <td>0.000088</td>\n",
+       "      <td>0.000264</td>\n",
        "      <td>91</td>\n",
        "      <td>{'n_neighbors': 91}</td>\n",
        "      <td>0.906977</td>\n",
@@ -2382,10 +3191,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19</th>\n",
-       "      <td>0.000548</td>\n",
-       "      <td>0.000048</td>\n",
-       "      <td>0.001064</td>\n",
-       "      <td>0.000042</td>\n",
+       "      <td>0.001921</td>\n",
+       "      <td>0.004337</td>\n",
+       "      <td>0.004902</td>\n",
+       "      <td>0.007439</td>\n",
        "      <td>96</td>\n",
        "      <td>{'n_neighbors': 96}</td>\n",
        "      <td>0.906977</td>\n",
@@ -2408,48 +3217,48 @@
       ],
       "text/plain": [
        "    mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
-       "0        0.001236      0.000532         0.001921        0.001362   \n",
-       "1        0.000739      0.000039         0.001109        0.000077   \n",
-       "2        0.000642      0.000028         0.000983        0.000054   \n",
-       "3        0.000591      0.000042         0.000902        0.000028   \n",
-       "4        0.000560      0.000055         0.000879        0.000042   \n",
-       "5        0.000539      0.000011         0.000888        0.000066   \n",
-       "6        0.000553      0.000030         0.000899        0.000049   \n",
-       "7        0.000532      0.000011         0.000890        0.000015   \n",
-       "8        0.000541      0.000023         0.000918        0.000048   \n",
-       "9        0.000551      0.000041         0.000936        0.000028   \n",
-       "10       0.000554      0.000036         0.000965        0.000057   \n",
-       "11       0.000553      0.000047         0.000977        0.000046   \n",
-       "12       0.000552      0.000017         0.001015        0.000091   \n",
-       "13       0.000573      0.000072         0.001017        0.000080   \n",
-       "14       0.000530      0.000008         0.001025        0.000093   \n",
-       "15       0.000552      0.000045         0.001024        0.000052   \n",
-       "16       0.000528      0.000015         0.001038        0.000071   \n",
-       "17       0.000538      0.000022         0.001039        0.000036   \n",
-       "18       0.000535      0.000017         0.001055        0.000070   \n",
-       "19       0.000548      0.000048         0.001064        0.000042   \n",
-       "\n",
-       "   param_n_neighbors               params  split0_test_score  \\\n",
-       "0                  1   {'n_neighbors': 1}           0.953488   \n",
-       "1                  6   {'n_neighbors': 6}           0.930233   \n",
-       "2                 11  {'n_neighbors': 11}           0.906977   \n",
-       "3                 16  {'n_neighbors': 16}           0.906977   \n",
-       "4                 21  {'n_neighbors': 21}           0.906977   \n",
-       "5                 26  {'n_neighbors': 26}           0.906977   \n",
-       "6                 31  {'n_neighbors': 31}           0.906977   \n",
-       "7                 36  {'n_neighbors': 36}           0.906977   \n",
-       "8                 41  {'n_neighbors': 41}           0.906977   \n",
-       "9                 46  {'n_neighbors': 46}           0.906977   \n",
-       "10                51  {'n_neighbors': 51}           0.906977   \n",
-       "11                56  {'n_neighbors': 56}           0.906977   \n",
-       "12                61  {'n_neighbors': 61}           0.906977   \n",
-       "13                66  {'n_neighbors': 66}           0.930233   \n",
-       "14                71  {'n_neighbors': 71}           0.930233   \n",
-       "15                76  {'n_neighbors': 76}           0.930233   \n",
-       "16                81  {'n_neighbors': 81}           0.930233   \n",
-       "17                86  {'n_neighbors': 86}           0.906977   \n",
-       "18                91  {'n_neighbors': 91}           0.906977   \n",
-       "19                96  {'n_neighbors': 96}           0.906977   \n",
+       "0        0.002365      0.002850         0.004009        0.002898   \n",
+       "1        0.002970      0.004753         0.001109        0.001382   \n",
+       "2        0.003535      0.005796         0.002741        0.003816   \n",
+       "3        0.001804      0.005148         0.004924        0.006816   \n",
+       "4        0.001850      0.003662         0.004817        0.006118   \n",
+       "5        0.001878      0.004291         0.003432        0.006023   \n",
+       "6        0.002092      0.004207         0.002637        0.004826   \n",
+       "7        0.000296      0.000625         0.005729        0.007880   \n",
+       "8        0.002190      0.004474         0.003729        0.005384   \n",
+       "9        0.003755      0.005865         0.001121        0.002985   \n",
+       "10       0.001470      0.003812         0.004952        0.007163   \n",
+       "11       0.001652      0.003419         0.005267        0.008048   \n",
+       "12       0.001976      0.003919         0.003366        0.006761   \n",
+       "13       0.004449      0.006554         0.002174        0.004767   \n",
+       "14       0.003282      0.005389         0.002785        0.005229   \n",
+       "15       0.001471      0.004413         0.003773        0.006788   \n",
+       "16       0.003095      0.006271         0.003519        0.005176   \n",
+       "17       0.000263      0.000789         0.005718        0.006187   \n",
+       "18       0.004861      0.007433         0.000088        0.000264   \n",
+       "19       0.001921      0.004337         0.004902        0.007439   \n",
+       "\n",
+       "    param_n_neighbors               params  split0_test_score  \\\n",
+       "0                   1   {'n_neighbors': 1}           0.953488   \n",
+       "1                   6   {'n_neighbors': 6}           0.930233   \n",
+       "2                  11  {'n_neighbors': 11}           0.906977   \n",
+       "3                  16  {'n_neighbors': 16}           0.906977   \n",
+       "4                  21  {'n_neighbors': 21}           0.906977   \n",
+       "5                  26  {'n_neighbors': 26}           0.906977   \n",
+       "6                  31  {'n_neighbors': 31}           0.906977   \n",
+       "7                  36  {'n_neighbors': 36}           0.906977   \n",
+       "8                  41  {'n_neighbors': 41}           0.906977   \n",
+       "9                  46  {'n_neighbors': 46}           0.906977   \n",
+       "10                 51  {'n_neighbors': 51}           0.906977   \n",
+       "11                 56  {'n_neighbors': 56}           0.906977   \n",
+       "12                 61  {'n_neighbors': 61}           0.906977   \n",
+       "13                 66  {'n_neighbors': 66}           0.930233   \n",
+       "14                 71  {'n_neighbors': 71}           0.930233   \n",
+       "15                 76  {'n_neighbors': 76}           0.930233   \n",
+       "16                 81  {'n_neighbors': 81}           0.930233   \n",
+       "17                 86  {'n_neighbors': 86}           0.906977   \n",
+       "18                 91  {'n_neighbors': 91}           0.906977   \n",
+       "19                 96  {'n_neighbors': 96}           0.906977   \n",
        "\n",
        "    split1_test_score  split2_test_score  split3_test_score  \\\n",
        "0            0.837209           0.906977           0.860465   \n",
@@ -2540,7 +3349,7 @@
        "19        0.047625               14  "
       ]
      },
-     "execution_count": 24,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2601,12 +3410,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
@@ -2789,7 +3598,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "base",
+   "display_name": "dsi_participant",
    "language": "python",
    "name": "python3"
   },
@@ -2803,7 +3612,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.10"
+   "version": "3.9.15"
   }
  },
  "nbformat": 4,
diff --git a/02_activities/assignments/assignment_1.ipynb b/02_activities/assignments/assignment_1.ipynb
index 73d92a3ee..5e3be7065 100644
--- a/02_activities/assignments/assignment_1.ipynb
+++ b/02_activities/assignments/assignment_1.ipynb
@@ -34,7 +34,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "4a3485d6-ba58-4660-a983-5680821c5719",
    "metadata": {},
    "outputs": [],
@@ -56,10 +56,288 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "a431d282-f9ca-4d5d-8912-71ffc9d8ea19",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>alcohol</th>\n",
+       "      <th>malic_acid</th>\n",
+       "      <th>ash</th>\n",
+       "      <th>alcalinity_of_ash</th>\n",
+       "      <th>magnesium</th>\n",
+       "      <th>total_phenols</th>\n",
+       "      <th>flavanoids</th>\n",
+       "      <th>nonflavanoid_phenols</th>\n",
+       "      <th>proanthocyanins</th>\n",
+       "      <th>color_intensity</th>\n",
+       "      <th>hue</th>\n",
+       "      <th>od280/od315_of_diluted_wines</th>\n",
+       "      <th>proline</th>\n",
+       "      <th>class</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>14.23</td>\n",
+       "      <td>1.71</td>\n",
+       "      <td>2.43</td>\n",
+       "      <td>15.6</td>\n",
+       "      <td>127.0</td>\n",
+       "      <td>2.80</td>\n",
+       "      <td>3.06</td>\n",
+       "      <td>0.28</td>\n",
+       "      <td>2.29</td>\n",
+       "      <td>5.64</td>\n",
+       "      <td>1.04</td>\n",
+       "      <td>3.92</td>\n",
+       "      <td>1065.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>13.20</td>\n",
+       "      <td>1.78</td>\n",
+       "      <td>2.14</td>\n",
+       "      <td>11.2</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>2.65</td>\n",
+       "      <td>2.76</td>\n",
+       "      <td>0.26</td>\n",
+       "      <td>1.28</td>\n",
+       "      <td>4.38</td>\n",
+       "      <td>1.05</td>\n",
+       "      <td>3.40</td>\n",
+       "      <td>1050.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>13.16</td>\n",
+       "      <td>2.36</td>\n",
+       "      <td>2.67</td>\n",
+       "      <td>18.6</td>\n",
+       "      <td>101.0</td>\n",
+       "      <td>2.80</td>\n",
+       "      <td>3.24</td>\n",
+       "      <td>0.30</td>\n",
+       "      <td>2.81</td>\n",
+       "      <td>5.68</td>\n",
+       "      <td>1.03</td>\n",
+       "      <td>3.17</td>\n",
+       "      <td>1185.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>14.37</td>\n",
+       "      <td>1.95</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>16.8</td>\n",
+       "      <td>113.0</td>\n",
+       "      <td>3.85</td>\n",
+       "      <td>3.49</td>\n",
+       "      <td>0.24</td>\n",
+       "      <td>2.18</td>\n",
+       "      <td>7.80</td>\n",
+       "      <td>0.86</td>\n",
+       "      <td>3.45</td>\n",
+       "      <td>1480.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>13.24</td>\n",
+       "      <td>2.59</td>\n",
+       "      <td>2.87</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>118.0</td>\n",
+       "      <td>2.80</td>\n",
+       "      <td>2.69</td>\n",
+       "      <td>0.39</td>\n",
+       "      <td>1.82</td>\n",
+       "      <td>4.32</td>\n",
+       "      <td>1.04</td>\n",
+       "      <td>2.93</td>\n",
+       "      <td>735.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>173</th>\n",
+       "      <td>13.71</td>\n",
+       "      <td>5.65</td>\n",
+       "      <td>2.45</td>\n",
+       "      <td>20.5</td>\n",
+       "      <td>95.0</td>\n",
+       "      <td>1.68</td>\n",
+       "      <td>0.61</td>\n",
+       "      <td>0.52</td>\n",
+       "      <td>1.06</td>\n",
+       "      <td>7.70</td>\n",
+       "      <td>0.64</td>\n",
+       "      <td>1.74</td>\n",
+       "      <td>740.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>174</th>\n",
+       "      <td>13.40</td>\n",
+       "      <td>3.91</td>\n",
+       "      <td>2.48</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>102.0</td>\n",
+       "      <td>1.80</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>0.43</td>\n",
+       "      <td>1.41</td>\n",
+       "      <td>7.30</td>\n",
+       "      <td>0.70</td>\n",
+       "      <td>1.56</td>\n",
+       "      <td>750.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>175</th>\n",
+       "      <td>13.27</td>\n",
+       "      <td>4.28</td>\n",
+       "      <td>2.26</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>120.0</td>\n",
+       "      <td>1.59</td>\n",
+       "      <td>0.69</td>\n",
+       "      <td>0.43</td>\n",
+       "      <td>1.35</td>\n",
+       "      <td>10.20</td>\n",
+       "      <td>0.59</td>\n",
+       "      <td>1.56</td>\n",
+       "      <td>835.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>176</th>\n",
+       "      <td>13.17</td>\n",
+       "      <td>2.59</td>\n",
+       "      <td>2.37</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>120.0</td>\n",
+       "      <td>1.65</td>\n",
+       "      <td>0.68</td>\n",
+       "      <td>0.53</td>\n",
+       "      <td>1.46</td>\n",
+       "      <td>9.30</td>\n",
+       "      <td>0.60</td>\n",
+       "      <td>1.62</td>\n",
+       "      <td>840.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>177</th>\n",
+       "      <td>14.13</td>\n",
+       "      <td>4.10</td>\n",
+       "      <td>2.74</td>\n",
+       "      <td>24.5</td>\n",
+       "      <td>96.0</td>\n",
+       "      <td>2.05</td>\n",
+       "      <td>0.76</td>\n",
+       "      <td>0.56</td>\n",
+       "      <td>1.35</td>\n",
+       "      <td>9.20</td>\n",
+       "      <td>0.61</td>\n",
+       "      <td>1.60</td>\n",
+       "      <td>560.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>178 rows × 14 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     alcohol  malic_acid   ash  alcalinity_of_ash  magnesium  total_phenols  \\\n",
+       "0      14.23        1.71  2.43               15.6      127.0           2.80   \n",
+       "1      13.20        1.78  2.14               11.2      100.0           2.65   \n",
+       "2      13.16        2.36  2.67               18.6      101.0           2.80   \n",
+       "3      14.37        1.95  2.50               16.8      113.0           3.85   \n",
+       "4      13.24        2.59  2.87               21.0      118.0           2.80   \n",
+       "..       ...         ...   ...                ...        ...            ...   \n",
+       "173    13.71        5.65  2.45               20.5       95.0           1.68   \n",
+       "174    13.40        3.91  2.48               23.0      102.0           1.80   \n",
+       "175    13.27        4.28  2.26               20.0      120.0           1.59   \n",
+       "176    13.17        2.59  2.37               20.0      120.0           1.65   \n",
+       "177    14.13        4.10  2.74               24.5       96.0           2.05   \n",
+       "\n",
+       "     flavanoids  nonflavanoid_phenols  proanthocyanins  color_intensity   hue  \\\n",
+       "0          3.06                  0.28             2.29             5.64  1.04   \n",
+       "1          2.76                  0.26             1.28             4.38  1.05   \n",
+       "2          3.24                  0.30             2.81             5.68  1.03   \n",
+       "3          3.49                  0.24             2.18             7.80  0.86   \n",
+       "4          2.69                  0.39             1.82             4.32  1.04   \n",
+       "..          ...                   ...              ...              ...   ...   \n",
+       "173        0.61                  0.52             1.06             7.70  0.64   \n",
+       "174        0.75                  0.43             1.41             7.30  0.70   \n",
+       "175        0.69                  0.43             1.35            10.20  0.59   \n",
+       "176        0.68                  0.53             1.46             9.30  0.60   \n",
+       "177        0.76                  0.56             1.35             9.20  0.61   \n",
+       "\n",
+       "     od280/od315_of_diluted_wines  proline  class  \n",
+       "0                            3.92   1065.0      0  \n",
+       "1                            3.40   1050.0      0  \n",
+       "2                            3.17   1185.0      0  \n",
+       "3                            3.45   1480.0      0  \n",
+       "4                            2.93    735.0      0  \n",
+       "..                            ...      ...    ...  \n",
+       "173                          1.74    740.0      2  \n",
+       "174                          1.56    750.0      2  \n",
+       "175                          1.56    835.0      2  \n",
+       "176                          1.62    840.0      2  \n",
+       "177                          1.60    560.0      2  \n",
+       "\n",
+       "[178 rows x 14 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "from sklearn.datasets import load_wine\n",
     "\n",
@@ -91,12 +369,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "56916892",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "178"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your answer here"
+    "# count number of rows\n",
+    "wine_df.shape[0]"
    ]
   },
   {
@@ -109,12 +399,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "df0ef103",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "14"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your answer here"
+    "# count number of columns\n",
+    "wine_df.shape[1]"
    ]
   },
   {
@@ -127,12 +429,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "47989426",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dtype('int32')"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your answer here"
+    "# data type of column class\n",
+    "wine_df.dtypes['class']"
    ]
   },
   {
@@ -146,12 +460,329 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "bd7b0910",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 178 entries, 0 to 177\n",
+      "Data columns (total 14 columns):\n",
+      " #   Column                        Non-Null Count  Dtype  \n",
+      "---  ------                        --------------  -----  \n",
+      " 0   alcohol                       178 non-null    float64\n",
+      " 1   malic_acid                    178 non-null    float64\n",
+      " 2   ash                           178 non-null    float64\n",
+      " 3   alcalinity_of_ash             178 non-null    float64\n",
+      " 4   magnesium                     178 non-null    float64\n",
+      " 5   total_phenols                 178 non-null    float64\n",
+      " 6   flavanoids                    178 non-null    float64\n",
+      " 7   nonflavanoid_phenols          178 non-null    float64\n",
+      " 8   proanthocyanins               178 non-null    float64\n",
+      " 9   color_intensity               178 non-null    float64\n",
+      " 10  hue                           178 non-null    float64\n",
+      " 11  od280/od315_of_diluted_wines  178 non-null    float64\n",
+      " 12  proline                       178 non-null    float64\n",
+      " 13  class                         178 non-null    int32  \n",
+      "dtypes: float64(13), int32(1)\n",
+      "memory usage: 18.9 KB\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your answer here"
+    "# Number of predictor variables is 12\n",
+    "wine_df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "156cc83a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>alcohol</th>\n",
+       "      <th>malic_acid</th>\n",
+       "      <th>ash</th>\n",
+       "      <th>alcalinity_of_ash</th>\n",
+       "      <th>magnesium</th>\n",
+       "      <th>total_phenols</th>\n",
+       "      <th>flavanoids</th>\n",
+       "      <th>nonflavanoid_phenols</th>\n",
+       "      <th>proanthocyanins</th>\n",
+       "      <th>color_intensity</th>\n",
+       "      <th>hue</th>\n",
+       "      <th>od280/od315_of_diluted_wines</th>\n",
+       "      <th>proline</th>\n",
+       "      <th>class</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>14.23</td>\n",
+       "      <td>1.71</td>\n",
+       "      <td>2.43</td>\n",
+       "      <td>15.6</td>\n",
+       "      <td>127.0</td>\n",
+       "      <td>2.80</td>\n",
+       "      <td>3.06</td>\n",
+       "      <td>0.28</td>\n",
+       "      <td>2.29</td>\n",
+       "      <td>5.64</td>\n",
+       "      <td>1.04</td>\n",
+       "      <td>3.92</td>\n",
+       "      <td>1065.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>13.20</td>\n",
+       "      <td>1.78</td>\n",
+       "      <td>2.14</td>\n",
+       "      <td>11.2</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>2.65</td>\n",
+       "      <td>2.76</td>\n",
+       "      <td>0.26</td>\n",
+       "      <td>1.28</td>\n",
+       "      <td>4.38</td>\n",
+       "      <td>1.05</td>\n",
+       "      <td>3.40</td>\n",
+       "      <td>1050.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>13.16</td>\n",
+       "      <td>2.36</td>\n",
+       "      <td>2.67</td>\n",
+       "      <td>18.6</td>\n",
+       "      <td>101.0</td>\n",
+       "      <td>2.80</td>\n",
+       "      <td>3.24</td>\n",
+       "      <td>0.30</td>\n",
+       "      <td>2.81</td>\n",
+       "      <td>5.68</td>\n",
+       "      <td>1.03</td>\n",
+       "      <td>3.17</td>\n",
+       "      <td>1185.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>14.37</td>\n",
+       "      <td>1.95</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>16.8</td>\n",
+       "      <td>113.0</td>\n",
+       "      <td>3.85</td>\n",
+       "      <td>3.49</td>\n",
+       "      <td>0.24</td>\n",
+       "      <td>2.18</td>\n",
+       "      <td>7.80</td>\n",
+       "      <td>0.86</td>\n",
+       "      <td>3.45</td>\n",
+       "      <td>1480.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>13.24</td>\n",
+       "      <td>2.59</td>\n",
+       "      <td>2.87</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>118.0</td>\n",
+       "      <td>2.80</td>\n",
+       "      <td>2.69</td>\n",
+       "      <td>0.39</td>\n",
+       "      <td>1.82</td>\n",
+       "      <td>4.32</td>\n",
+       "      <td>1.04</td>\n",
+       "      <td>2.93</td>\n",
+       "      <td>735.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>173</th>\n",
+       "      <td>13.71</td>\n",
+       "      <td>5.65</td>\n",
+       "      <td>2.45</td>\n",
+       "      <td>20.5</td>\n",
+       "      <td>95.0</td>\n",
+       "      <td>1.68</td>\n",
+       "      <td>0.61</td>\n",
+       "      <td>0.52</td>\n",
+       "      <td>1.06</td>\n",
+       "      <td>7.70</td>\n",
+       "      <td>0.64</td>\n",
+       "      <td>1.74</td>\n",
+       "      <td>740.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>174</th>\n",
+       "      <td>13.40</td>\n",
+       "      <td>3.91</td>\n",
+       "      <td>2.48</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>102.0</td>\n",
+       "      <td>1.80</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>0.43</td>\n",
+       "      <td>1.41</td>\n",
+       "      <td>7.30</td>\n",
+       "      <td>0.70</td>\n",
+       "      <td>1.56</td>\n",
+       "      <td>750.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>175</th>\n",
+       "      <td>13.27</td>\n",
+       "      <td>4.28</td>\n",
+       "      <td>2.26</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>120.0</td>\n",
+       "      <td>1.59</td>\n",
+       "      <td>0.69</td>\n",
+       "      <td>0.43</td>\n",
+       "      <td>1.35</td>\n",
+       "      <td>10.20</td>\n",
+       "      <td>0.59</td>\n",
+       "      <td>1.56</td>\n",
+       "      <td>835.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>176</th>\n",
+       "      <td>13.17</td>\n",
+       "      <td>2.59</td>\n",
+       "      <td>2.37</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>120.0</td>\n",
+       "      <td>1.65</td>\n",
+       "      <td>0.68</td>\n",
+       "      <td>0.53</td>\n",
+       "      <td>1.46</td>\n",
+       "      <td>9.30</td>\n",
+       "      <td>0.60</td>\n",
+       "      <td>1.62</td>\n",
+       "      <td>840.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>177</th>\n",
+       "      <td>14.13</td>\n",
+       "      <td>4.10</td>\n",
+       "      <td>2.74</td>\n",
+       "      <td>24.5</td>\n",
+       "      <td>96.0</td>\n",
+       "      <td>2.05</td>\n",
+       "      <td>0.76</td>\n",
+       "      <td>0.56</td>\n",
+       "      <td>1.35</td>\n",
+       "      <td>9.20</td>\n",
+       "      <td>0.61</td>\n",
+       "      <td>1.60</td>\n",
+       "      <td>560.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>178 rows × 14 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     alcohol  malic_acid   ash  alcalinity_of_ash  magnesium  total_phenols  \\\n",
+       "0      14.23        1.71  2.43               15.6      127.0           2.80   \n",
+       "1      13.20        1.78  2.14               11.2      100.0           2.65   \n",
+       "2      13.16        2.36  2.67               18.6      101.0           2.80   \n",
+       "3      14.37        1.95  2.50               16.8      113.0           3.85   \n",
+       "4      13.24        2.59  2.87               21.0      118.0           2.80   \n",
+       "..       ...         ...   ...                ...        ...            ...   \n",
+       "173    13.71        5.65  2.45               20.5       95.0           1.68   \n",
+       "174    13.40        3.91  2.48               23.0      102.0           1.80   \n",
+       "175    13.27        4.28  2.26               20.0      120.0           1.59   \n",
+       "176    13.17        2.59  2.37               20.0      120.0           1.65   \n",
+       "177    14.13        4.10  2.74               24.5       96.0           2.05   \n",
+       "\n",
+       "     flavanoids  nonflavanoid_phenols  proanthocyanins  color_intensity   hue  \\\n",
+       "0          3.06                  0.28             2.29             5.64  1.04   \n",
+       "1          2.76                  0.26             1.28             4.38  1.05   \n",
+       "2          3.24                  0.30             2.81             5.68  1.03   \n",
+       "3          3.49                  0.24             2.18             7.80  0.86   \n",
+       "4          2.69                  0.39             1.82             4.32  1.04   \n",
+       "..          ...                   ...              ...              ...   ...   \n",
+       "173        0.61                  0.52             1.06             7.70  0.64   \n",
+       "174        0.75                  0.43             1.41             7.30  0.70   \n",
+       "175        0.69                  0.43             1.35            10.20  0.59   \n",
+       "176        0.68                  0.53             1.46             9.30  0.60   \n",
+       "177        0.76                  0.56             1.35             9.20  0.61   \n",
+       "\n",
+       "     od280/od315_of_diluted_wines  proline  class  \n",
+       "0                            3.92   1065.0      0  \n",
+       "1                            3.40   1050.0      0  \n",
+       "2                            3.17   1185.0      0  \n",
+       "3                            3.45   1480.0      0  \n",
+       "4                            2.93    735.0      0  \n",
+       "..                            ...      ...    ...  \n",
+       "173                          1.74    740.0      2  \n",
+       "174                          1.56    750.0      2  \n",
+       "175                          1.56    835.0      2  \n",
+       "176                          1.62    840.0      2  \n",
+       "177                          1.60    560.0      2  \n",
+       "\n",
+       "[178 rows x 14 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "wine_df"
    ]
   },
   {
@@ -175,10 +806,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "cc899b59",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    alcohol  malic_acid       ash  alcalinity_of_ash  magnesium  \\\n",
+      "0  1.518613   -0.562250  0.232053          -1.169593   1.913905   \n",
+      "1  0.246290   -0.499413 -0.827996          -2.490847   0.018145   \n",
+      "2  0.196879    0.021231  1.109334          -0.268738   0.088358   \n",
+      "3  1.691550   -0.346811  0.487926          -0.809251   0.930918   \n",
+      "4  0.295700    0.227694  1.840403           0.451946   1.281985   \n",
+      "\n",
+      "   total_phenols  flavanoids  nonflavanoid_phenols  proanthocyanins  \\\n",
+      "0       0.808997    1.034819             -0.659563         1.224884   \n",
+      "1       0.568648    0.733629             -0.820719        -0.544721   \n",
+      "2       0.808997    1.215533             -0.498407         2.135968   \n",
+      "3       2.491446    1.466525             -0.981875         1.032155   \n",
+      "4       0.808997    0.663351              0.226796         0.401404   \n",
+      "\n",
+      "   color_intensity       hue  od280/od315_of_diluted_wines   proline  \n",
+      "0         0.251717  0.362177                      1.847920  1.013009  \n",
+      "1        -0.293321  0.406051                      1.113449  0.965242  \n",
+      "2         0.269020  0.318304                      0.788587  1.395148  \n",
+      "3         1.186068 -0.427544                      1.184071  2.334574  \n",
+      "4        -0.319276  0.362177                      0.449601 -0.037874  \n"
+     ]
+    }
+   ],
    "source": [
     "# Select predictors (excluding the last column)\n",
     "predictors = wine_df.iloc[:, :-1]\n",
@@ -204,7 +862,7 @@
    "id": "403ef0bb",
    "metadata": {},
    "source": [
-    "> Your answer here..."
+    "> To make sure all the predictor valiables to have same scale therefore none of them will be dominated because of large scale and skew the classifcation result when using machine learning models that rely on distance metrics."
    ]
   },
   {
@@ -220,7 +878,7 @@
    "id": "fdee5a15",
    "metadata": {},
    "source": [
-    "> Your answer here..."
+    "> This is the variable we want to determine through the model and its scale would not affect the classification result  "
    ]
   },
   {
@@ -236,7 +894,7 @@
    "id": "f0676c21",
    "metadata": {},
    "source": [
-    "> Your answer here..."
+    "> Setting random seeed is important because it allow us to control the randomness in our code. Therefore we can repoduce the same result after running the code and do comparison or testing."
    ]
   },
   {
@@ -251,19 +909,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "72c101f2",
+   "execution_count": 9,
+   "id": "8e4a9dda",
    "metadata": {},
    "outputs": [],
    "source": [
     "# set a seed for reproducibility\n",
     "np.random.seed(123)\n",
-    "\n",
     "# split the data into a training and testing set. hint: use train_test_split !\n",
-    "\n",
     "# Your code here ..."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "af2f9ce3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# split the data into training and testing set\n",
+    "\n",
+    "predictor_S_train, predictor_S_test, label_c_train, label_c_test= train_test_split(\n",
+    "    predictors_standardized, wine_df['class'], train_size=0.75, shuffle= True,\n",
+    "    stratify=wine_df[\"class\"], \n",
+    "    random_state= 123\n",
+    ")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "4604ee03",
@@ -282,14 +954,1125 @@
     "4. After fitting the model on the training data, identify and return the best value for `n_neighbors` based on the grid search results."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "905ed370",
+   "metadata": {},
+   "source": [
+    "Question 3 - point 1"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "id": "08818c64",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your code here..."
+    "# initiate KNN\n",
+    "knn = KNeighborsClassifier(n_neighbors=5)\n",
+    "                           "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "42b204f2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# define x and y for KNN\n",
+    "X_train = predictor_S_train\n",
+    "y_train = label_c_train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "44a9ab17",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;KNeighborsClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsClassifier()</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "KNeighborsClassifier()"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# fitting KNN\n",
+    "knn.fit(X_train,y_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9ffb8bf8",
+   "metadata": {},
+   "source": [
+    "Question 3  point 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "58c21754",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# implementing a gridSearch , define pararmeter grid, riging from 1 to 50\n",
+    "parameter_grid = {\n",
+    "    \"n_neighbors\": range(1, 50, 3),\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ddf8185b",
+   "metadata": {},
+   "source": [
+    "Question 3 point 3 "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "b3ad80ad",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# use function to search best K -- implementing a gridSearch \n",
+    "wine_tune_grid = GridSearchCV(\n",
+    "    estimator=knn,\n",
+    "    param_grid=parameter_grid,\n",
+    "    cv=10\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "672c7471",
+   "metadata": {},
+   "source": [
+    "Question 3 - point 4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "9fcf66a5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-2 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-2 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-2 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-2 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=10, estimator=KNeighborsClassifier(),\n",
+       "             param_grid={&#x27;n_neighbors&#x27;: range(1, 50, 3)})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=10, estimator=KNeighborsClassifier(),\n",
+       "             param_grid={&#x27;n_neighbors&#x27;: range(1, 50, 3)})</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">best_estimator_: KNeighborsClassifier</label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsClassifier(n_neighbors=7)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;KNeighborsClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsClassifier(n_neighbors=7)</pre></div> </div></div></div></div></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "GridSearchCV(cv=10, estimator=KNeighborsClassifier(),\n",
+       "             param_grid={'n_neighbors': range(1, 50, 3)})"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# fitting the x and y into the tune function\n",
+    "wine_tune_grid.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "e0cca0de",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>params</th>\n",
+       "      <th>mean_test_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>{'n_neighbors': 1}</td>\n",
+       "      <td>0.954396</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>{'n_neighbors': 4}</td>\n",
+       "      <td>0.954945</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>{'n_neighbors': 7}</td>\n",
+       "      <td>0.977473</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>{'n_neighbors': 10}</td>\n",
+       "      <td>0.954396</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>{'n_neighbors': 13}</td>\n",
+       "      <td>0.977473</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>{'n_neighbors': 16}</td>\n",
+       "      <td>0.962637</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>{'n_neighbors': 19}</td>\n",
+       "      <td>0.962637</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>{'n_neighbors': 22}</td>\n",
+       "      <td>0.970330</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>{'n_neighbors': 25}</td>\n",
+       "      <td>0.954945</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>{'n_neighbors': 28}</td>\n",
+       "      <td>0.962637</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>{'n_neighbors': 31}</td>\n",
+       "      <td>0.955495</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>{'n_neighbors': 34}</td>\n",
+       "      <td>0.963187</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>{'n_neighbors': 37}</td>\n",
+       "      <td>0.962637</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>{'n_neighbors': 40}</td>\n",
+       "      <td>0.954945</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>{'n_neighbors': 43}</td>\n",
+       "      <td>0.954945</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>{'n_neighbors': 46}</td>\n",
+       "      <td>0.947253</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>{'n_neighbors': 49}</td>\n",
+       "      <td>0.947253</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 params  mean_test_score\n",
+       "0    {'n_neighbors': 1}         0.954396\n",
+       "1    {'n_neighbors': 4}         0.954945\n",
+       "2    {'n_neighbors': 7}         0.977473\n",
+       "3   {'n_neighbors': 10}         0.954396\n",
+       "4   {'n_neighbors': 13}         0.977473\n",
+       "5   {'n_neighbors': 16}         0.962637\n",
+       "6   {'n_neighbors': 19}         0.962637\n",
+       "7   {'n_neighbors': 22}         0.970330\n",
+       "8   {'n_neighbors': 25}         0.954945\n",
+       "9   {'n_neighbors': 28}         0.962637\n",
+       "10  {'n_neighbors': 31}         0.955495\n",
+       "11  {'n_neighbors': 34}         0.963187\n",
+       "12  {'n_neighbors': 37}         0.962637\n",
+       "13  {'n_neighbors': 40}         0.954945\n",
+       "14  {'n_neighbors': 43}         0.954945\n",
+       "15  {'n_neighbors': 46}         0.947253\n",
+       "16  {'n_neighbors': 49}         0.947253"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# check out the accuracy and showing just n_neighbour and test score\n",
+    "accuracies_grid = pd.DataFrame(wine_tune_grid.cv_results_)\n",
+    "#accuracies_grid\n",
+    "accuracies_grid [[\"params\",\"mean_test_score\"]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "369cdf3b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'n_neighbors': 7}"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# optimal number of neighbours\n",
+    "wine_tune_grid.best_params_"
    ]
   },
   {
@@ -305,12 +2088,821 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "id": "ffefa9f2",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# Your code here..."
+    "# Create the plot- to show the best K value\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "\n",
+    "\n",
+    "# Plot mean test scores with error bars\n",
+    "plt.plot(accuracies_grid['param_n_neighbors'], accuracies_grid['mean_test_score'], '-o', color='blue')\n",
+    "\n",
+    "# Add labels and legend\n",
+    "plt.xlabel('Number of Neighbors')\n",
+    "plt.ylabel('Accuracy estimate')\n",
+    "plt.title('K-Nearest Neighbors Performance')\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "a698cfd3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-3 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-3 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-3 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-3 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-3 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=7)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;KNeighborsClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsClassifier(n_neighbors=7)</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "KNeighborsClassifier(n_neighbors=7)"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Initiate the KNN with the best 'n_neighbour' found\n",
+    "knn = KNeighborsClassifier(n_neighbors=wine_tune_grid.best_params_['n_neighbors'])\n",
+    "\n",
+    "# Perform the KNN on test set and find the predication . Define x and y \n",
+    "X_test = predictor_S_test\n",
+    "y_test = label_c_test\n",
+    "\n",
+    "# fitting KNN into the test set\n",
+    "knn.fit(X_test,y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "f6bad3b4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>class</th>\n",
+       "      <th>test_prediction</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>102</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>84</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>96</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>65</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>79</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>109</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>113</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>159</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>125</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>115</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>71</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>76</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>131</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>60</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>114</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>47</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>158</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>133</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>137</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>154</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>136</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>168</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>117</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>108</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>77</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>142</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>85</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>58</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>175</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>42</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>143</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>177</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     class  test_prediction\n",
+       "102      1                1\n",
+       "84       1                1\n",
+       "96       1                1\n",
+       "65       1                1\n",
+       "79       1                1\n",
+       "17       0                0\n",
+       "109      1                1\n",
+       "113      1                1\n",
+       "28       0                0\n",
+       "159      2                2\n",
+       "38       0                0\n",
+       "34       0                0\n",
+       "125      1                1\n",
+       "115      1                1\n",
+       "71       1                1\n",
+       "76       1                1\n",
+       "131      2                2\n",
+       "33       0                0\n",
+       "60       1                1\n",
+       "19       0                0\n",
+       "114      1                1\n",
+       "47       0                0\n",
+       "48       0                0\n",
+       "158      2                2\n",
+       "133      2                2\n",
+       "137      2                2\n",
+       "154      2                2\n",
+       "136      2                2\n",
+       "2        0                0\n",
+       "168      2                2\n",
+       "117      1                1\n",
+       "32       0                0\n",
+       "22       0                0\n",
+       "108      1                1\n",
+       "73       1                0\n",
+       "77       1                1\n",
+       "142      2                2\n",
+       "9        0                0\n",
+       "85       1                1\n",
+       "58       0                0\n",
+       "45       0                0\n",
+       "175      2                2\n",
+       "42       0                0\n",
+       "143      2                2\n",
+       "177      2                2"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# incorporate the test prediction into the test data set and compare\n",
+    "\n",
+    "# concatnate the test data into one dataframe \n",
+    "full_test_data = pd.concat([X_test,y_test], axis=1)\n",
+    "full_test_data\n",
+    "\n",
+    "# Using knn predict to predict result and show it in one df\n",
+    "full_test_data[\"test_prediction\"] = knn.predict(X_test)\n",
+    "full_test_data[[\"class\",\"test_prediction\"]]\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "fbda1bd4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9777777777777777"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# checking the prediction accuracy using Knn score method for accuracy\n",
+    "knn.score(X_test,y_test)"
    ]
   },
   {
@@ -365,7 +2957,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.10.4",
+   "display_name": "dsi_participant",
    "language": "python",
    "name": "python3"
   },
@@ -379,12 +2971,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.19"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "497a84dc8fec8cf8d24e7e87b6d954c9a18a327edc66feb9b9ea7e9e72cc5c7e"
-   }
+   "version": "3.9.15"
   }
  },
  "nbformat": 4,
diff --git a/02_activities/assignments/assignment_1_cohort 4.ipynb b/02_activities/assignments/assignment_1_cohort 4.ipynb
new file mode 100644
index 000000000..0e2091c8c
--- /dev/null
+++ b/02_activities/assignments/assignment_1_cohort 4.ipynb	
@@ -0,0 +1,455 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "7b0bcac6-5086-4f4e-928a-570a9ff7ae58",
+   "metadata": {},
+   "source": [
+    "# Assignment 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5fce0350-2a17-4e93-8d4c-0b8748fdfc32",
+   "metadata": {},
+   "source": [
+    "You only need to write one line of code for each question. When answering questions that ask you to identify or interpret something, the length of your response doesn’t matter. For example, if the answer is just ‘yes,’ ‘no,’ or a number, you can just give that answer without adding anything else.\n",
+    "\n",
+    "We will go through comparable code and concepts in the live learning session. If you run into trouble, start by using the help `help()` function in Python, to get information about the datasets and function in question. The internet is also a great resource when coding (though note that **no outside searches are required by the assignment!**). If you do incorporate code from the internet, please cite the source within your code (providing a URL is sufficient).\n",
+    "\n",
+    "Please bring questions that you cannot work out on your own to office hours, work periods or share with your peers on Slack. We will work with you through the issue."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5fc5001c-7715-4ebe-b0f7-e4bd04349629",
+   "metadata": {},
+   "source": [
+    "### Classification using KNN\n",
+    "\n",
+    "Let's set up our workspace and use the **Wine dataset** from `scikit-learn`. This dataset contains 178 wine samples with 13 chemical features, used to classify wines into different classes based on their origin.\n",
+    "\n",
+    "The **response variable** is `class`, which indicates the type of wine. We'll use all of the chemical features to predict this response variable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "4a3485d6-ba58-4660-a983-5680821c5719",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'pandas'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[6], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# Import standard libraries\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpd\u001b[39;00m\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[0;32m      4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mrandom\u001b[39;00m\n",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'pandas'"
+     ]
+    }
+   ],
+   "source": [
+    "# Import standard libraries\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import random\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.colors as mcolors\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.metrics import recall_score, precision_score\n",
+    "from sklearn.model_selection import cross_validate\n",
+    "from sklearn.model_selection import GridSearchCV\n",
+    "from sklearn.metrics import accuracy_score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "a431d282-f9ca-4d5d-8912-71ffc9d8ea19",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'sklearn'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[2], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdatasets\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m load_wine\n\u001b[0;32m      3\u001b[0m \u001b[38;5;66;03m# Load the Wine dataset\u001b[39;00m\n\u001b[0;32m      4\u001b[0m wine_data \u001b[38;5;241m=\u001b[39m load_wine()\n",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'sklearn'"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.datasets import load_wine\n",
+    "\n",
+    "# Load the Wine dataset\n",
+    "wine_data = load_wine()\n",
+    "\n",
+    "# Convert to DataFrame\n",
+    "wine_df = pd.DataFrame(wine_data.data, columns=wine_data.feature_names)\n",
+    "\n",
+    "# Bind the 'class' (wine target) to the DataFrame\n",
+    "wine_df['class'] = wine_data.target\n",
+    "\n",
+    "# Display the DataFrame\n",
+    "wine_df\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "721b2b17",
+   "metadata": {},
+   "source": [
+    "#### **Question 1:** \n",
+    "#### Data inspection\n",
+    "\n",
+    "Before fitting any model, it is essential to understand our data. **Use Python code** to answer the following questions about the **Wine dataset**:\n",
+    "\n",
+    "_(i)_ How many observations (rows) does the dataset contain?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "56916892",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your answer here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f7573b59",
+   "metadata": {},
+   "source": [
+    "_(ii)_ How many variables (columns) does the dataset contain?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "df0ef103",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your answer here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cb5180c7",
+   "metadata": {},
+   "source": [
+    "_(iii)_ What is the 'variable type' of the response variable `class` (e.g., 'integer', 'category', etc.)? What are the 'levels' (unique values) of the variable?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "47989426",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your answer here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a25f5e1b",
+   "metadata": {},
+   "source": [
+    "\n",
+    "_(iv)_ How many predictor variables do we have (Hint: all variables other than `class`)? "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bd7b0910",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your answer here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d631e8e3",
+   "metadata": {},
+   "source": [
+    "You can use `print()` and `describe()` to help answer these questions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fa3832d7",
+   "metadata": {},
+   "source": [
+    "#### **Question 2:** \n",
+    "#### Standardization and data-splitting\n",
+    "\n",
+    "Next, we must preform 'pre-processing' or 'data munging', to prepare our data for classification/prediction. For KNN, there are three essential steps. A first essential step is to 'standardize' the predictor variables. We can achieve this using the scaler method, provided as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "cc899b59",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'wine_df' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[3], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# Select predictors (excluding the last column)\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m predictors \u001b[38;5;241m=\u001b[39m \u001b[43mwine_df\u001b[49m\u001b[38;5;241m.\u001b[39miloc[:, :\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]\n\u001b[0;32m      4\u001b[0m \u001b[38;5;66;03m# Standardize the predictors\u001b[39;00m\n\u001b[0;32m      5\u001b[0m scaler \u001b[38;5;241m=\u001b[39m StandardScaler()\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'wine_df' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "# Select predictors (excluding the last column)\n",
+    "predictors = wine_df.iloc[:, :-1]\n",
+    "\n",
+    "# Standardize the predictors\n",
+    "scaler = StandardScaler()\n",
+    "predictors_standardized = pd.DataFrame(scaler.fit_transform(predictors), columns=predictors.columns)\n",
+    "\n",
+    "# Display the head of the standardized predictors\n",
+    "print(predictors_standardized.head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9981ca48",
+   "metadata": {},
+   "source": [
+    "(i) Why is it important to standardize the predictor variables?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "403ef0bb",
+   "metadata": {},
+   "source": [
+    "> Your answer here..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8e2e1bea",
+   "metadata": {},
+   "source": [
+    "(ii) Why did we elect not to standard our response variable `Class`?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fdee5a15",
+   "metadata": {},
+   "source": [
+    "> Your answer here..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8077ec21",
+   "metadata": {},
+   "source": [
+    "(iii) A second essential step is to set a random seed. Do so below (Hint: use the random.seed function). Why is setting a seed important? Is the particular seed value important? Why or why not?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f0676c21",
+   "metadata": {},
+   "source": [
+    "Your answer here..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "df9de570",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'np' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[4], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mnp\u001b[49m\u001b[38;5;241m.\u001b[39mrandom\u001b[38;5;241m.\u001b[39mseed(\u001b[38;5;241m100\u001b[39m)\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'np' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "np.random.seed(100)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "36ab9229",
+   "metadata": {},
+   "source": [
+    "(iv) A third essential step is to split our standardized data into separate training and testing sets. We will split into 75% training and 25% testing. The provided code randomly partitions our data, and creates linked training sets for the predictors and response variables. \n",
+    "\n",
+    "Extend the code to create a non-overlapping test set for the predictors and response variables."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "72c101f2",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'np' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[5], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# Do not touch\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m \u001b[43mnp\u001b[49m\u001b[38;5;241m.\u001b[39mrandom\u001b[38;5;241m.\u001b[39mseed(\u001b[38;5;241m123\u001b[39m)\n\u001b[0;32m      3\u001b[0m \u001b[38;5;66;03m# Create a random vector of True and False values to split the data\u001b[39;00m\n\u001b[0;32m      4\u001b[0m split \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mrandom\u001b[38;5;241m.\u001b[39mchoice([\u001b[38;5;28;01mTrue\u001b[39;00m, \u001b[38;5;28;01mFalse\u001b[39;00m], size\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mlen\u001b[39m(predictors_standardized), replace\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, p\u001b[38;5;241m=\u001b[39m[\u001b[38;5;241m0.75\u001b[39m, \u001b[38;5;241m0.25\u001b[39m])\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'np' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "# Do not touch\n",
+    "np.random.seed(123)\n",
+    "# Create a random vector of True and False values to split the data\n",
+    "split = np.random.choice([True, False], size=len(predictors_standardized), replace=True, p=[0.75, 0.25])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4604ee03",
+   "metadata": {},
+   "source": [
+    "#### **Question 3:**\n",
+    "#### Model initialization and cross-validation\n",
+    "We are finally set to fit the KNN model. \n",
+    "\n",
+    "\n",
+    "Perform a grid search to tune the `n_neighbors` hyperparameter using 10-fold cross-validation. Follow these steps:\n",
+    "\n",
+    "1. Initialize the KNN classifier using `KNeighborsClassifier()`.\n",
+    "2. Define a parameter grid for `n_neighbors` ranging from 1 to 50.\n",
+    "3. Implement a grid search using `GridSearchCV` with 10-fold cross-validation to find the optimal number of neighbors.\n",
+    "4. After fitting the model on the training data, identify and return the best value for `n_neighbors` based on the grid search results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "08818c64",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your code here..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3f76bf62",
+   "metadata": {},
+   "source": [
+    "#### **Question 4:**\n",
+    "#### Model evaluation\n",
+    "\n",
+    "Using the best value for `n_neighbors`, fit a KNN model on the training data and evaluate its performance on the test set using `accuracy_score`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ffefa9f2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your code here..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6f8a69db",
+   "metadata": {},
+   "source": [
+    "# Criteria\n",
+    "\n",
+    "\n",
+    "| **Criteria**                                           | **Complete**                                      | **Incomplete**                                    |\n",
+    "|--------------------------------------------------------|---------------------------------------------------|--------------------------------------------------|\n",
+    "| **Data Inspection**                                    | Data is inspected for number of variables, observations and data types. | Data inspection is missing or incomplete.         |\n",
+    "| **Data Scaling**                                       | Data scaling or normalization is applied where necessary (e.g., using `StandardScaler`). | Data scaling or normalization is missing or incorrectly applied. |\n",
+    "| **Model Initialization**                               | The KNN model is correctly initialized and a random seed is set for reproducibility.            | The KNN model is not initialized, is incorrect, or lacks a random seed for reproducibility. |\n",
+    "| **Parameter Grid for `n_neighbors`**                   | The parameter grid for `n_neighbors` is correctly defined. | The parameter grid is missing or incorrectly defined. |\n",
+    "| **Cross-Validation Setup**                             | Cross-validation is set up correctly with 10 folds. | Cross-validation is missing or incorrectly set up. |\n",
+    "| **Best Hyperparameter (`n_neighbors`) Selection**       | The best value for `n_neighbors` is identified using the grid search results. | The best `n_neighbors` is not selected or incorrect. |\n",
+    "| **Model Evaluation on Test Data**                      | The model is evaluated on the test data using accuracy. | The model evaluation is missing or uses the wrong metric. |\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0b4390cc",
+   "metadata": {},
+   "source": [
+    "## Submission Information\n",
+    "\n",
+    "🚨 **Please review our [Assignment Submission Guide](https://github.com/UofT-DSI/onboarding/blob/main/onboarding_documents/submissions.md)** 🚨 for detailed instructions on how to format, branch, and submit your work. Following these guidelines is crucial for your submissions to be evaluated correctly.\n",
+    "\n",
+    "### Note:\n",
+    "\n",
+    "If you like, you may collaborate with others in the cohort. If you choose to do so, please indicate with whom you have worked with in your pull request by tagging their GitHub username. Separate submissions are required.\n",
+    "\n",
+    "### Submission Parameters:\n",
+    "* Submission Due Date: `HH:MM AM/PM - DD/MM/YYYY`\n",
+    "* The branch name for your repo should be: `assignment-1`\n",
+    "* What to submit for this assignment:\n",
+    "    * This Jupyter Notebook (assignment_1.ipynb) should be populated and should be the only change in your pull request.\n",
+    "* What the pull request link should look like for this assignment: `https://github.com/<your_github_username>/applying_statistical_concepts/pull/<pr_id>`\n",
+    "    * Open a private window in your browser. Copy and paste the link to your pull request into the address bar. Make sure you can see your pull request properly. This helps the technical facilitator and learning support staff review your submission easily.\n",
+    "\n",
+    "Checklist:\n",
+    "- [ ] Created a branch with the correct naming convention.\n",
+    "- [ ] Ensured that the repository is public.\n",
+    "- [ ] Reviewed the PR description guidelines and adhered to them.\n",
+    "- [ ] Verify that the link is accessible in a private browser window.\n",
+    "\n",
+    "If you encounter any difficulties or have questions, please don't hesitate to reach out to our team via our Slack at `#cohort-4-help`. Our Technical Facilitators and Learning Support staff are here to help you navigate any challenges.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/02_activities/assignments/assignment_1_ori.ipynb b/02_activities/assignments/assignment_1_ori.ipynb
new file mode 100644
index 000000000..094ad69bc
--- /dev/null
+++ b/02_activities/assignments/assignment_1_ori.ipynb
@@ -0,0 +1,385 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "7b0bcac6-5086-4f4e-928a-570a9ff7ae58",
+   "metadata": {},
+   "source": [
+    "# Assignment 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5fce0350-2a17-4e93-8d4c-0b8748fdfc32",
+   "metadata": {},
+   "source": [
+    "You only need to write one line of code for each question. When answering questions that ask you to identify or interpret something, the length of your response doesn’t matter. For example, if the answer is just ‘yes,’ ‘no,’ or a number, you can just give that answer without adding anything else.\n",
+    "\n",
+    "We will go through comparable code and concepts in the live learning session. If you run into trouble, start by using the help `help()` function in Python, to get information about the datasets and function in question. The internet is also a great resource when coding (though note that **no outside searches are required by the assignment!**). If you do incorporate code from the internet, please cite the source within your code (providing a URL is sufficient).\n",
+    "\n",
+    "Please bring questions that you cannot work out on your own to office hours, work periods or share with your peers on Slack. We will work with you through the issue."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5fc5001c-7715-4ebe-b0f7-e4bd04349629",
+   "metadata": {},
+   "source": [
+    "### Classification using KNN\n",
+    "\n",
+    "Let's set up our workspace and use the **Wine dataset** from `scikit-learn`. This dataset contains 178 wine samples with 13 chemical features, used to classify wines into different classes based on their origin.\n",
+    "\n",
+    "The **response variable** is `class`, which indicates the type of wine. We'll use all of the chemical features to predict this response variable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4a3485d6-ba58-4660-a983-5680821c5719",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import standard libraries\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import random\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.colors as mcolors\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.metrics import recall_score, precision_score\n",
+    "from sklearn.model_selection import cross_validate\n",
+    "from sklearn.model_selection import GridSearchCV\n",
+    "from sklearn.metrics import accuracy_score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a431d282-f9ca-4d5d-8912-71ffc9d8ea19",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.datasets import load_wine\n",
+    "\n",
+    "# Load the Wine dataset\n",
+    "wine_data = load_wine()\n",
+    "\n",
+    "# Convert to DataFrame\n",
+    "wine_df = pd.DataFrame(wine_data.data, columns=wine_data.feature_names)\n",
+    "\n",
+    "# Bind the 'class' (wine target) to the DataFrame\n",
+    "wine_df['class'] = wine_data.target\n",
+    "\n",
+    "# Display the DataFrame\n",
+    "wine_df\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "721b2b17",
+   "metadata": {},
+   "source": [
+    "#### **Question 1:** \n",
+    "#### Data inspection\n",
+    "\n",
+    "Before fitting any model, it is essential to understand our data. **Use Python code** to answer the following questions about the **Wine dataset**:\n",
+    "\n",
+    "_(i)_ How many observations (rows) does the dataset contain?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "56916892",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your answer here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f7573b59",
+   "metadata": {},
+   "source": [
+    "_(ii)_ How many variables (columns) does the dataset contain?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "df0ef103",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your answer here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cb5180c7",
+   "metadata": {},
+   "source": [
+    "_(iii)_ What is the 'variable type' of the response variable `class` (e.g., 'integer', 'category', etc.)? What are the 'levels' (unique values) of the variable?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "47989426",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your answer here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a25f5e1b",
+   "metadata": {},
+   "source": [
+    "\n",
+    "_(iv)_ How many predictor variables do we have (Hint: all variables other than `class`)? "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bd7b0910",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your answer here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d631e8e3",
+   "metadata": {},
+   "source": [
+    "You can use `print()` and `describe()` to help answer these questions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fa3832d7",
+   "metadata": {},
+   "source": [
+    "#### **Question 2:** \n",
+    "#### Standardization and data-splitting\n",
+    "\n",
+    "Next, we must preform 'pre-processing' or 'data munging', to prepare our data for classification/prediction. For KNN, there are three essential steps. A first essential step is to 'standardize' the predictor variables. We can achieve this using the scaler method, provided as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cc899b59",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Select predictors (excluding the last column)\n",
+    "predictors = wine_df.iloc[:, :-1]\n",
+    "\n",
+    "# Standardize the predictors\n",
+    "scaler = StandardScaler()\n",
+    "predictors_standardized = pd.DataFrame(scaler.fit_transform(predictors), columns=predictors.columns)\n",
+    "\n",
+    "# Display the head of the standardized predictors\n",
+    "print(predictors_standardized.head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9981ca48",
+   "metadata": {},
+   "source": [
+    "(i) Why is it important to standardize the predictor variables?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "403ef0bb",
+   "metadata": {},
+   "source": [
+    "> Your answer here..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8e2e1bea",
+   "metadata": {},
+   "source": [
+    "(ii) Why did we elect not to standard our response variable `Class`?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fdee5a15",
+   "metadata": {},
+   "source": [
+    "> Your answer here..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8077ec21",
+   "metadata": {},
+   "source": [
+    "(iii) A second essential step is to set a random seed. Do so below (Hint: use the random.seed function). Why is setting a seed important? Is the particular seed value important? Why or why not?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f0676c21",
+   "metadata": {},
+   "source": [
+    "> Your answer here..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "36ab9229",
+   "metadata": {},
+   "source": [
+    "(iv) A third essential step is to split our standardized data into separate training and testing sets. We will split into 75% training and 25% testing. The provided code randomly partitions our data, and creates linked training sets for the predictors and response variables. \n",
+    "\n",
+    "Extend the code to create a non-overlapping test set for the predictors and response variables."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "72c101f2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Do not touch\n",
+    "np.random.seed(123)\n",
+    "# Create a random vector of True and False values to split the data\n",
+    "split = np.random.choice([True, False], size=len(predictors_standardized), replace=True, p=[0.75, 0.25])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4604ee03",
+   "metadata": {},
+   "source": [
+    "#### **Question 3:**\n",
+    "#### Model initialization and cross-validation\n",
+    "We are finally set to fit the KNN model. \n",
+    "\n",
+    "\n",
+    "Perform a grid search to tune the `n_neighbors` hyperparameter using 10-fold cross-validation. Follow these steps:\n",
+    "\n",
+    "1. Initialize the KNN classifier using `KNeighborsClassifier()`.\n",
+    "2. Define a parameter grid for `n_neighbors` ranging from 1 to 50.\n",
+    "3. Implement a grid search using `GridSearchCV` with 10-fold cross-validation to find the optimal number of neighbors.\n",
+    "4. After fitting the model on the training data, identify and return the best value for `n_neighbors` based on the grid search results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "08818c64",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your code here..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3f76bf62",
+   "metadata": {},
+   "source": [
+    "#### **Question 4:**\n",
+    "#### Model evaluation\n",
+    "\n",
+    "Using the best value for `n_neighbors`, fit a KNN model on the training data and evaluate its performance on the test set using `accuracy_score`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ffefa9f2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your code here..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6f8a69db",
+   "metadata": {},
+   "source": [
+    "# Criteria\n",
+    "\n",
+    "\n",
+    "| **Criteria**                                           | **Complete**                                      | **Incomplete**                                    |\n",
+    "|--------------------------------------------------------|---------------------------------------------------|--------------------------------------------------|\n",
+    "| **Data Inspection**                                    | Data is inspected for number of variables, observations and data types. | Data inspection is missing or incomplete.         |\n",
+    "| **Data Scaling**                                       | Data scaling or normalization is applied where necessary (e.g., using `StandardScaler`). | Data scaling or normalization is missing or incorrectly applied. |\n",
+    "| **Model Initialization**                               | The KNN model is correctly initialized and a random seed is set for reproducibility.            | The KNN model is not initialized, is incorrect, or lacks a random seed for reproducibility. |\n",
+    "| **Parameter Grid for `n_neighbors`**                   | The parameter grid for `n_neighbors` is correctly defined. | The parameter grid is missing or incorrectly defined. |\n",
+    "| **Cross-Validation Setup**                             | Cross-validation is set up correctly with 10 folds. | Cross-validation is missing or incorrectly set up. |\n",
+    "| **Best Hyperparameter (`n_neighbors`) Selection**       | The best value for `n_neighbors` is identified using the grid search results. | The best `n_neighbors` is not selected or incorrect. |\n",
+    "| **Model Evaluation on Test Data**                      | The model is evaluated on the test data using accuracy. | The model evaluation is missing or uses the wrong metric. |\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0b4390cc",
+   "metadata": {},
+   "source": [
+    "## Submission Information\n",
+    "\n",
+    "🚨 **Please review our [Assignment Submission Guide](https://github.com/UofT-DSI/onboarding/blob/main/onboarding_documents/submissions.md)** 🚨 for detailed instructions on how to format, branch, and submit your work. Following these guidelines is crucial for your submissions to be evaluated correctly.\n",
+    "\n",
+    "### Note:\n",
+    "\n",
+    "If you like, you may collaborate with others in the cohort. If you choose to do so, please indicate with whom you have worked with in your pull request by tagging their GitHub username. Separate submissions are required.\n",
+    "\n",
+    "### Submission Parameters:\n",
+    "* Submission Due Date: `11:59 PM - 01/12/2025`\n",
+    "* The branch name for your repo should be: `assignment-1`\n",
+    "* What to submit for this assignment:\n",
+    "    * This Jupyter Notebook (assignment_1.ipynb) should be populated and should be the only change in your pull request.\n",
+    "* What the pull request link should look like for this assignment: `https://github.com/<your_github_username>/LCR/pull/<pr_id>`\n",
+    "    * Open a private window in your browser. Copy and paste the link to your pull request into the address bar. Make sure you can see your pull request properly. This helps the technical facilitator and learning support staff review your submission easily.\n",
+    "\n",
+    "Checklist:\n",
+    "- [ ] Created a branch with the correct naming convention.\n",
+    "- [ ] Ensured that the repository is public.\n",
+    "- [ ] Reviewed the PR description guidelines and adhered to them.\n",
+    "- [ ] Verify that the link is accessible in a private browser window.\n",
+    "\n",
+    "If you encounter any difficulties or have questions, please don't hesitate to reach out to our team via our Slack at `#cohort-4-help`. Our Technical Facilitators and Learning Support staff are here to help you navigate any challenges.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "dsi_participant",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/02_activities/assignments/assignment_1_test2_1.ipynb b/02_activities/assignments/assignment_1_test2_1.ipynb
new file mode 100644
index 000000000..c1e2b6a7f
--- /dev/null
+++ b/02_activities/assignments/assignment_1_test2_1.ipynb
@@ -0,0 +1,3335 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "7b0bcac6-5086-4f4e-928a-570a9ff7ae58",
+   "metadata": {},
+   "source": [
+    "# Assignment 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5fce0350-2a17-4e93-8d4c-0b8748fdfc32",
+   "metadata": {},
+   "source": [
+    "You only need to write one line of code for each question. When answering questions that ask you to identify or interpret something, the length of your response doesn’t matter. For example, if the answer is just ‘yes,’ ‘no,’ or a number, you can just give that answer without adding anything else.\n",
+    "\n",
+    "We will go through comparable code and concepts in the live learning session. If you run into trouble, start by using the help `help()` function in Python, to get information about the datasets and function in question. The internet is also a great resource when coding (though note that **no outside searches are required by the assignment!**). If you do incorporate code from the internet, please cite the source within your code (providing a URL is sufficient).\n",
+    "\n",
+    "Please bring questions that you cannot work out on your own to office hours, work periods or share with your peers on Slack. We will work with you through the issue."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5fc5001c-7715-4ebe-b0f7-e4bd04349629",
+   "metadata": {},
+   "source": [
+    "### Classification using KNN\n",
+    "\n",
+    "Let's set up our workspace and use the **Wine dataset** from `scikit-learn`. This dataset contains 178 wine samples with 13 chemical features, used to classify wines into different classes based on their origin.\n",
+    "\n",
+    "The **response variable** is `class`, which indicates the type of wine. We'll use all of the chemical features to predict this response variable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "4a3485d6-ba58-4660-a983-5680821c5719",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import standard libraries\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import random\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.colors as mcolors\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.metrics import recall_score, precision_score\n",
+    "from sklearn.model_selection import cross_validate\n",
+    "from sklearn.model_selection import GridSearchCV\n",
+    "from sklearn.metrics import accuracy_score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "a431d282-f9ca-4d5d-8912-71ffc9d8ea19",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>alcohol</th>\n",
+       "      <th>malic_acid</th>\n",
+       "      <th>ash</th>\n",
+       "      <th>alcalinity_of_ash</th>\n",
+       "      <th>magnesium</th>\n",
+       "      <th>total_phenols</th>\n",
+       "      <th>flavanoids</th>\n",
+       "      <th>nonflavanoid_phenols</th>\n",
+       "      <th>proanthocyanins</th>\n",
+       "      <th>color_intensity</th>\n",
+       "      <th>hue</th>\n",
+       "      <th>od280/od315_of_diluted_wines</th>\n",
+       "      <th>proline</th>\n",
+       "      <th>class</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>14.23</td>\n",
+       "      <td>1.71</td>\n",
+       "      <td>2.43</td>\n",
+       "      <td>15.6</td>\n",
+       "      <td>127.0</td>\n",
+       "      <td>2.80</td>\n",
+       "      <td>3.06</td>\n",
+       "      <td>0.28</td>\n",
+       "      <td>2.29</td>\n",
+       "      <td>5.64</td>\n",
+       "      <td>1.04</td>\n",
+       "      <td>3.92</td>\n",
+       "      <td>1065.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>13.20</td>\n",
+       "      <td>1.78</td>\n",
+       "      <td>2.14</td>\n",
+       "      <td>11.2</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>2.65</td>\n",
+       "      <td>2.76</td>\n",
+       "      <td>0.26</td>\n",
+       "      <td>1.28</td>\n",
+       "      <td>4.38</td>\n",
+       "      <td>1.05</td>\n",
+       "      <td>3.40</td>\n",
+       "      <td>1050.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>13.16</td>\n",
+       "      <td>2.36</td>\n",
+       "      <td>2.67</td>\n",
+       "      <td>18.6</td>\n",
+       "      <td>101.0</td>\n",
+       "      <td>2.80</td>\n",
+       "      <td>3.24</td>\n",
+       "      <td>0.30</td>\n",
+       "      <td>2.81</td>\n",
+       "      <td>5.68</td>\n",
+       "      <td>1.03</td>\n",
+       "      <td>3.17</td>\n",
+       "      <td>1185.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>14.37</td>\n",
+       "      <td>1.95</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>16.8</td>\n",
+       "      <td>113.0</td>\n",
+       "      <td>3.85</td>\n",
+       "      <td>3.49</td>\n",
+       "      <td>0.24</td>\n",
+       "      <td>2.18</td>\n",
+       "      <td>7.80</td>\n",
+       "      <td>0.86</td>\n",
+       "      <td>3.45</td>\n",
+       "      <td>1480.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>13.24</td>\n",
+       "      <td>2.59</td>\n",
+       "      <td>2.87</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>118.0</td>\n",
+       "      <td>2.80</td>\n",
+       "      <td>2.69</td>\n",
+       "      <td>0.39</td>\n",
+       "      <td>1.82</td>\n",
+       "      <td>4.32</td>\n",
+       "      <td>1.04</td>\n",
+       "      <td>2.93</td>\n",
+       "      <td>735.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>173</th>\n",
+       "      <td>13.71</td>\n",
+       "      <td>5.65</td>\n",
+       "      <td>2.45</td>\n",
+       "      <td>20.5</td>\n",
+       "      <td>95.0</td>\n",
+       "      <td>1.68</td>\n",
+       "      <td>0.61</td>\n",
+       "      <td>0.52</td>\n",
+       "      <td>1.06</td>\n",
+       "      <td>7.70</td>\n",
+       "      <td>0.64</td>\n",
+       "      <td>1.74</td>\n",
+       "      <td>740.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>174</th>\n",
+       "      <td>13.40</td>\n",
+       "      <td>3.91</td>\n",
+       "      <td>2.48</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>102.0</td>\n",
+       "      <td>1.80</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>0.43</td>\n",
+       "      <td>1.41</td>\n",
+       "      <td>7.30</td>\n",
+       "      <td>0.70</td>\n",
+       "      <td>1.56</td>\n",
+       "      <td>750.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>175</th>\n",
+       "      <td>13.27</td>\n",
+       "      <td>4.28</td>\n",
+       "      <td>2.26</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>120.0</td>\n",
+       "      <td>1.59</td>\n",
+       "      <td>0.69</td>\n",
+       "      <td>0.43</td>\n",
+       "      <td>1.35</td>\n",
+       "      <td>10.20</td>\n",
+       "      <td>0.59</td>\n",
+       "      <td>1.56</td>\n",
+       "      <td>835.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>176</th>\n",
+       "      <td>13.17</td>\n",
+       "      <td>2.59</td>\n",
+       "      <td>2.37</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>120.0</td>\n",
+       "      <td>1.65</td>\n",
+       "      <td>0.68</td>\n",
+       "      <td>0.53</td>\n",
+       "      <td>1.46</td>\n",
+       "      <td>9.30</td>\n",
+       "      <td>0.60</td>\n",
+       "      <td>1.62</td>\n",
+       "      <td>840.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>177</th>\n",
+       "      <td>14.13</td>\n",
+       "      <td>4.10</td>\n",
+       "      <td>2.74</td>\n",
+       "      <td>24.5</td>\n",
+       "      <td>96.0</td>\n",
+       "      <td>2.05</td>\n",
+       "      <td>0.76</td>\n",
+       "      <td>0.56</td>\n",
+       "      <td>1.35</td>\n",
+       "      <td>9.20</td>\n",
+       "      <td>0.61</td>\n",
+       "      <td>1.60</td>\n",
+       "      <td>560.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>178 rows × 14 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     alcohol  malic_acid   ash  alcalinity_of_ash  magnesium  total_phenols  \\\n",
+       "0      14.23        1.71  2.43               15.6      127.0           2.80   \n",
+       "1      13.20        1.78  2.14               11.2      100.0           2.65   \n",
+       "2      13.16        2.36  2.67               18.6      101.0           2.80   \n",
+       "3      14.37        1.95  2.50               16.8      113.0           3.85   \n",
+       "4      13.24        2.59  2.87               21.0      118.0           2.80   \n",
+       "..       ...         ...   ...                ...        ...            ...   \n",
+       "173    13.71        5.65  2.45               20.5       95.0           1.68   \n",
+       "174    13.40        3.91  2.48               23.0      102.0           1.80   \n",
+       "175    13.27        4.28  2.26               20.0      120.0           1.59   \n",
+       "176    13.17        2.59  2.37               20.0      120.0           1.65   \n",
+       "177    14.13        4.10  2.74               24.5       96.0           2.05   \n",
+       "\n",
+       "     flavanoids  nonflavanoid_phenols  proanthocyanins  color_intensity   hue  \\\n",
+       "0          3.06                  0.28             2.29             5.64  1.04   \n",
+       "1          2.76                  0.26             1.28             4.38  1.05   \n",
+       "2          3.24                  0.30             2.81             5.68  1.03   \n",
+       "3          3.49                  0.24             2.18             7.80  0.86   \n",
+       "4          2.69                  0.39             1.82             4.32  1.04   \n",
+       "..          ...                   ...              ...              ...   ...   \n",
+       "173        0.61                  0.52             1.06             7.70  0.64   \n",
+       "174        0.75                  0.43             1.41             7.30  0.70   \n",
+       "175        0.69                  0.43             1.35            10.20  0.59   \n",
+       "176        0.68                  0.53             1.46             9.30  0.60   \n",
+       "177        0.76                  0.56             1.35             9.20  0.61   \n",
+       "\n",
+       "     od280/od315_of_diluted_wines  proline  class  \n",
+       "0                            3.92   1065.0      0  \n",
+       "1                            3.40   1050.0      0  \n",
+       "2                            3.17   1185.0      0  \n",
+       "3                            3.45   1480.0      0  \n",
+       "4                            2.93    735.0      0  \n",
+       "..                            ...      ...    ...  \n",
+       "173                          1.74    740.0      2  \n",
+       "174                          1.56    750.0      2  \n",
+       "175                          1.56    835.0      2  \n",
+       "176                          1.62    840.0      2  \n",
+       "177                          1.60    560.0      2  \n",
+       "\n",
+       "[178 rows x 14 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.datasets import load_wine\n",
+    "\n",
+    "# Load the Wine dataset\n",
+    "wine_data = load_wine()\n",
+    "\n",
+    "# Convert to DataFrame\n",
+    "wine_df = pd.DataFrame(wine_data.data, columns=wine_data.feature_names)\n",
+    "\n",
+    "# Bind the 'class' (wine target) to the DataFrame\n",
+    "wine_df['class'] = wine_data.target\n",
+    "\n",
+    "# Display the DataFrame\n",
+    "wine_df\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "721b2b17",
+   "metadata": {},
+   "source": [
+    "#### **Question 1:** \n",
+    "#### Data inspection\n",
+    "\n",
+    "Before fitting any model, it is essential to understand our data. **Use Python code** to answer the following questions about the **Wine dataset**:\n",
+    "\n",
+    "_(i)_ How many observations (rows) does the dataset contain?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "56916892",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "178"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# count number of rows\n",
+    "wine_df.shape[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f7573b59",
+   "metadata": {},
+   "source": [
+    "_(ii)_ How many variables (columns) does the dataset contain?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "df0ef103",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "14"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# count number of columns\n",
+    "wine_df.shape[1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cb5180c7",
+   "metadata": {},
+   "source": [
+    "_(iii)_ What is the 'variable type' of the response variable `class` (e.g., 'integer', 'category', etc.)? What are the 'levels' (unique values) of the variable?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "47989426",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dtype('int32')"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# data type of column class\n",
+    "wine_df.dtypes['class']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a25f5e1b",
+   "metadata": {},
+   "source": [
+    "\n",
+    "_(iv)_ How many predictor variables do we have (Hint: all variables other than `class`)? "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "bd7b0910",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 178 entries, 0 to 177\n",
+      "Data columns (total 14 columns):\n",
+      " #   Column                        Non-Null Count  Dtype  \n",
+      "---  ------                        --------------  -----  \n",
+      " 0   alcohol                       178 non-null    float64\n",
+      " 1   malic_acid                    178 non-null    float64\n",
+      " 2   ash                           178 non-null    float64\n",
+      " 3   alcalinity_of_ash             178 non-null    float64\n",
+      " 4   magnesium                     178 non-null    float64\n",
+      " 5   total_phenols                 178 non-null    float64\n",
+      " 6   flavanoids                    178 non-null    float64\n",
+      " 7   nonflavanoid_phenols          178 non-null    float64\n",
+      " 8   proanthocyanins               178 non-null    float64\n",
+      " 9   color_intensity               178 non-null    float64\n",
+      " 10  hue                           178 non-null    float64\n",
+      " 11  od280/od315_of_diluted_wines  178 non-null    float64\n",
+      " 12  proline                       178 non-null    float64\n",
+      " 13  class                         178 non-null    int32  \n",
+      "dtypes: float64(13), int32(1)\n",
+      "memory usage: 18.9 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Number of predictor variables is 12\n",
+    "wine_df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "156cc83a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>alcohol</th>\n",
+       "      <th>malic_acid</th>\n",
+       "      <th>ash</th>\n",
+       "      <th>alcalinity_of_ash</th>\n",
+       "      <th>magnesium</th>\n",
+       "      <th>total_phenols</th>\n",
+       "      <th>flavanoids</th>\n",
+       "      <th>nonflavanoid_phenols</th>\n",
+       "      <th>proanthocyanins</th>\n",
+       "      <th>color_intensity</th>\n",
+       "      <th>hue</th>\n",
+       "      <th>od280/od315_of_diluted_wines</th>\n",
+       "      <th>proline</th>\n",
+       "      <th>class</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>178.000000</td>\n",
+       "      <td>178.000000</td>\n",
+       "      <td>178.000000</td>\n",
+       "      <td>178.000000</td>\n",
+       "      <td>178.000000</td>\n",
+       "      <td>178.000000</td>\n",
+       "      <td>178.000000</td>\n",
+       "      <td>178.000000</td>\n",
+       "      <td>178.000000</td>\n",
+       "      <td>178.000000</td>\n",
+       "      <td>178.000000</td>\n",
+       "      <td>178.000000</td>\n",
+       "      <td>178.000000</td>\n",
+       "      <td>178.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>13.000618</td>\n",
+       "      <td>2.336348</td>\n",
+       "      <td>2.366517</td>\n",
+       "      <td>19.494944</td>\n",
+       "      <td>99.741573</td>\n",
+       "      <td>2.295112</td>\n",
+       "      <td>2.029270</td>\n",
+       "      <td>0.361854</td>\n",
+       "      <td>1.590899</td>\n",
+       "      <td>5.058090</td>\n",
+       "      <td>0.957449</td>\n",
+       "      <td>2.611685</td>\n",
+       "      <td>746.893258</td>\n",
+       "      <td>0.938202</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.811827</td>\n",
+       "      <td>1.117146</td>\n",
+       "      <td>0.274344</td>\n",
+       "      <td>3.339564</td>\n",
+       "      <td>14.282484</td>\n",
+       "      <td>0.625851</td>\n",
+       "      <td>0.998859</td>\n",
+       "      <td>0.124453</td>\n",
+       "      <td>0.572359</td>\n",
+       "      <td>2.318286</td>\n",
+       "      <td>0.228572</td>\n",
+       "      <td>0.709990</td>\n",
+       "      <td>314.907474</td>\n",
+       "      <td>0.775035</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>11.030000</td>\n",
+       "      <td>0.740000</td>\n",
+       "      <td>1.360000</td>\n",
+       "      <td>10.600000</td>\n",
+       "      <td>70.000000</td>\n",
+       "      <td>0.980000</td>\n",
+       "      <td>0.340000</td>\n",
+       "      <td>0.130000</td>\n",
+       "      <td>0.410000</td>\n",
+       "      <td>1.280000</td>\n",
+       "      <td>0.480000</td>\n",
+       "      <td>1.270000</td>\n",
+       "      <td>278.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>12.362500</td>\n",
+       "      <td>1.602500</td>\n",
+       "      <td>2.210000</td>\n",
+       "      <td>17.200000</td>\n",
+       "      <td>88.000000</td>\n",
+       "      <td>1.742500</td>\n",
+       "      <td>1.205000</td>\n",
+       "      <td>0.270000</td>\n",
+       "      <td>1.250000</td>\n",
+       "      <td>3.220000</td>\n",
+       "      <td>0.782500</td>\n",
+       "      <td>1.937500</td>\n",
+       "      <td>500.500000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>13.050000</td>\n",
+       "      <td>1.865000</td>\n",
+       "      <td>2.360000</td>\n",
+       "      <td>19.500000</td>\n",
+       "      <td>98.000000</td>\n",
+       "      <td>2.355000</td>\n",
+       "      <td>2.135000</td>\n",
+       "      <td>0.340000</td>\n",
+       "      <td>1.555000</td>\n",
+       "      <td>4.690000</td>\n",
+       "      <td>0.965000</td>\n",
+       "      <td>2.780000</td>\n",
+       "      <td>673.500000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>13.677500</td>\n",
+       "      <td>3.082500</td>\n",
+       "      <td>2.557500</td>\n",
+       "      <td>21.500000</td>\n",
+       "      <td>107.000000</td>\n",
+       "      <td>2.800000</td>\n",
+       "      <td>2.875000</td>\n",
+       "      <td>0.437500</td>\n",
+       "      <td>1.950000</td>\n",
+       "      <td>6.200000</td>\n",
+       "      <td>1.120000</td>\n",
+       "      <td>3.170000</td>\n",
+       "      <td>985.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>14.830000</td>\n",
+       "      <td>5.800000</td>\n",
+       "      <td>3.230000</td>\n",
+       "      <td>30.000000</td>\n",
+       "      <td>162.000000</td>\n",
+       "      <td>3.880000</td>\n",
+       "      <td>5.080000</td>\n",
+       "      <td>0.660000</td>\n",
+       "      <td>3.580000</td>\n",
+       "      <td>13.000000</td>\n",
+       "      <td>1.710000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>1680.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          alcohol  malic_acid         ash  alcalinity_of_ash   magnesium  \\\n",
+       "count  178.000000  178.000000  178.000000         178.000000  178.000000   \n",
+       "mean    13.000618    2.336348    2.366517          19.494944   99.741573   \n",
+       "std      0.811827    1.117146    0.274344           3.339564   14.282484   \n",
+       "min     11.030000    0.740000    1.360000          10.600000   70.000000   \n",
+       "25%     12.362500    1.602500    2.210000          17.200000   88.000000   \n",
+       "50%     13.050000    1.865000    2.360000          19.500000   98.000000   \n",
+       "75%     13.677500    3.082500    2.557500          21.500000  107.000000   \n",
+       "max     14.830000    5.800000    3.230000          30.000000  162.000000   \n",
+       "\n",
+       "       total_phenols  flavanoids  nonflavanoid_phenols  proanthocyanins  \\\n",
+       "count     178.000000  178.000000            178.000000       178.000000   \n",
+       "mean        2.295112    2.029270              0.361854         1.590899   \n",
+       "std         0.625851    0.998859              0.124453         0.572359   \n",
+       "min         0.980000    0.340000              0.130000         0.410000   \n",
+       "25%         1.742500    1.205000              0.270000         1.250000   \n",
+       "50%         2.355000    2.135000              0.340000         1.555000   \n",
+       "75%         2.800000    2.875000              0.437500         1.950000   \n",
+       "max         3.880000    5.080000              0.660000         3.580000   \n",
+       "\n",
+       "       color_intensity         hue  od280/od315_of_diluted_wines      proline  \\\n",
+       "count       178.000000  178.000000                    178.000000   178.000000   \n",
+       "mean          5.058090    0.957449                      2.611685   746.893258   \n",
+       "std           2.318286    0.228572                      0.709990   314.907474   \n",
+       "min           1.280000    0.480000                      1.270000   278.000000   \n",
+       "25%           3.220000    0.782500                      1.937500   500.500000   \n",
+       "50%           4.690000    0.965000                      2.780000   673.500000   \n",
+       "75%           6.200000    1.120000                      3.170000   985.000000   \n",
+       "max          13.000000    1.710000                      4.000000  1680.000000   \n",
+       "\n",
+       "            class  \n",
+       "count  178.000000  \n",
+       "mean     0.938202  \n",
+       "std      0.775035  \n",
+       "min      0.000000  \n",
+       "25%      0.000000  \n",
+       "50%      1.000000  \n",
+       "75%      2.000000  \n",
+       "max      2.000000  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "wine_df.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d631e8e3",
+   "metadata": {},
+   "source": [
+    "You can use `print()` and `describe()` to help answer these questions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fa3832d7",
+   "metadata": {},
+   "source": [
+    "#### **Question 2:** \n",
+    "#### Standardization and data-splitting\n",
+    "\n",
+    "Next, we must preform 'pre-processing' or 'data munging', to prepare our data for classification/prediction. For KNN, there are three essential steps. A first essential step is to 'standardize' the predictor variables. We can achieve this using the scaler method, provided as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "cc899b59",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    alcohol  malic_acid       ash  alcalinity_of_ash  magnesium  \\\n",
+      "0  1.518613   -0.562250  0.232053          -1.169593   1.913905   \n",
+      "1  0.246290   -0.499413 -0.827996          -2.490847   0.018145   \n",
+      "2  0.196879    0.021231  1.109334          -0.268738   0.088358   \n",
+      "3  1.691550   -0.346811  0.487926          -0.809251   0.930918   \n",
+      "4  0.295700    0.227694  1.840403           0.451946   1.281985   \n",
+      "\n",
+      "   total_phenols  flavanoids  nonflavanoid_phenols  proanthocyanins  \\\n",
+      "0       0.808997    1.034819             -0.659563         1.224884   \n",
+      "1       0.568648    0.733629             -0.820719        -0.544721   \n",
+      "2       0.808997    1.215533             -0.498407         2.135968   \n",
+      "3       2.491446    1.466525             -0.981875         1.032155   \n",
+      "4       0.808997    0.663351              0.226796         0.401404   \n",
+      "\n",
+      "   color_intensity       hue  od280/od315_of_diluted_wines   proline  \n",
+      "0         0.251717  0.362177                      1.847920  1.013009  \n",
+      "1        -0.293321  0.406051                      1.113449  0.965242  \n",
+      "2         0.269020  0.318304                      0.788587  1.395148  \n",
+      "3         1.186068 -0.427544                      1.184071  2.334574  \n",
+      "4        -0.319276  0.362177                      0.449601 -0.037874  \n"
+     ]
+    }
+   ],
+   "source": [
+    "# Select predictors (excluding the last column)\n",
+    "predictors = wine_df.iloc[:, :-1]\n",
+    "\n",
+    "# Standardize the predictors\n",
+    "scaler = StandardScaler()\n",
+    "predictors_standardized = pd.DataFrame(scaler.fit_transform(predictors), columns=predictors.columns)\n",
+    "\n",
+    "# Display the head of the standardized predictors\n",
+    "print(predictors_standardized.head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9981ca48",
+   "metadata": {},
+   "source": [
+    "(i) Why is it important to standardize the predictor variables?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "403ef0bb",
+   "metadata": {},
+   "source": [
+    "> To make sure all the predictor valiables to have same scale therefore none of them will be dominated because of large scale and skew the classifcation result when using machine learning models that rely on distance metrics."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8e2e1bea",
+   "metadata": {},
+   "source": [
+    "(ii) Why did we elect not to standard our response variable `Class`?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fdee5a15",
+   "metadata": {},
+   "source": [
+    "> This is the variable we want to determine through the model and its scale would not affect the classification result  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8077ec21",
+   "metadata": {},
+   "source": [
+    "(iii) A second essential step is to set a random seed. Do so below (Hint: use the random.seed function). Why is setting a seed important? Is the particular seed value important? Why or why not?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f0676c21",
+   "metadata": {},
+   "source": [
+    "> Setting random seeed is important because it allow us to control the randomness in our code. Therefore we can repoduce the same result after running the code and do comparison or testing."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "36ab9229",
+   "metadata": {},
+   "source": [
+    "(iv) A third essential step is to split our standardized data into separate training and testing sets. We will split into 75% training and 25% testing. The provided code randomly partitions our data, and creates linked training sets for the predictors and response variables. \n",
+    "\n",
+    "Extend the code to create a non-overlapping test set for the predictors and response variables."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "8e4a9dda",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.series.Series'>\n",
+      "Index: 133 entries, 78 to 66\n",
+      "Series name: class\n",
+      "Non-Null Count  Dtype\n",
+      "--------------  -----\n",
+      "133 non-null    int32\n",
+      "dtypes: int32(1)\n",
+      "memory usage: 1.6 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# set a seed for reproducibility\n",
+    "np.random.seed(123)\n",
+    "# split the data into a training and testing set. hint: use train_test_split !\n",
+    "# Your code here ...\n",
+    "\n",
+    "# concatnate the standardized predictor and response variable into one dataframe \n",
+    "full_std_data = pd.concat([predictors_standardized,wine_df['class']], axis=1)\n",
+    "full_std_data\n",
+    "\n",
+    "# split the data into training and testing set\n",
+    "full_std_train, full_std_test = train_test_split(\n",
+    "    full_std_data, train_size=0.75, shuffle= True,\n",
+    "    stratify=full_std_data[\"class\"], \n",
+    "    random_state= 123\n",
+    ")\n",
+    "\n",
+    "## set variable to retrieve train and test data from predictor variable df and reponse variable df\n",
+    "full_std_train\n",
+    "\n",
+    "std_train_x = full_std_train.iloc[:,:-1]\n",
+    "std_train_x\n",
+    "\n",
+    "std_test_x = full_std_test.iloc[:,:-1]\n",
+    "std_test_x\n",
+    "\n",
+    "std_train_y = full_std_train ['class']\n",
+    "std_train_y.info()\n",
+    "\n",
+    "std_test_y = full_std_test ['class']\n",
+    "#std_test_y.info()\n",
+    "\n",
+    "# output show std_train_y has 133 entries and std_test_y has 45 entries. concfirm it is a 0.75/0.25 split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "d7c21a90",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>alcohol</th>\n",
+       "      <th>malic_acid</th>\n",
+       "      <th>ash</th>\n",
+       "      <th>alcalinity_of_ash</th>\n",
+       "      <th>magnesium</th>\n",
+       "      <th>total_phenols</th>\n",
+       "      <th>flavanoids</th>\n",
+       "      <th>nonflavanoid_phenols</th>\n",
+       "      <th>proanthocyanins</th>\n",
+       "      <th>color_intensity</th>\n",
+       "      <th>hue</th>\n",
+       "      <th>od280/od315_of_diluted_wines</th>\n",
+       "      <th>proline</th>\n",
+       "      <th>class</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.518613</td>\n",
+       "      <td>-0.562250</td>\n",
+       "      <td>0.232053</td>\n",
+       "      <td>-1.169593</td>\n",
+       "      <td>1.913905</td>\n",
+       "      <td>0.808997</td>\n",
+       "      <td>1.034819</td>\n",
+       "      <td>-0.659563</td>\n",
+       "      <td>1.224884</td>\n",
+       "      <td>0.251717</td>\n",
+       "      <td>0.362177</td>\n",
+       "      <td>1.847920</td>\n",
+       "      <td>1.013009</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.246290</td>\n",
+       "      <td>-0.499413</td>\n",
+       "      <td>-0.827996</td>\n",
+       "      <td>-2.490847</td>\n",
+       "      <td>0.018145</td>\n",
+       "      <td>0.568648</td>\n",
+       "      <td>0.733629</td>\n",
+       "      <td>-0.820719</td>\n",
+       "      <td>-0.544721</td>\n",
+       "      <td>-0.293321</td>\n",
+       "      <td>0.406051</td>\n",
+       "      <td>1.113449</td>\n",
+       "      <td>0.965242</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.196879</td>\n",
+       "      <td>0.021231</td>\n",
+       "      <td>1.109334</td>\n",
+       "      <td>-0.268738</td>\n",
+       "      <td>0.088358</td>\n",
+       "      <td>0.808997</td>\n",
+       "      <td>1.215533</td>\n",
+       "      <td>-0.498407</td>\n",
+       "      <td>2.135968</td>\n",
+       "      <td>0.269020</td>\n",
+       "      <td>0.318304</td>\n",
+       "      <td>0.788587</td>\n",
+       "      <td>1.395148</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.691550</td>\n",
+       "      <td>-0.346811</td>\n",
+       "      <td>0.487926</td>\n",
+       "      <td>-0.809251</td>\n",
+       "      <td>0.930918</td>\n",
+       "      <td>2.491446</td>\n",
+       "      <td>1.466525</td>\n",
+       "      <td>-0.981875</td>\n",
+       "      <td>1.032155</td>\n",
+       "      <td>1.186068</td>\n",
+       "      <td>-0.427544</td>\n",
+       "      <td>1.184071</td>\n",
+       "      <td>2.334574</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.295700</td>\n",
+       "      <td>0.227694</td>\n",
+       "      <td>1.840403</td>\n",
+       "      <td>0.451946</td>\n",
+       "      <td>1.281985</td>\n",
+       "      <td>0.808997</td>\n",
+       "      <td>0.663351</td>\n",
+       "      <td>0.226796</td>\n",
+       "      <td>0.401404</td>\n",
+       "      <td>-0.319276</td>\n",
+       "      <td>0.362177</td>\n",
+       "      <td>0.449601</td>\n",
+       "      <td>-0.037874</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>173</th>\n",
+       "      <td>0.876275</td>\n",
+       "      <td>2.974543</td>\n",
+       "      <td>0.305159</td>\n",
+       "      <td>0.301803</td>\n",
+       "      <td>-0.332922</td>\n",
+       "      <td>-0.985614</td>\n",
+       "      <td>-1.424900</td>\n",
+       "      <td>1.274310</td>\n",
+       "      <td>-0.930179</td>\n",
+       "      <td>1.142811</td>\n",
+       "      <td>-1.392758</td>\n",
+       "      <td>-1.231206</td>\n",
+       "      <td>-0.021952</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>174</th>\n",
+       "      <td>0.493343</td>\n",
+       "      <td>1.412609</td>\n",
+       "      <td>0.414820</td>\n",
+       "      <td>1.052516</td>\n",
+       "      <td>0.158572</td>\n",
+       "      <td>-0.793334</td>\n",
+       "      <td>-1.284344</td>\n",
+       "      <td>0.549108</td>\n",
+       "      <td>-0.316950</td>\n",
+       "      <td>0.969783</td>\n",
+       "      <td>-1.129518</td>\n",
+       "      <td>-1.485445</td>\n",
+       "      <td>0.009893</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>175</th>\n",
+       "      <td>0.332758</td>\n",
+       "      <td>1.744744</td>\n",
+       "      <td>-0.389355</td>\n",
+       "      <td>0.151661</td>\n",
+       "      <td>1.422412</td>\n",
+       "      <td>-1.129824</td>\n",
+       "      <td>-1.344582</td>\n",
+       "      <td>0.549108</td>\n",
+       "      <td>-0.422075</td>\n",
+       "      <td>2.224236</td>\n",
+       "      <td>-1.612125</td>\n",
+       "      <td>-1.485445</td>\n",
+       "      <td>0.280575</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>176</th>\n",
+       "      <td>0.209232</td>\n",
+       "      <td>0.227694</td>\n",
+       "      <td>0.012732</td>\n",
+       "      <td>0.151661</td>\n",
+       "      <td>1.422412</td>\n",
+       "      <td>-1.033684</td>\n",
+       "      <td>-1.354622</td>\n",
+       "      <td>1.354888</td>\n",
+       "      <td>-0.229346</td>\n",
+       "      <td>1.834923</td>\n",
+       "      <td>-1.568252</td>\n",
+       "      <td>-1.400699</td>\n",
+       "      <td>0.296498</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>177</th>\n",
+       "      <td>1.395086</td>\n",
+       "      <td>1.583165</td>\n",
+       "      <td>1.365208</td>\n",
+       "      <td>1.502943</td>\n",
+       "      <td>-0.262708</td>\n",
+       "      <td>-0.392751</td>\n",
+       "      <td>-1.274305</td>\n",
+       "      <td>1.596623</td>\n",
+       "      <td>-0.422075</td>\n",
+       "      <td>1.791666</td>\n",
+       "      <td>-1.524378</td>\n",
+       "      <td>-1.428948</td>\n",
+       "      <td>-0.595160</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>178 rows × 14 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      alcohol  malic_acid       ash  alcalinity_of_ash  magnesium  \\\n",
+       "0    1.518613   -0.562250  0.232053          -1.169593   1.913905   \n",
+       "1    0.246290   -0.499413 -0.827996          -2.490847   0.018145   \n",
+       "2    0.196879    0.021231  1.109334          -0.268738   0.088358   \n",
+       "3    1.691550   -0.346811  0.487926          -0.809251   0.930918   \n",
+       "4    0.295700    0.227694  1.840403           0.451946   1.281985   \n",
+       "..        ...         ...       ...                ...        ...   \n",
+       "173  0.876275    2.974543  0.305159           0.301803  -0.332922   \n",
+       "174  0.493343    1.412609  0.414820           1.052516   0.158572   \n",
+       "175  0.332758    1.744744 -0.389355           0.151661   1.422412   \n",
+       "176  0.209232    0.227694  0.012732           0.151661   1.422412   \n",
+       "177  1.395086    1.583165  1.365208           1.502943  -0.262708   \n",
+       "\n",
+       "     total_phenols  flavanoids  nonflavanoid_phenols  proanthocyanins  \\\n",
+       "0         0.808997    1.034819             -0.659563         1.224884   \n",
+       "1         0.568648    0.733629             -0.820719        -0.544721   \n",
+       "2         0.808997    1.215533             -0.498407         2.135968   \n",
+       "3         2.491446    1.466525             -0.981875         1.032155   \n",
+       "4         0.808997    0.663351              0.226796         0.401404   \n",
+       "..             ...         ...                   ...              ...   \n",
+       "173      -0.985614   -1.424900              1.274310        -0.930179   \n",
+       "174      -0.793334   -1.284344              0.549108        -0.316950   \n",
+       "175      -1.129824   -1.344582              0.549108        -0.422075   \n",
+       "176      -1.033684   -1.354622              1.354888        -0.229346   \n",
+       "177      -0.392751   -1.274305              1.596623        -0.422075   \n",
+       "\n",
+       "     color_intensity       hue  od280/od315_of_diluted_wines   proline  class  \n",
+       "0           0.251717  0.362177                      1.847920  1.013009      0  \n",
+       "1          -0.293321  0.406051                      1.113449  0.965242      0  \n",
+       "2           0.269020  0.318304                      0.788587  1.395148      0  \n",
+       "3           1.186068 -0.427544                      1.184071  2.334574      0  \n",
+       "4          -0.319276  0.362177                      0.449601 -0.037874      0  \n",
+       "..               ...       ...                           ...       ...    ...  \n",
+       "173         1.142811 -1.392758                     -1.231206 -0.021952      2  \n",
+       "174         0.969783 -1.129518                     -1.485445  0.009893      2  \n",
+       "175         2.224236 -1.612125                     -1.485445  0.280575      2  \n",
+       "176         1.834923 -1.568252                     -1.400699  0.296498      2  \n",
+       "177         1.791666 -1.524378                     -1.428948 -0.595160      2  \n",
+       "\n",
+       "[178 rows x 14 columns]"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# display full_std_data\n",
+    "full_std_data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4604ee03",
+   "metadata": {},
+   "source": [
+    "#### **Question 3:**\n",
+    "#### Model initialization and cross-validation\n",
+    "We are finally set to fit the KNN model. \n",
+    "\n",
+    "\n",
+    "Perform a grid search to tune the `n_neighbors` hyperparameter using 10-fold cross-validation. Follow these steps:\n",
+    "\n",
+    "1. Initialize the KNN classifier using `KNeighborsClassifier()`.\n",
+    "2. Define a parameter grid for `n_neighbors` ranging from 1 to 50.\n",
+    "3. Implement a grid search using `GridSearchCV` with 10-fold cross-validation to find the optimal number of neighbors.\n",
+    "4. After fitting the model on the training data, identify and return the best value for `n_neighbors` based on the grid search results."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "905ed370",
+   "metadata": {},
+   "source": [
+    "Question 3 - point 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "08818c64",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# initiate KNN\n",
+    "knn = KNeighborsClassifier(n_neighbors=5)\n",
+    "                           "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "42b204f2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# define x and y for KNN trainig\n",
+    "X1 = std_train_x\n",
+    "y1 = std_train_y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "44a9ab17",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;KNeighborsClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsClassifier()</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "KNeighborsClassifier()"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# fitting KNN\n",
+    "knn.fit(X1,y1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9ffb8bf8",
+   "metadata": {},
+   "source": [
+    "Question 3  point 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "58c21754",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# implementing a gridSearch , define pararmeter grid, riging from 1 to 50\n",
+    "parameter_grid = {\n",
+    "    \"n_neighbors\": range(1, 50, 3),\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ddf8185b",
+   "metadata": {},
+   "source": [
+    "Question 3 point 3 "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "b3ad80ad",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# use function to search best K -- implementing a gridSearch \n",
+    "wine_tune_grid = GridSearchCV(\n",
+    "    estimator=knn,\n",
+    "    param_grid=parameter_grid,\n",
+    "    cv=10\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "672c7471",
+   "metadata": {},
+   "source": [
+    "Question 3 - point 4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "9fcf66a5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-2 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-2 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-2 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-2 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=10, estimator=KNeighborsClassifier(),\n",
+       "             param_grid={&#x27;n_neighbors&#x27;: range(1, 50, 3)})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=10, estimator=KNeighborsClassifier(),\n",
+       "             param_grid={&#x27;n_neighbors&#x27;: range(1, 50, 3)})</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">best_estimator_: KNeighborsClassifier</label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsClassifier(n_neighbors=7)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;KNeighborsClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsClassifier(n_neighbors=7)</pre></div> </div></div></div></div></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "GridSearchCV(cv=10, estimator=KNeighborsClassifier(),\n",
+       "             param_grid={'n_neighbors': range(1, 50, 3)})"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# fitting the x and y\n",
+    "wine_tune_grid.fit(\n",
+    "    X1,\n",
+    "    y1\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "e0cca0de",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>params</th>\n",
+       "      <th>mean_test_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>{'n_neighbors': 1}</td>\n",
+       "      <td>0.954396</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>{'n_neighbors': 4}</td>\n",
+       "      <td>0.954945</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>{'n_neighbors': 7}</td>\n",
+       "      <td>0.977473</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>{'n_neighbors': 10}</td>\n",
+       "      <td>0.954396</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>{'n_neighbors': 13}</td>\n",
+       "      <td>0.977473</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>{'n_neighbors': 16}</td>\n",
+       "      <td>0.962637</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>{'n_neighbors': 19}</td>\n",
+       "      <td>0.962637</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>{'n_neighbors': 22}</td>\n",
+       "      <td>0.970330</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>{'n_neighbors': 25}</td>\n",
+       "      <td>0.954945</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>{'n_neighbors': 28}</td>\n",
+       "      <td>0.962637</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>{'n_neighbors': 31}</td>\n",
+       "      <td>0.955495</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>{'n_neighbors': 34}</td>\n",
+       "      <td>0.963187</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>{'n_neighbors': 37}</td>\n",
+       "      <td>0.962637</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>{'n_neighbors': 40}</td>\n",
+       "      <td>0.954945</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>{'n_neighbors': 43}</td>\n",
+       "      <td>0.954945</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>{'n_neighbors': 46}</td>\n",
+       "      <td>0.947253</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>{'n_neighbors': 49}</td>\n",
+       "      <td>0.947253</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 params  mean_test_score\n",
+       "0    {'n_neighbors': 1}         0.954396\n",
+       "1    {'n_neighbors': 4}         0.954945\n",
+       "2    {'n_neighbors': 7}         0.977473\n",
+       "3   {'n_neighbors': 10}         0.954396\n",
+       "4   {'n_neighbors': 13}         0.977473\n",
+       "5   {'n_neighbors': 16}         0.962637\n",
+       "6   {'n_neighbors': 19}         0.962637\n",
+       "7   {'n_neighbors': 22}         0.970330\n",
+       "8   {'n_neighbors': 25}         0.954945\n",
+       "9   {'n_neighbors': 28}         0.962637\n",
+       "10  {'n_neighbors': 31}         0.955495\n",
+       "11  {'n_neighbors': 34}         0.963187\n",
+       "12  {'n_neighbors': 37}         0.962637\n",
+       "13  {'n_neighbors': 40}         0.954945\n",
+       "14  {'n_neighbors': 43}         0.954945\n",
+       "15  {'n_neighbors': 46}         0.947253\n",
+       "16  {'n_neighbors': 49}         0.947253"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# check out the accuracy\n",
+    "accuracies_grid = pd.DataFrame(wine_tune_grid.cv_results_)\n",
+    "#accuracies_grid\n",
+    "accuracies_grid [[\"params\",\"mean_test_score\"]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "369cdf3b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'n_neighbors': 7}"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# optimal number of neighbours\n",
+    "wine_tune_grid.best_params_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "aa45b949",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create the plot\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "\n",
+    "\n",
+    "# Plot mean test scores with error bars\n",
+    "plt.plot(accuracies_grid['param_n_neighbors'], accuracies_grid['mean_test_score'], '-o', color='blue')\n",
+    "\n",
+    "# Add labels and legend\n",
+    "plt.xlabel('Number of Neighbors')\n",
+    "plt.ylabel('Accuracy estimate')\n",
+    "plt.title('K-Nearest Neighbors Performance')\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3f76bf62",
+   "metadata": {},
+   "source": [
+    "#### **Question 4:**\n",
+    "#### Model evaluation\n",
+    "\n",
+    "Using the best value for `n_neighbors`, fit a KNN model on the training data and evaluate its performance on the test set using `accuracy_score`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "ffefa9f2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-3 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-3 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-3 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-3 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-3 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=7)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;KNeighborsClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsClassifier(n_neighbors=7)</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "KNeighborsClassifier(n_neighbors=7)"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Initiate the KNN with the best 'n_neighbour' found\n",
+    "knn = KNeighborsClassifier(n_neighbors=wine_tune_grid.best_params_['n_neighbors'])\n",
+    "\n",
+    "# define x and y for KNN using the test data now to find prediction\n",
+    "X2 = std_test_x\n",
+    "y2 = std_test_y\n",
+    "\n",
+    "# fitting KNN into the test set\n",
+    "knn.fit(X2,y2)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "4881b3d5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>class</th>\n",
+       "      <th>test_prediction</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>102</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>84</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>96</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>65</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>79</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>109</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>113</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>159</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>125</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>115</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>71</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>76</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>131</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>60</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>114</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>47</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>158</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>133</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>137</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>154</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>136</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>168</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>117</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>108</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>77</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>142</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>85</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>58</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>175</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>42</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>143</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>177</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     class  test_prediction\n",
+       "102      1                1\n",
+       "84       1                1\n",
+       "96       1                1\n",
+       "65       1                1\n",
+       "79       1                1\n",
+       "17       0                0\n",
+       "109      1                1\n",
+       "113      1                1\n",
+       "28       0                0\n",
+       "159      2                2\n",
+       "38       0                0\n",
+       "34       0                0\n",
+       "125      1                1\n",
+       "115      1                1\n",
+       "71       1                1\n",
+       "76       1                1\n",
+       "131      2                2\n",
+       "33       0                0\n",
+       "60       1                1\n",
+       "19       0                0\n",
+       "114      1                1\n",
+       "47       0                0\n",
+       "48       0                0\n",
+       "158      2                2\n",
+       "133      2                2\n",
+       "137      2                2\n",
+       "154      2                2\n",
+       "136      2                2\n",
+       "2        0                0\n",
+       "168      2                2\n",
+       "117      1                1\n",
+       "32       0                0\n",
+       "22       0                0\n",
+       "108      1                1\n",
+       "73       1                0\n",
+       "77       1                1\n",
+       "142      2                2\n",
+       "9        0                0\n",
+       "85       1                1\n",
+       "58       0                0\n",
+       "45       0                0\n",
+       "175      2                2\n",
+       "42       0                0\n",
+       "143      2                2\n",
+       "177      2                2"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# incorporate the test prediction into the test data set and compare\n",
+    "\n",
+    "\n",
+    "full_std_test[\"test_prediction\"] = knn.predict(X2)\n",
+    "full_std_test[[\"class\",\"test_prediction\"]]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "deae09fe",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "102    1\n",
+       "84     1\n",
+       "96     1\n",
+       "65     1\n",
+       "79     1\n",
+       "17     0\n",
+       "109    1\n",
+       "113    1\n",
+       "28     0\n",
+       "159    2\n",
+       "38     0\n",
+       "34     0\n",
+       "125    1\n",
+       "115    1\n",
+       "71     1\n",
+       "76     1\n",
+       "131    2\n",
+       "33     0\n",
+       "60     1\n",
+       "19     0\n",
+       "114    1\n",
+       "47     0\n",
+       "48     0\n",
+       "158    2\n",
+       "133    2\n",
+       "137    2\n",
+       "154    2\n",
+       "136    2\n",
+       "2      0\n",
+       "168    2\n",
+       "117    1\n",
+       "32     0\n",
+       "22     0\n",
+       "108    1\n",
+       "73     1\n",
+       "77     1\n",
+       "142    2\n",
+       "9      0\n",
+       "85     1\n",
+       "58     0\n",
+       "45     0\n",
+       "175    2\n",
+       "42     0\n",
+       "143    2\n",
+       "177    2\n",
+       "Name: class, dtype: int32"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#other method\n",
+    "\n",
+    "test_prediction = knn.predict(std_test_x)\n",
+    "test_prediction\n",
+    "std_test_y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "id": "ed8d4939",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9777777777777777"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# checking the prediction accuracy using Knn score method or accuracy_score \n",
+    "knn.score(X2,y2)\n",
+    "\n",
+    "#accuracy_score(X2,y2)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "1a401690",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9777777777777777"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#other method\n",
+    "accuracy_score(test_prediction,std_test_y)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6f8a69db",
+   "metadata": {},
+   "source": [
+    "# Criteria\n",
+    "\n",
+    "\n",
+    "| **Criteria**                                           | **Complete**                                      | **Incomplete**                                    |\n",
+    "|--------------------------------------------------------|---------------------------------------------------|--------------------------------------------------|\n",
+    "| **Data Inspection**                                    | Data is inspected for number of variables, observations and data types. | Data inspection is missing or incomplete.         |\n",
+    "| **Data Scaling**                                       | Data scaling or normalization is applied where necessary (e.g., using `StandardScaler`). | Data scaling or normalization is missing or incorrectly applied. |\n",
+    "| **Model Initialization**                               | The KNN model is correctly initialized and a random seed is set for reproducibility.            | The KNN model is not initialized, is incorrect, or lacks a random seed for reproducibility. |\n",
+    "| **Parameter Grid for `n_neighbors`**                   | The parameter grid for `n_neighbors` is correctly defined. | The parameter grid is missing or incorrectly defined. |\n",
+    "| **Cross-Validation Setup**                             | Cross-validation is set up correctly with 10 folds. | Cross-validation is missing or incorrectly set up. |\n",
+    "| **Best Hyperparameter (`n_neighbors`) Selection**       | The best value for `n_neighbors` is identified using the grid search results. | The best `n_neighbors` is not selected or incorrect. |\n",
+    "| **Model Evaluation on Test Data**                      | The model is evaluated on the test data using accuracy. | The model evaluation is missing or uses the wrong metric. |\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0b4390cc",
+   "metadata": {},
+   "source": [
+    "## Submission Information\n",
+    "\n",
+    "🚨 **Please review our [Assignment Submission Guide](https://github.com/UofT-DSI/onboarding/blob/main/onboarding_documents/submissions.md)** 🚨 for detailed instructions on how to format, branch, and submit your work. Following these guidelines is crucial for your submissions to be evaluated correctly.\n",
+    "\n",
+    "### Note:\n",
+    "\n",
+    "If you like, you may collaborate with others in the cohort. If you choose to do so, please indicate with whom you have worked with in your pull request by tagging their GitHub username. Separate submissions are required.\n",
+    "\n",
+    "### Submission Parameters:\n",
+    "* Submission Due Date: `11:59 PM - 01/12/2025`\n",
+    "* The branch name for your repo should be: `assignment-1`\n",
+    "* What to submit for this assignment:\n",
+    "    * This Jupyter Notebook (assignment_1.ipynb) should be populated and should be the only change in your pull request.\n",
+    "* What the pull request link should look like for this assignment: `https://github.com/<your_github_username>/LCR/pull/<pr_id>`\n",
+    "    * Open a private window in your browser. Copy and paste the link to your pull request into the address bar. Make sure you can see your pull request properly. This helps the technical facilitator and learning support staff review your submission easily.\n",
+    "\n",
+    "Checklist:\n",
+    "- [ ] Created a branch with the correct naming convention.\n",
+    "- [ ] Ensured that the repository is public.\n",
+    "- [ ] Reviewed the PR description guidelines and adhered to them.\n",
+    "- [ ] Verify that the link is accessible in a private browser window.\n",
+    "\n",
+    "If you encounter any difficulties or have questions, please don't hesitate to reach out to our team via our Slack at `#cohort-4-help`. Our Technical Facilitators and Learning Support staff are here to help you navigate any challenges.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "dsi_participant",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/02_activities/assignments/assignment_1_using lesson example.ipynb b/02_activities/assignments/assignment_1_using lesson example.ipynb
new file mode 100644
index 000000000..ed1fc91e9
--- /dev/null
+++ b/02_activities/assignments/assignment_1_using lesson example.ipynb	
@@ -0,0 +1,2661 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "7b0bcac6-5086-4f4e-928a-570a9ff7ae58",
+   "metadata": {},
+   "source": [
+    "# Assignment 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5fce0350-2a17-4e93-8d4c-0b8748fdfc32",
+   "metadata": {},
+   "source": [
+    "You only need to write one line of code for each question. When answering questions that ask you to identify or interpret something, the length of your response doesn’t matter. For example, if the answer is just ‘yes,’ ‘no,’ or a number, you can just give that answer without adding anything else.\n",
+    "\n",
+    "We will go through comparable code and concepts in the live learning session. If you run into trouble, start by using the help `help()` function in Python, to get information about the datasets and function in question. The internet is also a great resource when coding (though note that **no outside searches are required by the assignment!**). If you do incorporate code from the internet, please cite the source within your code (providing a URL is sufficient).\n",
+    "\n",
+    "Please bring questions that you cannot work out on your own to office hours, work periods or share with your peers on Slack. We will work with you through the issue."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5fc5001c-7715-4ebe-b0f7-e4bd04349629",
+   "metadata": {},
+   "source": [
+    "### Classification using KNN\n",
+    "\n",
+    "Let's set up our workspace and use the **Wine dataset** from `scikit-learn`. This dataset contains 178 wine samples with 13 chemical features, used to classify wines into different classes based on their origin.\n",
+    "\n",
+    "The **response variable** is `class`, which indicates the type of wine. We'll use all of the chemical features to predict this response variable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "4a3485d6-ba58-4660-a983-5680821c5719",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import standard libraries\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import random\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.colors as mcolors\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.metrics import recall_score, precision_score\n",
+    "from sklearn.model_selection import cross_validate\n",
+    "from sklearn.model_selection import GridSearchCV\n",
+    "from sklearn.metrics import accuracy_score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "a431d282-f9ca-4d5d-8912-71ffc9d8ea19",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>alcohol</th>\n",
+       "      <th>malic_acid</th>\n",
+       "      <th>ash</th>\n",
+       "      <th>alcalinity_of_ash</th>\n",
+       "      <th>magnesium</th>\n",
+       "      <th>total_phenols</th>\n",
+       "      <th>flavanoids</th>\n",
+       "      <th>nonflavanoid_phenols</th>\n",
+       "      <th>proanthocyanins</th>\n",
+       "      <th>color_intensity</th>\n",
+       "      <th>hue</th>\n",
+       "      <th>od280/od315_of_diluted_wines</th>\n",
+       "      <th>proline</th>\n",
+       "      <th>class</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>14.23</td>\n",
+       "      <td>1.71</td>\n",
+       "      <td>2.43</td>\n",
+       "      <td>15.6</td>\n",
+       "      <td>127.0</td>\n",
+       "      <td>2.80</td>\n",
+       "      <td>3.06</td>\n",
+       "      <td>0.28</td>\n",
+       "      <td>2.29</td>\n",
+       "      <td>5.64</td>\n",
+       "      <td>1.04</td>\n",
+       "      <td>3.92</td>\n",
+       "      <td>1065.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>13.20</td>\n",
+       "      <td>1.78</td>\n",
+       "      <td>2.14</td>\n",
+       "      <td>11.2</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>2.65</td>\n",
+       "      <td>2.76</td>\n",
+       "      <td>0.26</td>\n",
+       "      <td>1.28</td>\n",
+       "      <td>4.38</td>\n",
+       "      <td>1.05</td>\n",
+       "      <td>3.40</td>\n",
+       "      <td>1050.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>13.16</td>\n",
+       "      <td>2.36</td>\n",
+       "      <td>2.67</td>\n",
+       "      <td>18.6</td>\n",
+       "      <td>101.0</td>\n",
+       "      <td>2.80</td>\n",
+       "      <td>3.24</td>\n",
+       "      <td>0.30</td>\n",
+       "      <td>2.81</td>\n",
+       "      <td>5.68</td>\n",
+       "      <td>1.03</td>\n",
+       "      <td>3.17</td>\n",
+       "      <td>1185.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>14.37</td>\n",
+       "      <td>1.95</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>16.8</td>\n",
+       "      <td>113.0</td>\n",
+       "      <td>3.85</td>\n",
+       "      <td>3.49</td>\n",
+       "      <td>0.24</td>\n",
+       "      <td>2.18</td>\n",
+       "      <td>7.80</td>\n",
+       "      <td>0.86</td>\n",
+       "      <td>3.45</td>\n",
+       "      <td>1480.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>13.24</td>\n",
+       "      <td>2.59</td>\n",
+       "      <td>2.87</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>118.0</td>\n",
+       "      <td>2.80</td>\n",
+       "      <td>2.69</td>\n",
+       "      <td>0.39</td>\n",
+       "      <td>1.82</td>\n",
+       "      <td>4.32</td>\n",
+       "      <td>1.04</td>\n",
+       "      <td>2.93</td>\n",
+       "      <td>735.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>173</th>\n",
+       "      <td>13.71</td>\n",
+       "      <td>5.65</td>\n",
+       "      <td>2.45</td>\n",
+       "      <td>20.5</td>\n",
+       "      <td>95.0</td>\n",
+       "      <td>1.68</td>\n",
+       "      <td>0.61</td>\n",
+       "      <td>0.52</td>\n",
+       "      <td>1.06</td>\n",
+       "      <td>7.70</td>\n",
+       "      <td>0.64</td>\n",
+       "      <td>1.74</td>\n",
+       "      <td>740.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>174</th>\n",
+       "      <td>13.40</td>\n",
+       "      <td>3.91</td>\n",
+       "      <td>2.48</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>102.0</td>\n",
+       "      <td>1.80</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>0.43</td>\n",
+       "      <td>1.41</td>\n",
+       "      <td>7.30</td>\n",
+       "      <td>0.70</td>\n",
+       "      <td>1.56</td>\n",
+       "      <td>750.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>175</th>\n",
+       "      <td>13.27</td>\n",
+       "      <td>4.28</td>\n",
+       "      <td>2.26</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>120.0</td>\n",
+       "      <td>1.59</td>\n",
+       "      <td>0.69</td>\n",
+       "      <td>0.43</td>\n",
+       "      <td>1.35</td>\n",
+       "      <td>10.20</td>\n",
+       "      <td>0.59</td>\n",
+       "      <td>1.56</td>\n",
+       "      <td>835.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>176</th>\n",
+       "      <td>13.17</td>\n",
+       "      <td>2.59</td>\n",
+       "      <td>2.37</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>120.0</td>\n",
+       "      <td>1.65</td>\n",
+       "      <td>0.68</td>\n",
+       "      <td>0.53</td>\n",
+       "      <td>1.46</td>\n",
+       "      <td>9.30</td>\n",
+       "      <td>0.60</td>\n",
+       "      <td>1.62</td>\n",
+       "      <td>840.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>177</th>\n",
+       "      <td>14.13</td>\n",
+       "      <td>4.10</td>\n",
+       "      <td>2.74</td>\n",
+       "      <td>24.5</td>\n",
+       "      <td>96.0</td>\n",
+       "      <td>2.05</td>\n",
+       "      <td>0.76</td>\n",
+       "      <td>0.56</td>\n",
+       "      <td>1.35</td>\n",
+       "      <td>9.20</td>\n",
+       "      <td>0.61</td>\n",
+       "      <td>1.60</td>\n",
+       "      <td>560.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>178 rows × 14 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     alcohol  malic_acid   ash  alcalinity_of_ash  magnesium  total_phenols  \\\n",
+       "0      14.23        1.71  2.43               15.6      127.0           2.80   \n",
+       "1      13.20        1.78  2.14               11.2      100.0           2.65   \n",
+       "2      13.16        2.36  2.67               18.6      101.0           2.80   \n",
+       "3      14.37        1.95  2.50               16.8      113.0           3.85   \n",
+       "4      13.24        2.59  2.87               21.0      118.0           2.80   \n",
+       "..       ...         ...   ...                ...        ...            ...   \n",
+       "173    13.71        5.65  2.45               20.5       95.0           1.68   \n",
+       "174    13.40        3.91  2.48               23.0      102.0           1.80   \n",
+       "175    13.27        4.28  2.26               20.0      120.0           1.59   \n",
+       "176    13.17        2.59  2.37               20.0      120.0           1.65   \n",
+       "177    14.13        4.10  2.74               24.5       96.0           2.05   \n",
+       "\n",
+       "     flavanoids  nonflavanoid_phenols  proanthocyanins  color_intensity   hue  \\\n",
+       "0          3.06                  0.28             2.29             5.64  1.04   \n",
+       "1          2.76                  0.26             1.28             4.38  1.05   \n",
+       "2          3.24                  0.30             2.81             5.68  1.03   \n",
+       "3          3.49                  0.24             2.18             7.80  0.86   \n",
+       "4          2.69                  0.39             1.82             4.32  1.04   \n",
+       "..          ...                   ...              ...              ...   ...   \n",
+       "173        0.61                  0.52             1.06             7.70  0.64   \n",
+       "174        0.75                  0.43             1.41             7.30  0.70   \n",
+       "175        0.69                  0.43             1.35            10.20  0.59   \n",
+       "176        0.68                  0.53             1.46             9.30  0.60   \n",
+       "177        0.76                  0.56             1.35             9.20  0.61   \n",
+       "\n",
+       "     od280/od315_of_diluted_wines  proline  class  \n",
+       "0                            3.92   1065.0      0  \n",
+       "1                            3.40   1050.0      0  \n",
+       "2                            3.17   1185.0      0  \n",
+       "3                            3.45   1480.0      0  \n",
+       "4                            2.93    735.0      0  \n",
+       "..                            ...      ...    ...  \n",
+       "173                          1.74    740.0      2  \n",
+       "174                          1.56    750.0      2  \n",
+       "175                          1.56    835.0      2  \n",
+       "176                          1.62    840.0      2  \n",
+       "177                          1.60    560.0      2  \n",
+       "\n",
+       "[178 rows x 14 columns]"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.datasets import load_wine\n",
+    "\n",
+    "# Load the Wine dataset\n",
+    "wine_data = load_wine()\n",
+    "\n",
+    "# Convert to DataFrame\n",
+    "wine_df = pd.DataFrame(wine_data.data, columns=wine_data.feature_names)\n",
+    "\n",
+    "# Bind the 'class' (wine target) to the DataFrame\n",
+    "wine_df['class'] = wine_data.target\n",
+    "\n",
+    "# Display the DataFrame\n",
+    "wine_df\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "721b2b17",
+   "metadata": {},
+   "source": [
+    "#### **Question 1:** \n",
+    "#### Data inspection\n",
+    "\n",
+    "Before fitting any model, it is essential to understand our data. **Use Python code** to answer the following questions about the **Wine dataset**:\n",
+    "\n",
+    "_(i)_ How many observations (rows) does the dataset contain?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "56916892",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "178"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# count number of rows\n",
+    "wine_df.shape[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f7573b59",
+   "metadata": {},
+   "source": [
+    "_(ii)_ How many variables (columns) does the dataset contain?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "df0ef103",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "14"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# count number of columns\n",
+    "wine_df.shape[1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cb5180c7",
+   "metadata": {},
+   "source": [
+    "_(iii)_ What is the 'variable type' of the response variable `class` (e.g., 'integer', 'category', etc.)? What are the 'levels' (unique values) of the variable?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "47989426",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dtype('int32')"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# data type of column class\n",
+    "wine_df.dtypes['class']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a25f5e1b",
+   "metadata": {},
+   "source": [
+    "\n",
+    "_(iv)_ How many predictor variables do we have (Hint: all variables other than `class`)? "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "bd7b0910",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 178 entries, 0 to 177\n",
+      "Data columns (total 14 columns):\n",
+      " #   Column                        Non-Null Count  Dtype  \n",
+      "---  ------                        --------------  -----  \n",
+      " 0   alcohol                       178 non-null    float64\n",
+      " 1   malic_acid                    178 non-null    float64\n",
+      " 2   ash                           178 non-null    float64\n",
+      " 3   alcalinity_of_ash             178 non-null    float64\n",
+      " 4   magnesium                     178 non-null    float64\n",
+      " 5   total_phenols                 178 non-null    float64\n",
+      " 6   flavanoids                    178 non-null    float64\n",
+      " 7   nonflavanoid_phenols          178 non-null    float64\n",
+      " 8   proanthocyanins               178 non-null    float64\n",
+      " 9   color_intensity               178 non-null    float64\n",
+      " 10  hue                           178 non-null    float64\n",
+      " 11  od280/od315_of_diluted_wines  178 non-null    float64\n",
+      " 12  proline                       178 non-null    float64\n",
+      " 13  class                         178 non-null    int32  \n",
+      "dtypes: float64(13), int32(1)\n",
+      "memory usage: 18.9 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Number of predictor variables is 12\n",
+    "wine_df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "156cc83a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>alcohol</th>\n",
+       "      <th>malic_acid</th>\n",
+       "      <th>ash</th>\n",
+       "      <th>alcalinity_of_ash</th>\n",
+       "      <th>magnesium</th>\n",
+       "      <th>total_phenols</th>\n",
+       "      <th>flavanoids</th>\n",
+       "      <th>nonflavanoid_phenols</th>\n",
+       "      <th>proanthocyanins</th>\n",
+       "      <th>color_intensity</th>\n",
+       "      <th>hue</th>\n",
+       "      <th>od280/od315_of_diluted_wines</th>\n",
+       "      <th>proline</th>\n",
+       "      <th>class</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>14.23</td>\n",
+       "      <td>1.71</td>\n",
+       "      <td>2.43</td>\n",
+       "      <td>15.6</td>\n",
+       "      <td>127.0</td>\n",
+       "      <td>2.80</td>\n",
+       "      <td>3.06</td>\n",
+       "      <td>0.28</td>\n",
+       "      <td>2.29</td>\n",
+       "      <td>5.64</td>\n",
+       "      <td>1.04</td>\n",
+       "      <td>3.92</td>\n",
+       "      <td>1065.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>13.20</td>\n",
+       "      <td>1.78</td>\n",
+       "      <td>2.14</td>\n",
+       "      <td>11.2</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>2.65</td>\n",
+       "      <td>2.76</td>\n",
+       "      <td>0.26</td>\n",
+       "      <td>1.28</td>\n",
+       "      <td>4.38</td>\n",
+       "      <td>1.05</td>\n",
+       "      <td>3.40</td>\n",
+       "      <td>1050.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>13.16</td>\n",
+       "      <td>2.36</td>\n",
+       "      <td>2.67</td>\n",
+       "      <td>18.6</td>\n",
+       "      <td>101.0</td>\n",
+       "      <td>2.80</td>\n",
+       "      <td>3.24</td>\n",
+       "      <td>0.30</td>\n",
+       "      <td>2.81</td>\n",
+       "      <td>5.68</td>\n",
+       "      <td>1.03</td>\n",
+       "      <td>3.17</td>\n",
+       "      <td>1185.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>14.37</td>\n",
+       "      <td>1.95</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>16.8</td>\n",
+       "      <td>113.0</td>\n",
+       "      <td>3.85</td>\n",
+       "      <td>3.49</td>\n",
+       "      <td>0.24</td>\n",
+       "      <td>2.18</td>\n",
+       "      <td>7.80</td>\n",
+       "      <td>0.86</td>\n",
+       "      <td>3.45</td>\n",
+       "      <td>1480.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>13.24</td>\n",
+       "      <td>2.59</td>\n",
+       "      <td>2.87</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>118.0</td>\n",
+       "      <td>2.80</td>\n",
+       "      <td>2.69</td>\n",
+       "      <td>0.39</td>\n",
+       "      <td>1.82</td>\n",
+       "      <td>4.32</td>\n",
+       "      <td>1.04</td>\n",
+       "      <td>2.93</td>\n",
+       "      <td>735.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>173</th>\n",
+       "      <td>13.71</td>\n",
+       "      <td>5.65</td>\n",
+       "      <td>2.45</td>\n",
+       "      <td>20.5</td>\n",
+       "      <td>95.0</td>\n",
+       "      <td>1.68</td>\n",
+       "      <td>0.61</td>\n",
+       "      <td>0.52</td>\n",
+       "      <td>1.06</td>\n",
+       "      <td>7.70</td>\n",
+       "      <td>0.64</td>\n",
+       "      <td>1.74</td>\n",
+       "      <td>740.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>174</th>\n",
+       "      <td>13.40</td>\n",
+       "      <td>3.91</td>\n",
+       "      <td>2.48</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>102.0</td>\n",
+       "      <td>1.80</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>0.43</td>\n",
+       "      <td>1.41</td>\n",
+       "      <td>7.30</td>\n",
+       "      <td>0.70</td>\n",
+       "      <td>1.56</td>\n",
+       "      <td>750.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>175</th>\n",
+       "      <td>13.27</td>\n",
+       "      <td>4.28</td>\n",
+       "      <td>2.26</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>120.0</td>\n",
+       "      <td>1.59</td>\n",
+       "      <td>0.69</td>\n",
+       "      <td>0.43</td>\n",
+       "      <td>1.35</td>\n",
+       "      <td>10.20</td>\n",
+       "      <td>0.59</td>\n",
+       "      <td>1.56</td>\n",
+       "      <td>835.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>176</th>\n",
+       "      <td>13.17</td>\n",
+       "      <td>2.59</td>\n",
+       "      <td>2.37</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>120.0</td>\n",
+       "      <td>1.65</td>\n",
+       "      <td>0.68</td>\n",
+       "      <td>0.53</td>\n",
+       "      <td>1.46</td>\n",
+       "      <td>9.30</td>\n",
+       "      <td>0.60</td>\n",
+       "      <td>1.62</td>\n",
+       "      <td>840.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>177</th>\n",
+       "      <td>14.13</td>\n",
+       "      <td>4.10</td>\n",
+       "      <td>2.74</td>\n",
+       "      <td>24.5</td>\n",
+       "      <td>96.0</td>\n",
+       "      <td>2.05</td>\n",
+       "      <td>0.76</td>\n",
+       "      <td>0.56</td>\n",
+       "      <td>1.35</td>\n",
+       "      <td>9.20</td>\n",
+       "      <td>0.61</td>\n",
+       "      <td>1.60</td>\n",
+       "      <td>560.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>178 rows × 14 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     alcohol  malic_acid   ash  alcalinity_of_ash  magnesium  total_phenols  \\\n",
+       "0      14.23        1.71  2.43               15.6      127.0           2.80   \n",
+       "1      13.20        1.78  2.14               11.2      100.0           2.65   \n",
+       "2      13.16        2.36  2.67               18.6      101.0           2.80   \n",
+       "3      14.37        1.95  2.50               16.8      113.0           3.85   \n",
+       "4      13.24        2.59  2.87               21.0      118.0           2.80   \n",
+       "..       ...         ...   ...                ...        ...            ...   \n",
+       "173    13.71        5.65  2.45               20.5       95.0           1.68   \n",
+       "174    13.40        3.91  2.48               23.0      102.0           1.80   \n",
+       "175    13.27        4.28  2.26               20.0      120.0           1.59   \n",
+       "176    13.17        2.59  2.37               20.0      120.0           1.65   \n",
+       "177    14.13        4.10  2.74               24.5       96.0           2.05   \n",
+       "\n",
+       "     flavanoids  nonflavanoid_phenols  proanthocyanins  color_intensity   hue  \\\n",
+       "0          3.06                  0.28             2.29             5.64  1.04   \n",
+       "1          2.76                  0.26             1.28             4.38  1.05   \n",
+       "2          3.24                  0.30             2.81             5.68  1.03   \n",
+       "3          3.49                  0.24             2.18             7.80  0.86   \n",
+       "4          2.69                  0.39             1.82             4.32  1.04   \n",
+       "..          ...                   ...              ...              ...   ...   \n",
+       "173        0.61                  0.52             1.06             7.70  0.64   \n",
+       "174        0.75                  0.43             1.41             7.30  0.70   \n",
+       "175        0.69                  0.43             1.35            10.20  0.59   \n",
+       "176        0.68                  0.53             1.46             9.30  0.60   \n",
+       "177        0.76                  0.56             1.35             9.20  0.61   \n",
+       "\n",
+       "     od280/od315_of_diluted_wines  proline  class  \n",
+       "0                            3.92   1065.0      0  \n",
+       "1                            3.40   1050.0      0  \n",
+       "2                            3.17   1185.0      0  \n",
+       "3                            3.45   1480.0      0  \n",
+       "4                            2.93    735.0      0  \n",
+       "..                            ...      ...    ...  \n",
+       "173                          1.74    740.0      2  \n",
+       "174                          1.56    750.0      2  \n",
+       "175                          1.56    835.0      2  \n",
+       "176                          1.62    840.0      2  \n",
+       "177                          1.60    560.0      2  \n",
+       "\n",
+       "[178 rows x 14 columns]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "wine_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d631e8e3",
+   "metadata": {},
+   "source": [
+    "You can use `print()` and `describe()` to help answer these questions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fa3832d7",
+   "metadata": {},
+   "source": [
+    "#### **Question 2:** \n",
+    "#### Standardization and data-splitting\n",
+    "\n",
+    "Next, we must preform 'pre-processing' or 'data munging', to prepare our data for classification/prediction. For KNN, there are three essential steps. A first essential step is to 'standardize' the predictor variables. We can achieve this using the scaler method, provided as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "cc899b59",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    alcohol  malic_acid       ash  alcalinity_of_ash  magnesium  \\\n",
+      "0  1.518613   -0.562250  0.232053          -1.169593   1.913905   \n",
+      "1  0.246290   -0.499413 -0.827996          -2.490847   0.018145   \n",
+      "2  0.196879    0.021231  1.109334          -0.268738   0.088358   \n",
+      "3  1.691550   -0.346811  0.487926          -0.809251   0.930918   \n",
+      "4  0.295700    0.227694  1.840403           0.451946   1.281985   \n",
+      "\n",
+      "   total_phenols  flavanoids  nonflavanoid_phenols  proanthocyanins  \\\n",
+      "0       0.808997    1.034819             -0.659563         1.224884   \n",
+      "1       0.568648    0.733629             -0.820719        -0.544721   \n",
+      "2       0.808997    1.215533             -0.498407         2.135968   \n",
+      "3       2.491446    1.466525             -0.981875         1.032155   \n",
+      "4       0.808997    0.663351              0.226796         0.401404   \n",
+      "\n",
+      "   color_intensity       hue  od280/od315_of_diluted_wines   proline  \n",
+      "0         0.251717  0.362177                      1.847920  1.013009  \n",
+      "1        -0.293321  0.406051                      1.113449  0.965242  \n",
+      "2         0.269020  0.318304                      0.788587  1.395148  \n",
+      "3         1.186068 -0.427544                      1.184071  2.334574  \n",
+      "4        -0.319276  0.362177                      0.449601 -0.037874  \n"
+     ]
+    }
+   ],
+   "source": [
+    "# Select predictors (excluding the last column)\n",
+    "predictors = wine_df.iloc[:, :-1]\n",
+    "\n",
+    "# Standardize the predictors\n",
+    "scaler = StandardScaler()\n",
+    "predictors_standardized = pd.DataFrame(scaler.fit_transform(predictors), columns=predictors.columns)\n",
+    "\n",
+    "# Display the head of the standardized predictors\n",
+    "print(predictors_standardized.head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9981ca48",
+   "metadata": {},
+   "source": [
+    "(i) Why is it important to standardize the predictor variables?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "403ef0bb",
+   "metadata": {},
+   "source": [
+    "> To make sure all the predictor valiables to have same scale therefore none of them will be dominated because of large scale and skew the classifcation result when using machine learning models that rely on distance metrics."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8e2e1bea",
+   "metadata": {},
+   "source": [
+    "(ii) Why did we elect not to standard our response variable `Class`?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fdee5a15",
+   "metadata": {},
+   "source": [
+    "> This is the variable we want to determine through the model and its scale would not affect the classification result  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8077ec21",
+   "metadata": {},
+   "source": [
+    "(iii) A second essential step is to set a random seed. Do so below (Hint: use the random.seed function). Why is setting a seed important? Is the particular seed value important? Why or why not?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f0676c21",
+   "metadata": {},
+   "source": [
+    "> Setting random seeed is important because it allow us to control the randomness in our code. Therefore we can repoduce the same result after running the code and do comparison or testing."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "36ab9229",
+   "metadata": {},
+   "source": [
+    "(iv) A third essential step is to split our standardized data into separate training and testing sets. We will split into 75% training and 25% testing. The provided code randomly partitions our data, and creates linked training sets for the predictors and response variables. \n",
+    "\n",
+    "Extend the code to create a non-overlapping test set for the predictors and response variables."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8e4a9dda",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# set a seed for reproducibility\n",
+    "np.random.seed(123)\n",
+    "# split the data into a training and testing set. hint: use train_test_split !\n",
+    "# Your code here ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "72c101f2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Do not touch - PLEASE IGNORE THIS CELL\n",
+    "#np.random.seed(123)\n",
+    "# Create a random vector of True and False values to split the data\n",
+    "#split = np.random.choice([True, False], size=len(predictors_standardized), replace=True, p=[0.75, 0.25])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "af2f9ce3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# split the data into training and testing set\n",
+    "#\n",
+    "predictor_S_train, predictor_S_test, label_c_train, label_c_test= train_test_split(\n",
+    "    predictors_standardized, wine_df['class'], train_size=0.75, shuffle= True,\n",
+    "    stratify=wine_df[\"class\"], \n",
+    "    random_state= 123\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "ea62ffc2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 133 entries, 28 to 109\n",
+      "Data columns (total 13 columns):\n",
+      " #   Column                        Non-Null Count  Dtype  \n",
+      "---  ------                        --------------  -----  \n",
+      " 0   alcohol                       133 non-null    float64\n",
+      " 1   malic_acid                    133 non-null    float64\n",
+      " 2   ash                           133 non-null    float64\n",
+      " 3   alcalinity_of_ash             133 non-null    float64\n",
+      " 4   magnesium                     133 non-null    float64\n",
+      " 5   total_phenols                 133 non-null    float64\n",
+      " 6   flavanoids                    133 non-null    float64\n",
+      " 7   nonflavanoid_phenols          133 non-null    float64\n",
+      " 8   proanthocyanins               133 non-null    float64\n",
+      " 9   color_intensity               133 non-null    float64\n",
+      " 10  hue                           133 non-null    float64\n",
+      " 11  od280/od315_of_diluted_wines  133 non-null    float64\n",
+      " 12  proline                       133 non-null    float64\n",
+      "dtypes: float64(13)\n",
+      "memory usage: 14.5 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "predictor_S_train.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "0159273e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 45 entries, 102 to 177\n",
+      "Data columns (total 13 columns):\n",
+      " #   Column                        Non-Null Count  Dtype  \n",
+      "---  ------                        --------------  -----  \n",
+      " 0   alcohol                       45 non-null     float64\n",
+      " 1   malic_acid                    45 non-null     float64\n",
+      " 2   ash                           45 non-null     float64\n",
+      " 3   alcalinity_of_ash             45 non-null     float64\n",
+      " 4   magnesium                     45 non-null     float64\n",
+      " 5   total_phenols                 45 non-null     float64\n",
+      " 6   flavanoids                    45 non-null     float64\n",
+      " 7   nonflavanoid_phenols          45 non-null     float64\n",
+      " 8   proanthocyanins               45 non-null     float64\n",
+      " 9   color_intensity               45 non-null     float64\n",
+      " 10  hue                           45 non-null     float64\n",
+      " 11  od280/od315_of_diluted_wines  45 non-null     float64\n",
+      " 12  proline                       45 non-null     float64\n",
+      "dtypes: float64(13)\n",
+      "memory usage: 4.9 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "predictor_S_test.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4604ee03",
+   "metadata": {},
+   "source": [
+    "#### **Question 3:**\n",
+    "#### Model initialization and cross-validation\n",
+    "We are finally set to fit the KNN model. \n",
+    "\n",
+    "\n",
+    "Perform a grid search to tune the `n_neighbors` hyperparameter using 10-fold cross-validation. Follow these steps:\n",
+    "\n",
+    "1. Initialize the KNN classifier using `KNeighborsClassifier()`.\n",
+    "2. Define a parameter grid for `n_neighbors` ranging from 1 to 50.\n",
+    "3. Implement a grid search using `GridSearchCV` with 10-fold cross-validation to find the optimal number of neighbors.\n",
+    "4. After fitting the model on the training data, identify and return the best value for `n_neighbors` based on the grid search results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "334fc8d0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>alcohol</th>\n",
+       "      <th>malic_acid</th>\n",
+       "      <th>ash</th>\n",
+       "      <th>alcalinity_of_ash</th>\n",
+       "      <th>magnesium</th>\n",
+       "      <th>total_phenols</th>\n",
+       "      <th>flavanoids</th>\n",
+       "      <th>nonflavanoid_phenols</th>\n",
+       "      <th>proanthocyanins</th>\n",
+       "      <th>color_intensity</th>\n",
+       "      <th>hue</th>\n",
+       "      <th>od280/od315_of_diluted_wines</th>\n",
+       "      <th>proline</th>\n",
+       "      <th>class</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.518613</td>\n",
+       "      <td>-0.562250</td>\n",
+       "      <td>0.232053</td>\n",
+       "      <td>-1.169593</td>\n",
+       "      <td>1.913905</td>\n",
+       "      <td>0.808997</td>\n",
+       "      <td>1.034819</td>\n",
+       "      <td>-0.659563</td>\n",
+       "      <td>1.224884</td>\n",
+       "      <td>0.251717</td>\n",
+       "      <td>0.362177</td>\n",
+       "      <td>1.847920</td>\n",
+       "      <td>1.013009</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.246290</td>\n",
+       "      <td>-0.499413</td>\n",
+       "      <td>-0.827996</td>\n",
+       "      <td>-2.490847</td>\n",
+       "      <td>0.018145</td>\n",
+       "      <td>0.568648</td>\n",
+       "      <td>0.733629</td>\n",
+       "      <td>-0.820719</td>\n",
+       "      <td>-0.544721</td>\n",
+       "      <td>-0.293321</td>\n",
+       "      <td>0.406051</td>\n",
+       "      <td>1.113449</td>\n",
+       "      <td>0.965242</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.196879</td>\n",
+       "      <td>0.021231</td>\n",
+       "      <td>1.109334</td>\n",
+       "      <td>-0.268738</td>\n",
+       "      <td>0.088358</td>\n",
+       "      <td>0.808997</td>\n",
+       "      <td>1.215533</td>\n",
+       "      <td>-0.498407</td>\n",
+       "      <td>2.135968</td>\n",
+       "      <td>0.269020</td>\n",
+       "      <td>0.318304</td>\n",
+       "      <td>0.788587</td>\n",
+       "      <td>1.395148</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.691550</td>\n",
+       "      <td>-0.346811</td>\n",
+       "      <td>0.487926</td>\n",
+       "      <td>-0.809251</td>\n",
+       "      <td>0.930918</td>\n",
+       "      <td>2.491446</td>\n",
+       "      <td>1.466525</td>\n",
+       "      <td>-0.981875</td>\n",
+       "      <td>1.032155</td>\n",
+       "      <td>1.186068</td>\n",
+       "      <td>-0.427544</td>\n",
+       "      <td>1.184071</td>\n",
+       "      <td>2.334574</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.295700</td>\n",
+       "      <td>0.227694</td>\n",
+       "      <td>1.840403</td>\n",
+       "      <td>0.451946</td>\n",
+       "      <td>1.281985</td>\n",
+       "      <td>0.808997</td>\n",
+       "      <td>0.663351</td>\n",
+       "      <td>0.226796</td>\n",
+       "      <td>0.401404</td>\n",
+       "      <td>-0.319276</td>\n",
+       "      <td>0.362177</td>\n",
+       "      <td>0.449601</td>\n",
+       "      <td>-0.037874</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>173</th>\n",
+       "      <td>0.876275</td>\n",
+       "      <td>2.974543</td>\n",
+       "      <td>0.305159</td>\n",
+       "      <td>0.301803</td>\n",
+       "      <td>-0.332922</td>\n",
+       "      <td>-0.985614</td>\n",
+       "      <td>-1.424900</td>\n",
+       "      <td>1.274310</td>\n",
+       "      <td>-0.930179</td>\n",
+       "      <td>1.142811</td>\n",
+       "      <td>-1.392758</td>\n",
+       "      <td>-1.231206</td>\n",
+       "      <td>-0.021952</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>174</th>\n",
+       "      <td>0.493343</td>\n",
+       "      <td>1.412609</td>\n",
+       "      <td>0.414820</td>\n",
+       "      <td>1.052516</td>\n",
+       "      <td>0.158572</td>\n",
+       "      <td>-0.793334</td>\n",
+       "      <td>-1.284344</td>\n",
+       "      <td>0.549108</td>\n",
+       "      <td>-0.316950</td>\n",
+       "      <td>0.969783</td>\n",
+       "      <td>-1.129518</td>\n",
+       "      <td>-1.485445</td>\n",
+       "      <td>0.009893</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>175</th>\n",
+       "      <td>0.332758</td>\n",
+       "      <td>1.744744</td>\n",
+       "      <td>-0.389355</td>\n",
+       "      <td>0.151661</td>\n",
+       "      <td>1.422412</td>\n",
+       "      <td>-1.129824</td>\n",
+       "      <td>-1.344582</td>\n",
+       "      <td>0.549108</td>\n",
+       "      <td>-0.422075</td>\n",
+       "      <td>2.224236</td>\n",
+       "      <td>-1.612125</td>\n",
+       "      <td>-1.485445</td>\n",
+       "      <td>0.280575</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>176</th>\n",
+       "      <td>0.209232</td>\n",
+       "      <td>0.227694</td>\n",
+       "      <td>0.012732</td>\n",
+       "      <td>0.151661</td>\n",
+       "      <td>1.422412</td>\n",
+       "      <td>-1.033684</td>\n",
+       "      <td>-1.354622</td>\n",
+       "      <td>1.354888</td>\n",
+       "      <td>-0.229346</td>\n",
+       "      <td>1.834923</td>\n",
+       "      <td>-1.568252</td>\n",
+       "      <td>-1.400699</td>\n",
+       "      <td>0.296498</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>177</th>\n",
+       "      <td>1.395086</td>\n",
+       "      <td>1.583165</td>\n",
+       "      <td>1.365208</td>\n",
+       "      <td>1.502943</td>\n",
+       "      <td>-0.262708</td>\n",
+       "      <td>-0.392751</td>\n",
+       "      <td>-1.274305</td>\n",
+       "      <td>1.596623</td>\n",
+       "      <td>-0.422075</td>\n",
+       "      <td>1.791666</td>\n",
+       "      <td>-1.524378</td>\n",
+       "      <td>-1.428948</td>\n",
+       "      <td>-0.595160</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>178 rows × 14 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      alcohol  malic_acid       ash  alcalinity_of_ash  magnesium  \\\n",
+       "0    1.518613   -0.562250  0.232053          -1.169593   1.913905   \n",
+       "1    0.246290   -0.499413 -0.827996          -2.490847   0.018145   \n",
+       "2    0.196879    0.021231  1.109334          -0.268738   0.088358   \n",
+       "3    1.691550   -0.346811  0.487926          -0.809251   0.930918   \n",
+       "4    0.295700    0.227694  1.840403           0.451946   1.281985   \n",
+       "..        ...         ...       ...                ...        ...   \n",
+       "173  0.876275    2.974543  0.305159           0.301803  -0.332922   \n",
+       "174  0.493343    1.412609  0.414820           1.052516   0.158572   \n",
+       "175  0.332758    1.744744 -0.389355           0.151661   1.422412   \n",
+       "176  0.209232    0.227694  0.012732           0.151661   1.422412   \n",
+       "177  1.395086    1.583165  1.365208           1.502943  -0.262708   \n",
+       "\n",
+       "     total_phenols  flavanoids  nonflavanoid_phenols  proanthocyanins  \\\n",
+       "0         0.808997    1.034819             -0.659563         1.224884   \n",
+       "1         0.568648    0.733629             -0.820719        -0.544721   \n",
+       "2         0.808997    1.215533             -0.498407         2.135968   \n",
+       "3         2.491446    1.466525             -0.981875         1.032155   \n",
+       "4         0.808997    0.663351              0.226796         0.401404   \n",
+       "..             ...         ...                   ...              ...   \n",
+       "173      -0.985614   -1.424900              1.274310        -0.930179   \n",
+       "174      -0.793334   -1.284344              0.549108        -0.316950   \n",
+       "175      -1.129824   -1.344582              0.549108        -0.422075   \n",
+       "176      -1.033684   -1.354622              1.354888        -0.229346   \n",
+       "177      -0.392751   -1.274305              1.596623        -0.422075   \n",
+       "\n",
+       "     color_intensity       hue  od280/od315_of_diluted_wines   proline  class  \n",
+       "0           0.251717  0.362177                      1.847920  1.013009      0  \n",
+       "1          -0.293321  0.406051                      1.113449  0.965242      0  \n",
+       "2           0.269020  0.318304                      0.788587  1.395148      0  \n",
+       "3           1.186068 -0.427544                      1.184071  2.334574      0  \n",
+       "4          -0.319276  0.362177                      0.449601 -0.037874      0  \n",
+       "..               ...       ...                           ...       ...    ...  \n",
+       "173         1.142811 -1.392758                     -1.231206 -0.021952      2  \n",
+       "174         0.969783 -1.129518                     -1.485445  0.009893      2  \n",
+       "175         2.224236 -1.612125                     -1.485445  0.280575      2  \n",
+       "176         1.834923 -1.568252                     -1.400699  0.296498      2  \n",
+       "177         1.791666 -1.524378                     -1.428948 -0.595160      2  \n",
+       "\n",
+       "[178 rows x 14 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# prepare and scale the df to initialize the knn model\n",
+    "# create a df that only class is not scaled but the other columns are standardized.\n",
+    "\n",
+    "standardized_wine = wine_df.copy()\n",
+    "\n",
+    "columns_to_exclude = ['class']\n",
+    "\n",
+    "columns_to_scale = standardized_wine.columns.difference(columns_to_exclude)\n",
+    "\n",
+    "scaler = StandardScaler()\n",
+    "standardized_wine[columns_to_scale] = scaler.fit_transform(standardized_wine[columns_to_scale])\n",
+    "standardized_wine"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "2f36e25c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# split the data into training and testing set\n",
+    "wine_train, wine_test = train_test_split(\n",
+    "    standardized_wine, train_size=0.75, shuffle= True,\n",
+    "    stratify=standardized_wine[\"class\"], random_state= 123\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "73531bc8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 133 entries, 78 to 66\n",
+      "Data columns (total 14 columns):\n",
+      " #   Column                        Non-Null Count  Dtype  \n",
+      "---  ------                        --------------  -----  \n",
+      " 0   alcohol                       133 non-null    float64\n",
+      " 1   malic_acid                    133 non-null    float64\n",
+      " 2   ash                           133 non-null    float64\n",
+      " 3   alcalinity_of_ash             133 non-null    float64\n",
+      " 4   magnesium                     133 non-null    float64\n",
+      " 5   total_phenols                 133 non-null    float64\n",
+      " 6   flavanoids                    133 non-null    float64\n",
+      " 7   nonflavanoid_phenols          133 non-null    float64\n",
+      " 8   proanthocyanins               133 non-null    float64\n",
+      " 9   color_intensity               133 non-null    float64\n",
+      " 10  hue                           133 non-null    float64\n",
+      " 11  od280/od315_of_diluted_wines  133 non-null    float64\n",
+      " 12  proline                       133 non-null    float64\n",
+      " 13  class                         133 non-null    int32  \n",
+      "dtypes: float64(13), int32(1)\n",
+      "memory usage: 15.1 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "wine_train.info() # to check if the split is right"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "c4d9c807",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 45 entries, 102 to 177\n",
+      "Data columns (total 14 columns):\n",
+      " #   Column                        Non-Null Count  Dtype  \n",
+      "---  ------                        --------------  -----  \n",
+      " 0   alcohol                       45 non-null     float64\n",
+      " 1   malic_acid                    45 non-null     float64\n",
+      " 2   ash                           45 non-null     float64\n",
+      " 3   alcalinity_of_ash             45 non-null     float64\n",
+      " 4   magnesium                     45 non-null     float64\n",
+      " 5   total_phenols                 45 non-null     float64\n",
+      " 6   flavanoids                    45 non-null     float64\n",
+      " 7   nonflavanoid_phenols          45 non-null     float64\n",
+      " 8   proanthocyanins               45 non-null     float64\n",
+      " 9   color_intensity               45 non-null     float64\n",
+      " 10  hue                           45 non-null     float64\n",
+      " 11  od280/od315_of_diluted_wines  45 non-null     float64\n",
+      " 12  proline                       45 non-null     float64\n",
+      " 13  class                         45 non-null     int32  \n",
+      "dtypes: float64(13), int32(1)\n",
+      "memory usage: 5.1 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "wine_test.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "905ed370",
+   "metadata": {},
+   "source": [
+    "Question 3 - point 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "08818c64",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# initiate KNN\n",
+    "knn = KNeighborsClassifier(n_neighbors=5)\n",
+    "                           "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "42b204f2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# define x and y for KNN\n",
+    "X1 = predictor_S_train\n",
+    "y1 = label_c_train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "5f52c83b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# define x and y for KNN\n",
+    "X = standardized_wine[columns_to_scale]\n",
+    "y = standardized_wine['class']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "44a9ab17",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-3 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-3 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-3 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-3 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-3 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;KNeighborsClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsClassifier()</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "KNeighborsClassifier()"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# fitting KNN\n",
+    "knn.fit(X1,y1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9ffb8bf8",
+   "metadata": {},
+   "source": [
+    "Question 3  point 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "58c21754",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# implementing a gridSearch , define pararmeter grid, riging from 1 to 50\n",
+    "parameter_grid = {\n",
+    "    \"n_neighbors\": range(1, 50, 3),\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ddf8185b",
+   "metadata": {},
+   "source": [
+    "Question 3 point 3 "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "b3ad80ad",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# use function to search best K -- implementing a gridSearch \n",
+    "wine_tune_grid = GridSearchCV(\n",
+    "    estimator=knn,\n",
+    "    param_grid=parameter_grid,\n",
+    "    cv=10\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "672c7471",
+   "metadata": {},
+   "source": [
+    "Question 3 - point 4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "9fcf66a5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-2 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-2 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-2 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-2 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=10, estimator=KNeighborsClassifier(),\n",
+       "             param_grid={&#x27;n_neighbors&#x27;: range(1, 50, 3)})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=10, estimator=KNeighborsClassifier(),\n",
+       "             param_grid={&#x27;n_neighbors&#x27;: range(1, 50, 3)})</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">best_estimator_: KNeighborsClassifier</label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsClassifier(n_neighbors=7)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;KNeighborsClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsClassifier(n_neighbors=7)</pre></div> </div></div></div></div></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "GridSearchCV(cv=10, estimator=KNeighborsClassifier(),\n",
+       "             param_grid={'n_neighbors': range(1, 50, 3)})"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# fitting the x and y\n",
+    "wine_tune_grid.fit(\n",
+    "    wine_train[columns_to_scale],\n",
+    "    wine_train[\"class\"]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "e0cca0de",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>params</th>\n",
+       "      <th>mean_test_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>{'n_neighbors': 1}</td>\n",
+       "      <td>0.954396</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>{'n_neighbors': 4}</td>\n",
+       "      <td>0.954945</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>{'n_neighbors': 7}</td>\n",
+       "      <td>0.977473</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>{'n_neighbors': 10}</td>\n",
+       "      <td>0.954396</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>{'n_neighbors': 13}</td>\n",
+       "      <td>0.977473</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>{'n_neighbors': 16}</td>\n",
+       "      <td>0.962637</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>{'n_neighbors': 19}</td>\n",
+       "      <td>0.962637</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>{'n_neighbors': 22}</td>\n",
+       "      <td>0.970330</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>{'n_neighbors': 25}</td>\n",
+       "      <td>0.954945</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>{'n_neighbors': 28}</td>\n",
+       "      <td>0.962637</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>{'n_neighbors': 31}</td>\n",
+       "      <td>0.955495</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>{'n_neighbors': 34}</td>\n",
+       "      <td>0.963187</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>{'n_neighbors': 37}</td>\n",
+       "      <td>0.962637</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>{'n_neighbors': 40}</td>\n",
+       "      <td>0.954945</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>{'n_neighbors': 43}</td>\n",
+       "      <td>0.954945</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>{'n_neighbors': 46}</td>\n",
+       "      <td>0.947253</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>{'n_neighbors': 49}</td>\n",
+       "      <td>0.947253</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 params  mean_test_score\n",
+       "0    {'n_neighbors': 1}         0.954396\n",
+       "1    {'n_neighbors': 4}         0.954945\n",
+       "2    {'n_neighbors': 7}         0.977473\n",
+       "3   {'n_neighbors': 10}         0.954396\n",
+       "4   {'n_neighbors': 13}         0.977473\n",
+       "5   {'n_neighbors': 16}         0.962637\n",
+       "6   {'n_neighbors': 19}         0.962637\n",
+       "7   {'n_neighbors': 22}         0.970330\n",
+       "8   {'n_neighbors': 25}         0.954945\n",
+       "9   {'n_neighbors': 28}         0.962637\n",
+       "10  {'n_neighbors': 31}         0.955495\n",
+       "11  {'n_neighbors': 34}         0.963187\n",
+       "12  {'n_neighbors': 37}         0.962637\n",
+       "13  {'n_neighbors': 40}         0.954945\n",
+       "14  {'n_neighbors': 43}         0.954945\n",
+       "15  {'n_neighbors': 46}         0.947253\n",
+       "16  {'n_neighbors': 49}         0.947253"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# check out the accuracy\n",
+    "accuracies_grid = pd.DataFrame(wine_tune_grid.cv_results_)\n",
+    "#accuracies_grid\n",
+    "accuracies_grid [[\"params\",\"mean_test_score\"]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "369cdf3b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'n_neighbors': 7}"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# optimal number of neighbours\n",
+    "wine_tune_grid.best_params_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3f76bf62",
+   "metadata": {},
+   "source": [
+    "#### **Question 4:**\n",
+    "#### Model evaluation\n",
+    "\n",
+    "Using the best value for `n_neighbors`, fit a KNN model on the training data and evaluate its performance on the test set using `accuracy_score`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ffefa9f2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your code here..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6f8a69db",
+   "metadata": {},
+   "source": [
+    "# Criteria\n",
+    "\n",
+    "\n",
+    "| **Criteria**                                           | **Complete**                                      | **Incomplete**                                    |\n",
+    "|--------------------------------------------------------|---------------------------------------------------|--------------------------------------------------|\n",
+    "| **Data Inspection**                                    | Data is inspected for number of variables, observations and data types. | Data inspection is missing or incomplete.         |\n",
+    "| **Data Scaling**                                       | Data scaling or normalization is applied where necessary (e.g., using `StandardScaler`). | Data scaling or normalization is missing or incorrectly applied. |\n",
+    "| **Model Initialization**                               | The KNN model is correctly initialized and a random seed is set for reproducibility.            | The KNN model is not initialized, is incorrect, or lacks a random seed for reproducibility. |\n",
+    "| **Parameter Grid for `n_neighbors`**                   | The parameter grid for `n_neighbors` is correctly defined. | The parameter grid is missing or incorrectly defined. |\n",
+    "| **Cross-Validation Setup**                             | Cross-validation is set up correctly with 10 folds. | Cross-validation is missing or incorrectly set up. |\n",
+    "| **Best Hyperparameter (`n_neighbors`) Selection**       | The best value for `n_neighbors` is identified using the grid search results. | The best `n_neighbors` is not selected or incorrect. |\n",
+    "| **Model Evaluation on Test Data**                      | The model is evaluated on the test data using accuracy. | The model evaluation is missing or uses the wrong metric. |\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0b4390cc",
+   "metadata": {},
+   "source": [
+    "## Submission Information\n",
+    "\n",
+    "🚨 **Please review our [Assignment Submission Guide](https://github.com/UofT-DSI/onboarding/blob/main/onboarding_documents/submissions.md)** 🚨 for detailed instructions on how to format, branch, and submit your work. Following these guidelines is crucial for your submissions to be evaluated correctly.\n",
+    "\n",
+    "### Note:\n",
+    "\n",
+    "If you like, you may collaborate with others in the cohort. If you choose to do so, please indicate with whom you have worked with in your pull request by tagging their GitHub username. Separate submissions are required.\n",
+    "\n",
+    "### Submission Parameters:\n",
+    "* Submission Due Date: `11:59 PM - 01/12/2025`\n",
+    "* The branch name for your repo should be: `assignment-1`\n",
+    "* What to submit for this assignment:\n",
+    "    * This Jupyter Notebook (assignment_1.ipynb) should be populated and should be the only change in your pull request.\n",
+    "* What the pull request link should look like for this assignment: `https://github.com/<your_github_username>/LCR/pull/<pr_id>`\n",
+    "    * Open a private window in your browser. Copy and paste the link to your pull request into the address bar. Make sure you can see your pull request properly. This helps the technical facilitator and learning support staff review your submission easily.\n",
+    "\n",
+    "Checklist:\n",
+    "- [ ] Created a branch with the correct naming convention.\n",
+    "- [ ] Ensured that the repository is public.\n",
+    "- [ ] Reviewed the PR description guidelines and adhered to them.\n",
+    "- [ ] Verify that the link is accessible in a private browser window.\n",
+    "\n",
+    "If you encounter any difficulties or have questions, please don't hesitate to reach out to our team via our Slack at `#cohort-4-help`. Our Technical Facilitators and Learning Support staff are here to help you navigate any challenges.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "dsi_participant",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/02_activities/simple sample for train test split.ipynb b/02_activities/simple sample for train test split.ipynb
new file mode 100644
index 000000000..3f32ef7e9
--- /dev/null
+++ b/02_activities/simple sample for train test split.ipynb	
@@ -0,0 +1,170 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0, 1],\n",
+       "       [2, 3],\n",
+       "       [4, 5],\n",
+       "       [6, 7],\n",
+       "       [8, 9]])"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "X, y = np.arange(10).reshape((5, 2)), range(5)\n",
+    "\n",
+    "\n",
+    "X"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[0, 1, 2, 3, 4]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list (y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(\n",
+    "\n",
+    "    X, y, test_size=0.2, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[8, 9],\n",
+       "       [4, 5],\n",
+       "       [0, 1],\n",
+       "       [6, 7]])"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[2, 3]])"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[4, 2, 0, 3]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_test"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "dsi_participant",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/load_file_test.ipynb b/load_file_test.ipynb
new file mode 100644
index 000000000..9c355e1f9
--- /dev/null
+++ b/load_file_test.ipynb
@@ -0,0 +1,438 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load in libraries\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.colors as mcolors\n",
+    "from mpl_toolkits import mplot3d"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>diagnosis</th>\n",
+       "      <th>radius_mean</th>\n",
+       "      <th>texture_mean</th>\n",
+       "      <th>perimeter_mean</th>\n",
+       "      <th>area_mean</th>\n",
+       "      <th>smoothness_mean</th>\n",
+       "      <th>compactness_mean</th>\n",
+       "      <th>concavity_mean</th>\n",
+       "      <th>concave points_mean</th>\n",
+       "      <th>...</th>\n",
+       "      <th>radius_worst</th>\n",
+       "      <th>texture_worst</th>\n",
+       "      <th>perimeter_worst</th>\n",
+       "      <th>area_worst</th>\n",
+       "      <th>smoothness_worst</th>\n",
+       "      <th>compactness_worst</th>\n",
+       "      <th>concavity_worst</th>\n",
+       "      <th>concave points_worst</th>\n",
+       "      <th>symmetry_worst</th>\n",
+       "      <th>fractal_dimension_worst</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>842302</td>\n",
+       "      <td>M</td>\n",
+       "      <td>17.99</td>\n",
+       "      <td>10.38</td>\n",
+       "      <td>122.80</td>\n",
+       "      <td>1001.0</td>\n",
+       "      <td>0.11840</td>\n",
+       "      <td>0.27760</td>\n",
+       "      <td>0.30010</td>\n",
+       "      <td>0.14710</td>\n",
+       "      <td>...</td>\n",
+       "      <td>25.380</td>\n",
+       "      <td>17.33</td>\n",
+       "      <td>184.60</td>\n",
+       "      <td>2019.0</td>\n",
+       "      <td>0.16220</td>\n",
+       "      <td>0.66560</td>\n",
+       "      <td>0.7119</td>\n",
+       "      <td>0.2654</td>\n",
+       "      <td>0.4601</td>\n",
+       "      <td>0.11890</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>842517</td>\n",
+       "      <td>M</td>\n",
+       "      <td>20.57</td>\n",
+       "      <td>17.77</td>\n",
+       "      <td>132.90</td>\n",
+       "      <td>1326.0</td>\n",
+       "      <td>0.08474</td>\n",
+       "      <td>0.07864</td>\n",
+       "      <td>0.08690</td>\n",
+       "      <td>0.07017</td>\n",
+       "      <td>...</td>\n",
+       "      <td>24.990</td>\n",
+       "      <td>23.41</td>\n",
+       "      <td>158.80</td>\n",
+       "      <td>1956.0</td>\n",
+       "      <td>0.12380</td>\n",
+       "      <td>0.18660</td>\n",
+       "      <td>0.2416</td>\n",
+       "      <td>0.1860</td>\n",
+       "      <td>0.2750</td>\n",
+       "      <td>0.08902</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>84300903</td>\n",
+       "      <td>M</td>\n",
+       "      <td>19.69</td>\n",
+       "      <td>21.25</td>\n",
+       "      <td>130.00</td>\n",
+       "      <td>1203.0</td>\n",
+       "      <td>0.10960</td>\n",
+       "      <td>0.15990</td>\n",
+       "      <td>0.19740</td>\n",
+       "      <td>0.12790</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23.570</td>\n",
+       "      <td>25.53</td>\n",
+       "      <td>152.50</td>\n",
+       "      <td>1709.0</td>\n",
+       "      <td>0.14440</td>\n",
+       "      <td>0.42450</td>\n",
+       "      <td>0.4504</td>\n",
+       "      <td>0.2430</td>\n",
+       "      <td>0.3613</td>\n",
+       "      <td>0.08758</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>84348301</td>\n",
+       "      <td>M</td>\n",
+       "      <td>11.42</td>\n",
+       "      <td>20.38</td>\n",
+       "      <td>77.58</td>\n",
+       "      <td>386.1</td>\n",
+       "      <td>0.14250</td>\n",
+       "      <td>0.28390</td>\n",
+       "      <td>0.24140</td>\n",
+       "      <td>0.10520</td>\n",
+       "      <td>...</td>\n",
+       "      <td>14.910</td>\n",
+       "      <td>26.50</td>\n",
+       "      <td>98.87</td>\n",
+       "      <td>567.7</td>\n",
+       "      <td>0.20980</td>\n",
+       "      <td>0.86630</td>\n",
+       "      <td>0.6869</td>\n",
+       "      <td>0.2575</td>\n",
+       "      <td>0.6638</td>\n",
+       "      <td>0.17300</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>84358402</td>\n",
+       "      <td>M</td>\n",
+       "      <td>20.29</td>\n",
+       "      <td>14.34</td>\n",
+       "      <td>135.10</td>\n",
+       "      <td>1297.0</td>\n",
+       "      <td>0.10030</td>\n",
+       "      <td>0.13280</td>\n",
+       "      <td>0.19800</td>\n",
+       "      <td>0.10430</td>\n",
+       "      <td>...</td>\n",
+       "      <td>22.540</td>\n",
+       "      <td>16.67</td>\n",
+       "      <td>152.20</td>\n",
+       "      <td>1575.0</td>\n",
+       "      <td>0.13740</td>\n",
+       "      <td>0.20500</td>\n",
+       "      <td>0.4000</td>\n",
+       "      <td>0.1625</td>\n",
+       "      <td>0.2364</td>\n",
+       "      <td>0.07678</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>564</th>\n",
+       "      <td>926424</td>\n",
+       "      <td>M</td>\n",
+       "      <td>21.56</td>\n",
+       "      <td>22.39</td>\n",
+       "      <td>142.00</td>\n",
+       "      <td>1479.0</td>\n",
+       "      <td>0.11100</td>\n",
+       "      <td>0.11590</td>\n",
+       "      <td>0.24390</td>\n",
+       "      <td>0.13890</td>\n",
+       "      <td>...</td>\n",
+       "      <td>25.450</td>\n",
+       "      <td>26.40</td>\n",
+       "      <td>166.10</td>\n",
+       "      <td>2027.0</td>\n",
+       "      <td>0.14100</td>\n",
+       "      <td>0.21130</td>\n",
+       "      <td>0.4107</td>\n",
+       "      <td>0.2216</td>\n",
+       "      <td>0.2060</td>\n",
+       "      <td>0.07115</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>565</th>\n",
+       "      <td>926682</td>\n",
+       "      <td>M</td>\n",
+       "      <td>20.13</td>\n",
+       "      <td>28.25</td>\n",
+       "      <td>131.20</td>\n",
+       "      <td>1261.0</td>\n",
+       "      <td>0.09780</td>\n",
+       "      <td>0.10340</td>\n",
+       "      <td>0.14400</td>\n",
+       "      <td>0.09791</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23.690</td>\n",
+       "      <td>38.25</td>\n",
+       "      <td>155.00</td>\n",
+       "      <td>1731.0</td>\n",
+       "      <td>0.11660</td>\n",
+       "      <td>0.19220</td>\n",
+       "      <td>0.3215</td>\n",
+       "      <td>0.1628</td>\n",
+       "      <td>0.2572</td>\n",
+       "      <td>0.06637</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>566</th>\n",
+       "      <td>926954</td>\n",
+       "      <td>M</td>\n",
+       "      <td>16.60</td>\n",
+       "      <td>28.08</td>\n",
+       "      <td>108.30</td>\n",
+       "      <td>858.1</td>\n",
+       "      <td>0.08455</td>\n",
+       "      <td>0.10230</td>\n",
+       "      <td>0.09251</td>\n",
+       "      <td>0.05302</td>\n",
+       "      <td>...</td>\n",
+       "      <td>18.980</td>\n",
+       "      <td>34.12</td>\n",
+       "      <td>126.70</td>\n",
+       "      <td>1124.0</td>\n",
+       "      <td>0.11390</td>\n",
+       "      <td>0.30940</td>\n",
+       "      <td>0.3403</td>\n",
+       "      <td>0.1418</td>\n",
+       "      <td>0.2218</td>\n",
+       "      <td>0.07820</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>567</th>\n",
+       "      <td>927241</td>\n",
+       "      <td>M</td>\n",
+       "      <td>20.60</td>\n",
+       "      <td>29.33</td>\n",
+       "      <td>140.10</td>\n",
+       "      <td>1265.0</td>\n",
+       "      <td>0.11780</td>\n",
+       "      <td>0.27700</td>\n",
+       "      <td>0.35140</td>\n",
+       "      <td>0.15200</td>\n",
+       "      <td>...</td>\n",
+       "      <td>25.740</td>\n",
+       "      <td>39.42</td>\n",
+       "      <td>184.60</td>\n",
+       "      <td>1821.0</td>\n",
+       "      <td>0.16500</td>\n",
+       "      <td>0.86810</td>\n",
+       "      <td>0.9387</td>\n",
+       "      <td>0.2650</td>\n",
+       "      <td>0.4087</td>\n",
+       "      <td>0.12400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>568</th>\n",
+       "      <td>92751</td>\n",
+       "      <td>B</td>\n",
+       "      <td>7.76</td>\n",
+       "      <td>24.54</td>\n",
+       "      <td>47.92</td>\n",
+       "      <td>181.0</td>\n",
+       "      <td>0.05263</td>\n",
+       "      <td>0.04362</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>9.456</td>\n",
+       "      <td>30.37</td>\n",
+       "      <td>59.16</td>\n",
+       "      <td>268.6</td>\n",
+       "      <td>0.08996</td>\n",
+       "      <td>0.06444</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.2871</td>\n",
+       "      <td>0.07039</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>569 rows × 32 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
+       "0      842302         M        17.99         10.38          122.80     1001.0   \n",
+       "1      842517         M        20.57         17.77          132.90     1326.0   \n",
+       "2    84300903         M        19.69         21.25          130.00     1203.0   \n",
+       "3    84348301         M        11.42         20.38           77.58      386.1   \n",
+       "4    84358402         M        20.29         14.34          135.10     1297.0   \n",
+       "..        ...       ...          ...           ...             ...        ...   \n",
+       "564    926424         M        21.56         22.39          142.00     1479.0   \n",
+       "565    926682         M        20.13         28.25          131.20     1261.0   \n",
+       "566    926954         M        16.60         28.08          108.30      858.1   \n",
+       "567    927241         M        20.60         29.33          140.10     1265.0   \n",
+       "568     92751         B         7.76         24.54           47.92      181.0   \n",
+       "\n",
+       "     smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
+       "0            0.11840           0.27760         0.30010              0.14710   \n",
+       "1            0.08474           0.07864         0.08690              0.07017   \n",
+       "2            0.10960           0.15990         0.19740              0.12790   \n",
+       "3            0.14250           0.28390         0.24140              0.10520   \n",
+       "4            0.10030           0.13280         0.19800              0.10430   \n",
+       "..               ...               ...             ...                  ...   \n",
+       "564          0.11100           0.11590         0.24390              0.13890   \n",
+       "565          0.09780           0.10340         0.14400              0.09791   \n",
+       "566          0.08455           0.10230         0.09251              0.05302   \n",
+       "567          0.11780           0.27700         0.35140              0.15200   \n",
+       "568          0.05263           0.04362         0.00000              0.00000   \n",
+       "\n",
+       "     ...  radius_worst  texture_worst  perimeter_worst  area_worst  \\\n",
+       "0    ...        25.380          17.33           184.60      2019.0   \n",
+       "1    ...        24.990          23.41           158.80      1956.0   \n",
+       "2    ...        23.570          25.53           152.50      1709.0   \n",
+       "3    ...        14.910          26.50            98.87       567.7   \n",
+       "4    ...        22.540          16.67           152.20      1575.0   \n",
+       "..   ...           ...            ...              ...         ...   \n",
+       "564  ...        25.450          26.40           166.10      2027.0   \n",
+       "565  ...        23.690          38.25           155.00      1731.0   \n",
+       "566  ...        18.980          34.12           126.70      1124.0   \n",
+       "567  ...        25.740          39.42           184.60      1821.0   \n",
+       "568  ...         9.456          30.37            59.16       268.6   \n",
+       "\n",
+       "     smoothness_worst  compactness_worst  concavity_worst  \\\n",
+       "0             0.16220            0.66560           0.7119   \n",
+       "1             0.12380            0.18660           0.2416   \n",
+       "2             0.14440            0.42450           0.4504   \n",
+       "3             0.20980            0.86630           0.6869   \n",
+       "4             0.13740            0.20500           0.4000   \n",
+       "..                ...                ...              ...   \n",
+       "564           0.14100            0.21130           0.4107   \n",
+       "565           0.11660            0.19220           0.3215   \n",
+       "566           0.11390            0.30940           0.3403   \n",
+       "567           0.16500            0.86810           0.9387   \n",
+       "568           0.08996            0.06444           0.0000   \n",
+       "\n",
+       "     concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
+       "0                  0.2654          0.4601                  0.11890  \n",
+       "1                  0.1860          0.2750                  0.08902  \n",
+       "2                  0.2430          0.3613                  0.08758  \n",
+       "3                  0.2575          0.6638                  0.17300  \n",
+       "4                  0.1625          0.2364                  0.07678  \n",
+       "..                    ...             ...                      ...  \n",
+       "564                0.2216          0.2060                  0.07115  \n",
+       "565                0.1628          0.2572                  0.06637  \n",
+       "566                0.1418          0.2218                  0.07820  \n",
+       "567                0.2650          0.4087                  0.12400  \n",
+       "568                0.0000          0.2871                  0.07039  \n",
+       "\n",
+       "[569 rows x 32 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# loading data\n",
+    "\n",
+    "cancer = pd.read_csv('01_materials/notebooks/dataset/wdbc.csv')\n",
+    "cancer"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "dsi_participant",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}