diff --git a/02_activities/assignments/assignment_1.ipynb b/02_activities/assignments/assignment_1.ipynb
index e50cc66eb..0f87a5728 100644
--- a/02_activities/assignments/assignment_1.ipynb
+++ b/02_activities/assignments/assignment_1.ipynb
@@ -34,7 +34,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 126,
+   "id": "77566c38",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2.3.2\n"
+     ]
+    }
+   ],
+   "source": [
+    "#pip install numpy\n",
+    "import numpy as np\n",
+    "print(np.__version__)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
    "id": "4a3485d6-ba58-4660-a983-5680821c5719",
    "metadata": {},
    "outputs": [],
@@ -42,7 +62,7 @@
     "# Import standard libraries\n",
     "import pandas as pd\n",
     "import numpy as np\n",
-    "import random\n",
+    "import random as rnd\n",
     "import matplotlib.pyplot as plt\n",
     "import matplotlib.colors as mcolors\n",
     "from sklearn.preprocessing import StandardScaler\n",
@@ -56,11 +76,291 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 128,
    "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": 128,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
+    "# pd.set_option('display.max_rows', None)\n",
+    "\n",
     "from sklearn.datasets import load_wine\n",
     "\n",
     "# Load the Wine dataset\n",
@@ -73,7 +373,48 @@
     "wine_df['class'] = wine_data.target\n",
     "\n",
     "# Display the DataFrame\n",
-    "wine_df\n"
+    "wine_df\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "id": "7e41583e",
+   "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    int64  \n",
+      "dtypes: float64(13), int64(1)\n",
+      "memory usage: 19.6 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "#look at our data\n",
+    "wine_df.info()\n"
    ]
   },
   {
@@ -91,12 +432,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 130,
    "id": "56916892",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "178\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your answer here"
+    "# Your answer here\n",
+    "# 178\n",
+    "print(wine_df.shape[0])"
    ]
   },
   {
@@ -109,12 +460,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 131,
    "id": "df0ef103",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "14\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your answer here"
+    "# Your answer here\n",
+    "# 14\n",
+    "print(wine_df.shape[1])"
    ]
   },
   {
@@ -127,12 +488,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 132,
    "id": "47989426",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Type: int64\n",
+      "Levels: [0 1 2]\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your answer here"
+    "# Your answer here\n",
+    "# Numerical/integers - 0, 1, 2\n",
+    "\n",
+    "print(\"Type:\", wine_df['class'].dtype)\n",
+    "print(\"Levels:\", wine_df['class'].unique())\n"
    ]
   },
   {
@@ -146,12 +520,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 133,
    "id": "bd7b0910",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "13\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your answer here"
+    "# Your answer here\n",
+    "# 13\n",
+    "count_predictors = wine_df.shape[1] - 1\n",
+    "print(count_predictors)"
    ]
   },
   {
@@ -162,6 +547,45 @@
     "You can use `print()` and `describe()` to help answer these questions."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "id": "6ddf7522",
+   "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    int64  \n",
+      "dtypes: float64(13), int64(1)\n",
+      "memory usage: 19.6 KB\n",
+      "None\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(wine_df.info())\n"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "fa3832d7",
@@ -175,13 +599,40 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 135,
    "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",
+    "predictors = wine_df.iloc[:, :-1] \n",
     "\n",
     "# Standardize the predictors\n",
     "scaler = StandardScaler()\n",
@@ -204,7 +655,8 @@
    "id": "403ef0bb",
    "metadata": {},
    "source": [
-    "> Your answer here..."
+    "> Your answer here...\n",
+    "# To ensure fair contribution from all variables"
    ]
   },
   {
@@ -220,7 +672,9 @@
    "id": "fdee5a15",
    "metadata": {},
    "source": [
-    "> Your answer here..."
+    "> Your answer here...\n",
+    "# we did not include class as a predictor variable\n",
+    "# its our response variable - meaning it is the measured outcome for the experiment also called the dependent variable"
    ]
   },
   {
@@ -236,7 +690,14 @@
    "id": "f0676c21",
    "metadata": {},
    "source": [
-    "> Your answer here..."
+    "> Your answer here...\n",
+    "# Randomness is used to make fair, ubiased decisions without human input.\n",
+    "# Debbugging and testing - setting a seed allows for consistency in reproducing specific scenarios. making it easier to identify the source of issues and verify fixes - In coding\n",
+    "# consistency in experiments: allows for comparisons accross test and training datasets\n",
+    "# while the value of the seed is not important. Consistency of the value used accross datasets is important. \n",
+    "    - using the same value allows for reproducability \n",
+    "\n",
+    "Source: https://medium.com/@abhishekjainindore24/seeding-success-a-guide-to-setting-seeds-in-data-science-5d24e00c3dc7"
    ]
   },
   {
@@ -251,17 +712,328 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 136,
    "id": "72c101f2",
    "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",
+       "    </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",
+       "    </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",
+       "    </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",
+       "    </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",
+       "    </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",
+       "    </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",
+       "    </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",
+       "    </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",
+       "    </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",
+       "    </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",
+       "    </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",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>178 rows × 13 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  \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",
+       "..               ...       ...                           ...       ...  \n",
+       "173         1.142811 -1.392758                     -1.231206 -0.021952  \n",
+       "174         0.969783 -1.129518                     -1.485445  0.009893  \n",
+       "175         2.224236 -1.612125                     -1.485445  0.280575  \n",
+       "176         1.834923 -1.568252                     -1.400699  0.296498  \n",
+       "177         1.791666 -1.524378                     -1.428948 -0.595160  \n",
+       "\n",
+       "[178 rows x 13 columns]"
+      ]
+     },
+     "execution_count": 136,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# set a seed for reproducibility\n",
     "np.random.seed(123)\n",
     "\n",
+    "# Your code here ...\n",
+    "\n",
+    "wine_for_testing = predictors_standardized.copy()\n",
+    "\n",
+    "\n",
     "# split the data into a training and testing set. hint: use train_test_split !\n",
+    "wine_train, wine_test, class_train, class_test = train_test_split( \n",
+    "    wine_for_testing, wine_df['class'], train_size = 0.75, shuffle = True, stratify = wine_df['class']\n",
+    ")\n",
     "\n",
-    "# Your code here ..."
+    "wine_for_testing\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "id": "9927c4aa",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 133 entries, 78 to 66\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": [
+    "wine_train.info()"
    ]
   },
   {
@@ -284,12 +1056,1463 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "08818c64",
+   "execution_count": 138,
+   "id": "64f24995",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-19 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\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-19 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 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-19 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-19 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-19 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-19 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-19 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-19 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-19 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-19 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-19 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 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-19 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-19 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 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-19 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 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-19 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-19 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-19 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-19 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-19 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-19 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-19 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-19 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-19 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-19 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-19 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-19 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-19 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 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: 0.5em;\n",
+       "  text-align: center;\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-19 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-19 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-19 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-19 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    font-family: monospace;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value pre {\n",
+       "    color:rgb(255, 94, 0) !important;\n",
+       "    background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-19\" 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-31\" type=\"checkbox\" checked><label for=\"sk-estimator-id-31\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>KNeighborsClassifier</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></div></label><div class=\"sk-toggleable__content \" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_neighbors',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_neighbors&nbsp;</td>\n",
+       "            <td class=\"value\">5</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('weights',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">weights&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;uniform&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('algorithm',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">algorithm&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('leaf_size',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">leaf_size&nbsp;</td>\n",
+       "            <td class=\"value\">30</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('p',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">p&nbsp;</td>\n",
+       "            <td class=\"value\">2</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('metric',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">metric&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;minkowski&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('metric_params',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">metric_params&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "</script></body>"
+      ],
+      "text/plain": [
+       "KNeighborsClassifier()"
+      ]
+     },
+     "execution_count": 138,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Your code here...\n",
+    "#1 Initialize KNN classifier using KNeighborsClassifier()\n",
+    "knn = KNeighborsClassifier()\n",
+    "knn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "id": "e5355e13",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your code here..."
+    "#Define parameters for n_neighbors ranging from 1 to 50\n",
+    "from sklearn.model_selection import ParameterGrid\n",
+    "parameter_grid = {\n",
+    "    'n_neighbors': np.arange(1, 51)}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "id": "0947bf5c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#implement a grid search ursing GridSearchCV with 10-fold cross-validation\n",
+    "wine_tune_grid = GridSearchCV(\n",
+    "    estimator=knn, \n",
+    "    param_grid=parameter_grid,\n",
+    "    cv=10\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "id": "d798a329",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-20 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\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-20 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 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-20 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-20 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-20 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-20 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-20 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-20 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-20 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-20 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-20 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 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-20 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-20 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 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-20 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 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-20 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-20 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-20 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-20 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-20 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-20 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-20 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-20 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-20 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-20 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-20 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-20 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-20 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 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: 0.5em;\n",
+       "  text-align: center;\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-20 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-20 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-20 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-20 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    font-family: monospace;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value pre {\n",
+       "    color:rgb(255, 94, 0) !important;\n",
+       "    background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-20\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=10, estimator=KNeighborsClassifier(),\n",
+       "             param_grid={&#x27;n_neighbors&#x27;: array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,\n",
+       "       35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50])})</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-32\" type=\"checkbox\" ><label for=\"sk-estimator-id-32\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>GridSearchCV</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/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></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('estimator',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">estimator&nbsp;</td>\n",
+       "            <td class=\"value\">KNeighborsClassifier()</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('param_grid',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">param_grid&nbsp;</td>\n",
+       "            <td class=\"value\">{&#x27;n_neighbors&#x27;: array([ 1,  2..., 48, 49, 50])}</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('scoring',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">scoring&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('refit',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">refit&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('cv',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">cv&nbsp;</td>\n",
+       "            <td class=\"value\">10</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">verbose&nbsp;</td>\n",
+       "            <td class=\"value\">0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('pre_dispatch',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">pre_dispatch&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;2*n_jobs&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('error_score',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">error_score&nbsp;</td>\n",
+       "            <td class=\"value\">nan</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('return_train_score',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">return_train_score&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </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-33\" type=\"checkbox\" ><label for=\"sk-estimator-id-33\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>best_estimator_: KNeighborsClassifier</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"best_estimator___\"><pre>KNeighborsClassifier(n_neighbors=np.int64(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-34\" type=\"checkbox\" ><label for=\"sk-estimator-id-34\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>KNeighborsClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"best_estimator___\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_neighbors',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_neighbors&nbsp;</td>\n",
+       "            <td class=\"value\">np.int64(7)</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('weights',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">weights&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;uniform&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('algorithm',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">algorithm&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('leaf_size',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">leaf_size&nbsp;</td>\n",
+       "            <td class=\"value\">30</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('p',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">p&nbsp;</td>\n",
+       "            <td class=\"value\">2</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('metric',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">metric&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;minkowski&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('metric_params',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">metric_params&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "</script></body>"
+      ],
+      "text/plain": [
+       "GridSearchCV(cv=10, estimator=KNeighborsClassifier(),\n",
+       "             param_grid={'n_neighbors': array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,\n",
+       "       35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50])})"
+      ]
+     },
+     "execution_count": 141,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#fit the model\n",
+    "wine_tune_grid.fit(\n",
+    "    wine_train,\n",
+    "    class_train\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "id": "d6141021",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'n_neighbors': np.int64(7)}"
+      ]
+     },
+     "execution_count": 142,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#best value for n_neighbors\n",
+    "wine_tune_grid.best_params_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "id": "a4e47abe",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.int64(7)"
+      ]
+     },
+     "execution_count": 143,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ideal_k = wine_tune_grid.best_params_['n_neighbors']\n",
+    "ideal_k"
    ]
   },
   {
@@ -305,12 +2528,673 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "ffefa9f2",
+   "execution_count": 144,
+   "id": "a120274d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-21 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\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-21 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-21 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-21 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-21 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-21 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-21 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-21 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-21 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-21 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-21 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-21 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-21 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-21 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-21 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-21 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-21 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-21 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-21 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-21 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-21 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-21 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-21 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-21 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-21 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-21 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-21 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-21 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-21 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-21 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-21 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-21 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-21 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-21 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-21 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-21 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-21 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-21 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-21 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: 0.5em;\n",
+       "  text-align: center;\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-21 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-21 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-21 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-21 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    font-family: monospace;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value pre {\n",
+       "    color:rgb(255, 94, 0) !important;\n",
+       "    background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-21\" 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-35\" type=\"checkbox\" checked><label for=\"sk-estimator-id-35\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>KNeighborsClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_neighbors',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_neighbors&nbsp;</td>\n",
+       "            <td class=\"value\">7</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('weights',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">weights&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;uniform&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('algorithm',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">algorithm&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('leaf_size',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">leaf_size&nbsp;</td>\n",
+       "            <td class=\"value\">30</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('p',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">p&nbsp;</td>\n",
+       "            <td class=\"value\">2</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('metric',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">metric&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;minkowski&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('metric_params',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">metric_params&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "</script></body>"
+      ],
+      "text/plain": [
+       "KNeighborsClassifier(n_neighbors=7)"
+      ]
+     },
+     "execution_count": 144,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "knn = KNeighborsClassifier(n_neighbors=7)\n",
+    "x = wine_train\n",
+    "y = class_train\n",
+    "\n",
+    "knn. fit(x, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "id": "0807a640",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your code here..."
+    "#predict the wine_test  \n",
+    "\n",
+    "wine_test['predicted'] = knn.predict(wine_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "id": "3deef5ea",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " The accurancy score is 0.9333333333333333\n"
+     ]
+    }
+   ],
+   "source": [
+    "# compare actual vs predicted\n",
+    "y_test = class_test\n",
+    "y_pred = wine_test['predicted']\n",
+    "wine_score = accuracy_score(y_test, y_pred)\n",
+    "print(f' The accurancy score is {wine_score}')"
    ]
   },
   {
@@ -365,7 +3249,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.10.4",
+   "display_name": "base",
    "language": "python",
    "name": "python3"
   },
@@ -379,12 +3263,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.19"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "497a84dc8fec8cf8d24e7e87b6d954c9a18a327edc66feb9b9ea7e9e72cc5c7e"
-   }
+   "version": "3.13.5"
   }
  },
  "nbformat": 4,
diff --git a/02_activities/assignments/assignment_2.ipynb b/02_activities/assignments/assignment_2.ipynb
index f929d4e99..f6fca919d 100644
--- a/02_activities/assignments/assignment_2.ipynb
+++ b/02_activities/assignments/assignment_2.ipynb
@@ -34,10 +34,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 9,
    "id": "4a3485d6-ba58-4660-a983-5680821c5719",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'seaborn'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mModuleNotFoundError\u001b[39m                       Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[9]\u001b[39m\u001b[32m, line 4\u001b[39m\n\u001b[32m      2\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpandas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpd\u001b[39;00m\n\u001b[32m      3\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnumpy\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnp\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m4\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mseaborn\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01msns\u001b[39;00m\n\u001b[32m      5\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mmatplotlib\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mpyplot\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mplt\u001b[39;00m\n\u001b[32m      6\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01msklearn\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mmodel_selection\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m train_test_split\n",
+      "\u001b[31mModuleNotFoundError\u001b[39m: No module named 'seaborn'"
+     ]
+    }
+   ],
    "source": [
     "# Import standard libraries\n",
     "import pandas as pd\n",
@@ -50,10 +62,165 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
+   "id": "93bc714a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting seaborn\n",
+      "  Downloading seaborn-0.13.2-py3-none-any.whl.metadata (5.4 kB)\n",
+      "Requirement already satisfied: numpy!=1.24.0,>=1.20 in c:\\users\\karma\\miniconda3\\lib\\site-packages (from seaborn) (2.3.2)\n",
+      "Requirement already satisfied: pandas>=1.2 in c:\\users\\karma\\miniconda3\\lib\\site-packages (from seaborn) (2.3.2)\n",
+      "Requirement already satisfied: matplotlib!=3.6.1,>=3.4 in c:\\users\\karma\\miniconda3\\lib\\site-packages (from seaborn) (3.10.6)\n",
+      "Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\karma\\miniconda3\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.3.3)\n",
+      "Requirement already satisfied: cycler>=0.10 in c:\\users\\karma\\miniconda3\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (0.12.1)\n",
+      "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\karma\\miniconda3\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (4.59.2)\n",
+      "Requirement already satisfied: kiwisolver>=1.3.1 in c:\\users\\karma\\miniconda3\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.4.9)\n",
+      "Requirement already satisfied: packaging>=20.0 in c:\\users\\karma\\miniconda3\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (25.0)\n",
+      "Requirement already satisfied: pillow>=8 in c:\\users\\karma\\miniconda3\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (11.3.0)\n",
+      "Requirement already satisfied: pyparsing>=2.3.1 in c:\\users\\karma\\miniconda3\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (3.2.3)\n",
+      "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\karma\\miniconda3\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (2.9.0.post0)\n",
+      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\karma\\miniconda3\\lib\\site-packages (from pandas>=1.2->seaborn) (2025.2)\n",
+      "Requirement already satisfied: tzdata>=2022.7 in c:\\users\\karma\\miniconda3\\lib\\site-packages (from pandas>=1.2->seaborn) (2025.2)\n",
+      "Requirement already satisfied: six>=1.5 in c:\\users\\karma\\miniconda3\\lib\\site-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn) (1.17.0)\n",
+      "Downloading seaborn-0.13.2-py3-none-any.whl (294 kB)\n",
+      "Installing collected packages: seaborn\n",
+      "Successfully installed seaborn-0.13.2\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "pip install seaborn\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
    "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>mpg</th>\n",
+       "      <th>cylinders</th>\n",
+       "      <th>displacement</th>\n",
+       "      <th>horsepower</th>\n",
+       "      <th>weight</th>\n",
+       "      <th>acceleration</th>\n",
+       "      <th>model_year</th>\n",
+       "      <th>origin</th>\n",
+       "      <th>name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>18.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>307.0</td>\n",
+       "      <td>130.0</td>\n",
+       "      <td>3504</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>70</td>\n",
+       "      <td>usa</td>\n",
+       "      <td>chevrolet chevelle malibu</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>15.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>350.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>3693</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>70</td>\n",
+       "      <td>usa</td>\n",
+       "      <td>buick skylark 320</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>18.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>318.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>3436</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>70</td>\n",
+       "      <td>usa</td>\n",
+       "      <td>plymouth satellite</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>16.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>304.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>3433</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>70</td>\n",
+       "      <td>usa</td>\n",
+       "      <td>amc rebel sst</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>17.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>302.0</td>\n",
+       "      <td>140.0</td>\n",
+       "      <td>3449</td>\n",
+       "      <td>10.5</td>\n",
+       "      <td>70</td>\n",
+       "      <td>usa</td>\n",
+       "      <td>ford torino</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    mpg  cylinders  displacement  horsepower  weight  acceleration  \\\n",
+       "0  18.0          8         307.0       130.0    3504          12.0   \n",
+       "1  15.0          8         350.0       165.0    3693          11.5   \n",
+       "2  18.0          8         318.0       150.0    3436          11.0   \n",
+       "3  16.0          8         304.0       150.0    3433          12.0   \n",
+       "4  17.0          8         302.0       140.0    3449          10.5   \n",
+       "\n",
+       "   model_year origin                       name  \n",
+       "0          70    usa  chevrolet chevelle malibu  \n",
+       "1          70    usa          buick skylark 320  \n",
+       "2          70    usa         plymouth satellite  \n",
+       "3          70    usa              amc rebel sst  \n",
+       "4          70    usa                ford torino  "
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "import seaborn as sns\n",
     "\n",
@@ -82,12 +249,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "id": "5d79f1cf",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "392\n",
+      "9\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your answer here..."
+    "# Your answer here...\n",
+    "print(mpg_data.shape[0]) # Number of rows is 392\n",
+    "print(mpg_data.shape[1]) # Number of columns is 9"
    ]
   },
   {
@@ -100,12 +278,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "id": "ac306190",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "type: float64\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your answer here..."
+    "# Your answer here...\n",
+    "print(\"type:\", mpg_data['mpg'].dtype) # <class 'pandas.core.frame.DataFrame'>"
    ]
   },
   {
@@ -116,6 +303,14 @@
     "Your explanation... \n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "58e557f2",
+   "metadata": {},
+   "source": [
+    "#float 64 type is a continuous numeric used for storing real numbers with a decimal point to allow fractional values and thus a wider representation rage. It can presenting both whole and fractional numbers, this continuous aspect makes it different from integers and categorical type of variables."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "be0119ad",
@@ -126,12 +321,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 39,
    "id": "9f034a5d",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      mpg  horsepower  weight  displacement  cylinders  model_year  \\\n",
+      "116  16.0       230.0    4278         400.0          8          73   \n",
+      "95   12.0       225.0    4951         455.0          8          73   \n",
+      "8    14.0       225.0    4425         455.0          8          70   \n",
+      "13   14.0       225.0    3086         455.0          8          70   \n",
+      "6    14.0       220.0    4354         454.0          8          70   \n",
+      "\n",
+      "     acceleration  \n",
+      "116           9.5  \n",
+      "95           11.0  \n",
+      "8            10.0  \n",
+      "13           10.0  \n",
+      "6             9.0  \n"
+     ]
+    }
+   ],
    "source": [
-    "# Your answer here... "
+    "# Your answer here... \n",
+    "selected_columns = mpg_data[['mpg', 'horsepower', 'weight', 'displacement', 'cylinders', 'model_year', 'acceleration']]\n",
+    "selected_columns = selected_columns.sort_values(by='horsepower', ascending=False)\n",
+    "print(selected_columns.head(5))\n",
+    "\n",
+    "\n"
    ]
   },
   {
@@ -144,12 +364,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 33,
    "id": "1b91233e",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "mpg             float64\n",
+      "horsepower      float64\n",
+      "weight            int64\n",
+      "displacement    float64\n",
+      "cylinders         int64\n",
+      "origin           object\n",
+      "model_year        int64\n",
+      "acceleration    float64\n",
+      "name             object\n",
+      "dtype: object\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your answer here..."
+    "# Your answer here...\n",
+    "print(selected_columns.dtypes)\n",
+    "# 6 Variables total - objects are irrelevant for regression analysis of mpg"
    ]
   },
   {
@@ -173,10 +412,71 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 58,
    "id": "732784d8",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Exclude the 'mpg' (target variable) and non-numeric columns from the feature names\n",
     "feature_names = mpg_data.select_dtypes(include=[float, int]).columns.difference(['mpg'])\n",
@@ -225,7 +525,16 @@
    "id": "f67e57ab",
    "metadata": {},
    "source": [
-    "> Your answer here..."
+    "> Your answer here...\n",
+    "# POSITIVE CORRELATION : \n",
+    "    accelaration and MPG \n",
+    "    model year and MPG\n",
+    "\n",
+    "# NEGATIVE CORRELATION:\n",
+    "    cylinder vs mpg\n",
+    "    displacement vs mpg\n",
+    "    horsepower vs mpg\n",
+    "    weight vs mpg"
    ]
   },
   {
@@ -241,7 +550,9 @@
    "id": "843f9eef",
    "metadata": {},
    "source": [
-    "> Your answer here..."
+    "> Your answer here...\n",
+    "## Line of Best fit \n",
+    "    fit by miniimising the average squared vertical distance between the line and osbserved data points - a process equivalent to minimizing the Root Mean Saured Error (RMSE)"
    ]
   },
   {
@@ -257,7 +568,9 @@
    "id": "2ea782fc",
    "metadata": {},
    "source": [
-    "> Your answer here..."
+    "> Your answer here...\n",
+    "# All data points do not fall perfectly along the plotted line\n",
+    "    the relationship betwween the variables is relative"
    ]
   },
   {
@@ -274,12 +587,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 78,
    "id": "399225f4",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your answer here..."
+    "\n",
+    "# Split the dataset into training and testing sets (75% train, 25% test)\n",
+    "X_train, X_test, y_train, y_test = train_test_split(mpg_data.drop(columns=['mpg']), mpg_data['mpg'], test_size=0.25, random_state=42)"
    ]
   },
   {
@@ -292,15 +607,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 79,
    "id": "ac1e1117",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      predictor     slope  intercept\n",
+      "0     cylinders -0.160143 -15.047371\n",
+      "1  displacement  0.000373 -15.047371\n",
+      "2    horsepower -0.001899 -15.047371\n",
+      "3        weight -0.006457 -15.047371\n",
+      "4  acceleration  0.057588 -15.047371\n",
+      "5    model_year  0.762270 -15.047371\n"
+     ]
+    }
+   ],
    "source": [
     "# Your code here ...\n",
+    "# Initialize and train the linear regression model\n",
+    "lm = LinearRegression()    \n",
     "\n",
-    "numeric_predictors = 🤷‍♂️\n",
+    "##numeric_predictors = 🤷‍♂️\n",
+    "numeric_predictors = X_train.select_dtypes(include=[float, int])\n",
     "\n",
+    "lm.fit(numeric_predictors, y_train)\n",
     "\n",
     "# Create a DataFrame containing the slope (coefficients) and intercept\n",
     "coefficients_df = pd.DataFrame({\n",
@@ -330,12 +663,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 80,
    "id": "ffefa9f2",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean Squared Error (MSE): 10.147250665609228\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your code here ..."
+    "# Your code here ...\n",
+    "predictions = lm.predict(X_test.select_dtypes(include=[float, int]))\n",
+    "\n",
+    "# Calculate the Mean Squared Error (MSE) between actual and predicted values\n",
+    "mse = mean_squared_error(y_test, predictions)   \n",
+    "\n",
+    "print(f\"Mean Squared Error (MSE): {mse}\")\n"
    ]
   },
   {
@@ -375,10 +722,10 @@
     "    * 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",
+    "- [y] Created a branch with the correct naming convention.\n",
+    "- [y] Ensured that the repository is public.\n",
+    "- [y] Reviewed the PR description guidelines and adhered to them.\n",
+    "- [y] 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-7-help`. Our Technical Facilitators and Learning Support staff are here to help you navigate any challenges.\n"
    ]
@@ -386,7 +733,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "base",
    "language": "python",
    "name": "python3"
   },
@@ -400,12 +747,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.3"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "497a84dc8fec8cf8d24e7e87b6d954c9a18a327edc66feb9b9ea7e9e72cc5c7e"
-   }
+   "version": "3.13.5"
   }
  },
  "nbformat": 4,