diff --git a/01_materials/notebooks/Classification-1.ipynb b/01_materials/notebooks/Classification-1.ipynb
index 7b6959a7a..5b798239b 100644
--- a/01_materials/notebooks/Classification-1.ipynb
+++ b/01_materials/notebooks/Classification-1.ipynb
@@ -2326,7 +2326,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "base",
+   "display_name": "dsi_participant",
    "language": "python",
    "name": "python3"
   },
@@ -2340,7 +2340,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.14"
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,
diff --git a/02_activities/assignments/assignment_1.ipynb b/02_activities/assignments/assignment_1.ipynb
index e50cc66eb..8a4221ed9 100644
--- a/02_activities/assignments/assignment_1.ipynb
+++ b/02_activities/assignments/assignment_1.ipynb
@@ -34,7 +34,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 91,
    "id": "4a3485d6-ba58-4660-a983-5680821c5719",
    "metadata": {},
    "outputs": [],
@@ -56,10 +56,288 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 92,
    "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": 92,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "from sklearn.datasets import load_wine\n",
     "\n",
@@ -76,6 +354,154 @@
     "wine_df\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "id": "bcc00857",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Keys: dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names'])\n",
+      "\n",
+      "Description: .. _wine_dataset:\n",
+      "\n",
+      "Wine recognition dataset\n",
+      "------------------------\n",
+      "\n",
+      "**Data Set Characteristics:**\n",
+      "\n",
+      ":Number of Instances: 178\n",
+      ":Number of Attributes: 13 numeric, predictive attributes and the class\n",
+      ":Attribute Information:\n",
+      "    - Alcohol\n",
+      "    - Malic acid\n",
+      "    - Ash\n",
+      "    - Alcalinity of ash\n",
+      "    - Magnesium\n",
+      "    - Total phenols\n",
+      "    - Flavanoids\n",
+      "    - Nonflavanoid phenols\n",
+      "    - Proanthocyanins\n",
+      "    - Color intensity\n",
+      "    - Hue\n",
+      "    - OD280/OD315 of diluted wines\n",
+      "    - Proline\n",
+      "    - class:\n",
+      "        - class_0\n",
+      "        - class_1\n",
+      "        - class_2\n",
+      "\n",
+      ":Summary Statistics:\n",
+      "\n",
+      "============================= ==== ===== ======= =====\n",
+      "                                Min   Max   Mean     SD\n",
+      "============================= ==== ===== ======= =====\n",
+      "Alcohol:                      11.0  14.8    13.0   0.8\n",
+      "Malic Acid:                   0.74  5.80    2.34  1.12\n",
+      "Ash:                          1.36  3.23    2.36  0.27\n",
+      "Alcalinity of Ash:            10.6  30.0    19.5   3.3\n",
+      "Magnesium:                    70.0 162.0    99.7  14.3\n",
+      "Total Phenols:                0.98  3.88    2.29  0.63\n",
+      "Flavanoids:                   0.34  5.08    2.03  1.00\n",
+      "Nonflavanoid Phenols:         0.13  0.66    0.36  0.12\n",
+      "Proanthocyanins:              0.41  3.58    1.59  0.57\n",
+      "Colour Intensity:              1.3  13.0     5.1   2.3\n",
+      "Hue:                          0.48  1.71    0.96  0.23\n",
+      "OD280/OD315 of diluted wines: 1.27  4.00    2.61  0.71\n",
+      "Proline:                       278  1680     746   315\n",
+      "============================= ==== ===== ======= =====\n",
+      "\n",
+      ":Missing Attribute Values: None\n",
+      ":Class Distribution: class_0 (59), class_1 (71), class_2 (48)\n",
+      ":Creator: R.A. Fisher\n",
+      ":Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n",
+      ":Date: July, 1988\n",
+      "\n",
+      "This is a copy of UCI ML Wine recognition datasets.\n",
+      "https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data\n",
+      "\n",
+      "The data is the results of a chemical analysis of wines grown in the same\n",
+      "region in Italy by three different cultivators. There are thirteen different\n",
+      "measurements taken for different constituents found in the three types of\n",
+      "wine.\n",
+      "\n",
+      "Original Owners:\n",
+      "\n",
+      "Forina, M. et al, PARVUS -\n",
+      "An Extendible Package for Data Exploration, Classification and Correlation.\n",
+      "Institute of Pharmaceutical and Food Analysis and Technologies,\n",
+      "Via Brigata Salerno, 16147 Genoa, Italy.\n",
+      "\n",
+      "Citation:\n",
+      "\n",
+      "Lichman, M. (2013). UCI Machine Learning Repository\n",
+      "[https://archive.ics.uci.edu/ml]. Irvine, CA: University of California,\n",
+      "School of Information and Computer Science.\n",
+      "\n",
+      ".. dropdown:: References\n",
+      "\n",
+      "    (1) S. Aeberhard, D. Coomans and O. de Vel,\n",
+      "    Comparison of Classifiers in High Dimensional Settings,\n",
+      "    Tech. Rep. no. 92-02, (1992), Dept. of Computer Science and Dept. of\n",
+      "    Mathematics and Statistics, James Cook University of North Queensland.\n",
+      "    (Also submitted to Technometrics).\n",
+      "\n",
+      "    The data was used with many others for comparing various\n",
+      "    classifiers. The classes are separable, though only RDA\n",
+      "    has achieved 100% correct classification.\n",
+      "    (RDA : 100%, QDA 99.4%, LDA 98.9%, 1NN 96.1% (z-transformed data))\n",
+      "    (All results using the leave-one-out technique)\n",
+      "\n",
+      "    (2) S. Aeberhard, D. Coomans and O. de Vel,\n",
+      "    \"THE CLASSIFICATION PERFORMANCE OF RDA\"\n",
+      "    Tech. Rep. no. 92-01, (1992), Dept. of Computer Science and Dept. of\n",
+      "    Mathematics and Statistics, James Cook University of North Queensland.\n",
+      "    (Also submitted to Journal of Chemometrics).\n",
+      "\n",
+      "\n",
+      "Feature Name: ['alcohol', 'malic_acid', 'ash', 'alcalinity_of_ash', 'magnesium', 'total_phenols', 'flavanoids', 'nonflavanoid_phenols', 'proanthocyanins', 'color_intensity', 'hue', 'od280/od315_of_diluted_wines', 'proline']\n",
+      "\n",
+      "Target Name: ['class_0' 'class_1' 'class_2']\n",
+      "\n",
+      "Data: [[1.423e+01 1.710e+00 2.430e+00 1.560e+01 1.270e+02 2.800e+00 3.060e+00\n",
+      "  2.800e-01 2.290e+00 5.640e+00 1.040e+00 3.920e+00 1.065e+03]\n",
+      " [1.320e+01 1.780e+00 2.140e+00 1.120e+01 1.000e+02 2.650e+00 2.760e+00\n",
+      "  2.600e-01 1.280e+00 4.380e+00 1.050e+00 3.400e+00 1.050e+03]\n",
+      " [1.316e+01 2.360e+00 2.670e+00 1.860e+01 1.010e+02 2.800e+00 3.240e+00\n",
+      "  3.000e-01 2.810e+00 5.680e+00 1.030e+00 3.170e+00 1.185e+03]\n",
+      " [1.437e+01 1.950e+00 2.500e+00 1.680e+01 1.130e+02 3.850e+00 3.490e+00\n",
+      "  2.400e-01 2.180e+00 7.800e+00 8.600e-01 3.450e+00 1.480e+03]\n",
+      " [1.324e+01 2.590e+00 2.870e+00 2.100e+01 1.180e+02 2.800e+00 2.690e+00\n",
+      "  3.900e-01 1.820e+00 4.320e+00 1.040e+00 2.930e+00 7.350e+02]]\n",
+      "\n",
+      "Target: [0 0 0 0 0]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Display the dataset keys\n",
+    "print(f\"\\nKeys: {wine_data.keys()}\")\n",
+    "\n",
+    "#Display the dataset description\n",
+    "print(f\"\\nDescription: {wine_data.DESCR}\")\n",
+    "\n",
+    "#Display the feature names\n",
+    "print(f\"\\nFeature Name: {wine_data.feature_names}\")\n",
+    "\n",
+    "#Display the target names\n",
+    "print(f\"\\nTarget Name: {wine_data.target_names}\")\n",
+    "\n",
+    "#Display the first 5 rows of data\n",
+    "print(f\"\\nData: {wine_data.data[:5]}\")\n",
+    "\n",
+    "#display the first 5 target values\n",
+    "print(f\"\\nTarget: {wine_data.target[:5]}\")\n"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "721b2b17",
@@ -91,12 +517,49 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 94,
    "id": "56916892",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 178 entries, 0 to 177\n",
+      "Data columns (total 14 columns):\n",
+      " #   Column                        Non-Null Count  Dtype  \n",
+      "---  ------                        --------------  -----  \n",
+      " 0   alcohol                       178 non-null    float64\n",
+      " 1   malic_acid                    178 non-null    float64\n",
+      " 2   ash                           178 non-null    float64\n",
+      " 3   alcalinity_of_ash             178 non-null    float64\n",
+      " 4   magnesium                     178 non-null    float64\n",
+      " 5   total_phenols                 178 non-null    float64\n",
+      " 6   flavanoids                    178 non-null    float64\n",
+      " 7   nonflavanoid_phenols          178 non-null    float64\n",
+      " 8   proanthocyanins               178 non-null    float64\n",
+      " 9   color_intensity               178 non-null    float64\n",
+      " 10  hue                           178 non-null    float64\n",
+      " 11  od280/od315_of_diluted_wines  178 non-null    float64\n",
+      " 12  proline                       178 non-null    float64\n",
+      " 13  class                         178 non-null    int64  \n",
+      "dtypes: float64(13), int64(1)\n",
+      "memory usage: 19.6 KB\n",
+      "Info: None\n",
+      "\n",
+      "Number of rows: 178\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your answer here"
+    "# Your answer here: 178\n",
+    "\n",
+    "# Inspect the wine_df DataFrame using the .info() method\n",
+    "print(f\"Info: {wine_df.info()}\")  #There should be 178 entries and 14 columns\n",
+    "\n",
+    "#Using the .shape attribute to find the number of rows\n",
+    "print(f\"\\nNumber of rows: {wine_df.shape[0]}\")  #There should be 178 entries\n"
    ]
   },
   {
@@ -109,12 +572,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 95,
    "id": "df0ef103",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Number of columns: 14\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your answer here"
+    "# Your answer here: 14\n",
+    "\n",
+    "#Using the .shape attribute to find the number of columns \n",
+    "print(f\"\\nNumber of columns: {wine_df.shape[1]}\") \n"
    ]
   },
   {
@@ -127,12 +602,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 96,
    "id": "47989426",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Variable type: int64\n",
+      "Unique value: 3\n",
+      "Value(s): [0 1 2]\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your answer here"
+    "# Your answer here: Integer with 3 unique values/levels (0 1 2)\n",
+    "\n",
+    "print(f\"\\nVariable type: {wine_df['class'].dtype}\")     #The type should be integer (int64)\n",
+    "print(f\"Unique value: {wine_df['class'].nunique()}\")    #There should be 3 unique values\n",
+    "print(f\"Value(s): {wine_df['class'].unique()}\")         #The values should be 0, 1, and 2\n",
+    "\n"
    ]
   },
   {
@@ -146,12 +637,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 97,
    "id": "bd7b0910",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Number of predictor variables: 13\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your answer here"
+    "# Your answer here: 13\n",
+    "\n",
+    "#Number of predictor variables (features)\n",
+    "print(f\"\\nNumber of predictor variables: {wine_df.shape[1] - 1}\")   #Number of columns - target\n"
    ]
   },
   {
@@ -175,10 +678,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 98,
    "id": "cc899b59",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    alcohol  malic_acid       ash  alcalinity_of_ash  magnesium  \\\n",
+      "0  1.518613   -0.562250  0.232053          -1.169593   1.913905   \n",
+      "1  0.246290   -0.499413 -0.827996          -2.490847   0.018145   \n",
+      "2  0.196879    0.021231  1.109334          -0.268738   0.088358   \n",
+      "3  1.691550   -0.346811  0.487926          -0.809251   0.930918   \n",
+      "4  0.295700    0.227694  1.840403           0.451946   1.281985   \n",
+      "\n",
+      "   total_phenols  flavanoids  nonflavanoid_phenols  proanthocyanins  \\\n",
+      "0       0.808997    1.034819             -0.659563         1.224884   \n",
+      "1       0.568648    0.733629             -0.820719        -0.544721   \n",
+      "2       0.808997    1.215533             -0.498407         2.135968   \n",
+      "3       2.491446    1.466525             -0.981875         1.032155   \n",
+      "4       0.808997    0.663351              0.226796         0.401404   \n",
+      "\n",
+      "   color_intensity       hue  od280/od315_of_diluted_wines   proline  \n",
+      "0         0.251717  0.362177                      1.847920  1.013009  \n",
+      "1        -0.293321  0.406051                      1.113449  0.965242  \n",
+      "2         0.269020  0.318304                      0.788587  1.395148  \n",
+      "3         1.186068 -0.427544                      1.184071  2.334574  \n",
+      "4        -0.319276  0.362177                      0.449601 -0.037874  \n"
+     ]
+    }
+   ],
    "source": [
     "# Select predictors (excluding the last column)\n",
     "predictors = wine_df.iloc[:, :-1]\n",
@@ -204,7 +734,7 @@
    "id": "403ef0bb",
    "metadata": {},
    "source": [
-    "> Your answer here..."
+    ">We standardize so we can avoid having big numbers will completely overshadow the small ones. This step ensures all 13 predictor variables (features) have an equal chance to influence classification."
    ]
   },
   {
@@ -220,7 +750,7 @@
    "id": "fdee5a15",
    "metadata": {},
    "source": [
-    "> Your answer here..."
+    "> We standardize predictor variables only and exclude the response variable 'Class' because it’s just the label/category (Class: 0, 1, or 2) we’re trying to predict."
    ]
   },
   {
@@ -236,7 +766,7 @@
    "id": "f0676c21",
    "metadata": {},
    "source": [
-    "> Your answer here..."
+    "> Setting a seed makes random processes repeatable, which is useful for reproducibility, debugging and comparing models since they all train/test on the same results."
    ]
   },
   {
@@ -251,17 +781,122 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 99,
    "id": "72c101f2",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "X_train shape: (133, 13)\n",
+      "X_test shape: (45, 13)\n",
+      "y_train shape: (133,)\n",
+      "y_test shape: (45,)\n",
+      "X_train (first 5 rows):\n",
+      "     alcohol  malic_acid       ash  alcalinity_of_ash  magnesium  \\\n",
+      "78 -0.828391   -1.208567 -1.522511          -1.409821   2.545825   \n",
+      "0   1.518613   -0.562250  0.232053          -1.169593   1.913905   \n",
+      "15  0.777454   -0.472483  1.218995          -0.689137   0.860705   \n",
+      "13  2.160950   -0.544297  0.085839          -2.430790  -0.613775   \n",
+      "14  1.703902   -0.418624  0.049285          -2.250619   0.158572   \n",
+      "\n",
+      "    total_phenols  flavanoids  nonflavanoid_phenols  proanthocyanins  \\\n",
+      "78      -0.633101   -0.179981             -0.095517         2.048364   \n",
+      "0        0.808997    1.034819             -0.659563         1.224884   \n",
+      "15       0.889114    0.884224             -0.498407        -0.229346   \n",
+      "13       1.289697    1.667318              0.549108         2.135968   \n",
+      "14       1.610163    1.617120             -0.578985         2.398780   \n",
+      "\n",
+      "    color_intensity       hue  od280/od315_of_diluted_wines   proline  \n",
+      "78        -0.717240  0.449924                     -0.426113  0.009893  \n",
+      "0          0.251717  0.362177                      1.847920  1.013009  \n",
+      "15         0.969783  1.415139                      0.378979  1.793210  \n",
+      "13         0.147900  1.283518                      0.167113  1.283691  \n",
+      "14         1.056297  1.064151                      0.548472  2.547935   \n",
+      "\n",
+      "y_train (first 5 rows):\n",
+      "78    1\n",
+      "0     0\n",
+      "15    0\n",
+      "13    0\n",
+      "14    0\n",
+      "Name: class, dtype: int64 \n",
+      "\n",
+      "X_test (first 5 rows):\n",
+      "      alcohol  malic_acid       ash  alcalinity_of_ash  magnesium  \\\n",
+      "102 -0.816038    0.102021  0.341713           0.451946  -0.122282   \n",
+      "84  -1.433671   -1.298334  0.780354          -0.448909  -0.403135   \n",
+      "96  -1.470729   -0.194208  1.365208           0.602088   2.405399   \n",
+      "65  -0.778980   -1.011081  0.707247          -0.418881  -0.122282   \n",
+      "79  -0.371343    1.376703  0.122392           1.052516   0.088358   \n",
+      "\n",
+      "     total_phenols  flavanoids  nonflavanoid_phenols  proanthocyanins  \\\n",
+      "102       0.424438    0.081051             -0.176095        -0.492158   \n",
+      "84       -0.152402    0.181447             -1.143031         1.330009   \n",
+      "96       -1.113800   -1.043392             -1.787656        -0.054137   \n",
+      "65        0.200111    0.623193              0.065639         0.856946   \n",
+      "79        0.857067    0.522796              0.549108         0.629175   \n",
+      "\n",
+      "     color_intensity       hue  od280/od315_of_diluted_wines   proline  \n",
+      "102        -0.976782 -0.690784                      1.085200 -0.983669  \n",
+      "84         -0.868639 -0.734657                      0.661468 -0.722540  \n",
+      "96         -1.106553 -0.032683                     -0.496736 -0.388168  \n",
+      "65         -0.198156  1.020278                     -0.440238 -0.219390  \n",
+      "79         -1.076273  1.020278                      0.732090 -0.904056   \n",
+      "\n",
+      "y_test (first 5 rows):\n",
+      "102    1\n",
+      "84     1\n",
+      "96     1\n",
+      "65     1\n",
+      "79     1\n",
+      "Name: class, dtype: int64\n"
+     ]
+    }
+   ],
    "source": [
     "# set a seed for reproducibility\n",
     "np.random.seed(123)\n",
     "\n",
-    "# split the data into a training and testing set. hint: use train_test_split !\n",
+    "# # split the data into a training and testing set. hint: use train_test_split !\n",
+    "\n",
+    "# Features (already standardized)\n",
+    "X = predictors_standardized\n",
+    "\n",
+    "# Target/Response variable\n",
+    "y = wine_df[\"class\"]\n",
+    "\n",
+    "# Split into train/test (75/25), preserving class balance\n",
+    "X_train, X_test, y_train, y_test = train_test_split(\n",
+    "    X, y,\n",
+    "    train_size=0.75,\n",
+    "    stratify=y\n",
+    ")\n",
+    "\n",
+    "# Display the shapes of the resulting datasets\n",
+    "print(\"X_train shape:\", X_train.shape)\n",
+    "print(\"X_test shape:\", X_test.shape)\n",
+    "print(\"y_train shape:\", y_train.shape)\n",
+    "print(\"y_test shape:\", y_test.shape)\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Display training predictors\n",
+    "print(\"X_train (first 5 rows):\")\n",
+    "print(X_train.head(), \"\\n\")\n",
+    "\n",
+    "# Display training target\n",
+    "print(\"y_train (first 5 rows):\")\n",
+    "print(y_train.head(), \"\\n\")\n",
     "\n",
-    "# Your code here ..."
+    "# Display testing predictors\n",
+    "print(\"X_test (first 5 rows):\")\n",
+    "print(X_test.head(), \"\\n\")\n",
+    "\n",
+    "# Display testing target\n",
+    "print(\"y_test (first 5 rows):\")\n",
+    "print(y_test.head())\n"
    ]
   },
   {
@@ -284,12 +919,42 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 100,
    "id": "08818c64",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Optimal k value: 7\n",
+      "Accuracy: 0.9774725274725276\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your code here..."
+    "# Step 1. Initialize the KNN model\n",
+    "knn = KNeighborsClassifier()\n",
+    "\n",
+    "# Step 2. Define a parameter grid for `n_neighbors` ranging from 1 to 50.\n",
+    "parameter_grid = {\n",
+    "    \"n_neighbors\" : range(1,51)      \n",
+    "}   \n",
+    "\n",
+    "# Step 3. Implement a grid search using `GridSearchCV` with 10-fold cross-validation to find the optimal number of neighbors.\n",
+    "grid_search = GridSearchCV(\n",
+    "    estimator=knn,\n",
+    "    param_grid=parameter_grid,\n",
+    "    cv=10,\n",
+    "    scoring='accuracy'\n",
+    ")   \n",
+    "\n",
+    "# Step 4. After fitting the model on the training data, identify and return the best value for `n_neighbors` based on the grid search results.\n",
+    "grid_search.fit(X_train, y_train)\n",
+    "\n",
+    "# Step 5. Evaluate the model's performance on the test set using the best found hyperparameter and report the accuracy.\n",
+    "print(\"Optimal k value:\", grid_search.best_params_[\"n_neighbors\"])\n",
+    "print(\"Accuracy:\", grid_search.best_score_)"
    ]
   },
   {
@@ -308,9 +973,193 @@
    "execution_count": null,
    "id": "ffefa9f2",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test set accuracy: 0.9333333333333333\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Display the accuracy of each fold\n",
+    "optimal_k = grid_search.best_params_[\"n_neighbors\"]\n",
+    "\n",
+    "# Initialize KNN with the optimal k\n",
+    "knn_optimal = KNeighborsClassifier(n_neighbors=optimal_k)\n",
+    "knn_optimal.fit(X_train, y_train)\n",
+    "y_pred = knn_optimal.predict(X_test)\n",
+    "\n",
+    "print(\"Test set accuracy:\", accuracy_score(y_test, y_pred))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "id": "cc6c5a90",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Confusion Matrix:\n",
+      "\n",
+      "Predicted   0   1   2\n",
+      "Actual               \n",
+      "0          15   0   0\n",
+      "1           2  15   1\n",
+      "2           0   0  12\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Confusion Matrix using pandas crosstab\n",
+    "conf_matrix = pd.crosstab(\n",
+    "    y_test, y_pred,\n",
+    "    rownames=['Actual'],\n",
+    "    colnames=['Predicted'],\n",
+    "    dropna=False\n",
+    ")\n",
+    "print(\"\\nConfusion Matrix:\\n\")\n",
+    "print(conf_matrix)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "id": "20511d0b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "         Precision  Recall\n",
+      "class_0      0.882   1.000\n",
+      "class_1      1.000   0.833\n",
+      "class_2      0.923   1.000\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your code here..."
+    "# Precision, Recall, F1 for each class\n",
+    "precision = precision_score(y_test, y_pred, average=None)\n",
+    "recall = recall_score(y_test, y_pred, average=None)\n",
+    "\n",
+    "# Put results into a DataFrame for readability\n",
+    "metrics_df = pd.DataFrame({\n",
+    "    \"Precision\": precision.round(3),\n",
+    "    \"Recall\": recall.round(3)\n",
+    "}, index=wine_data.target_names)\n",
+    "\n",
+    "print(metrics_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "id": "f0bd353a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Assignment 1: Wine Dataset KNN Classification\n",
+      "\n",
+      "Question 1: Dataset Summary\n",
+      "Total observations (rows): 178\n",
+      "Total variables (columns): 14\n",
+      "Total features: 13\n",
+      "Target classes: ['class_0', 'class_1', 'class_2']\n",
+      "Variable type: int64\n",
+      "Unique value: 3\n",
+      "Value(s): [0 1 2]\n",
+      "\n",
+      "Question 2: Standardization and data-splitting\n",
+      "Train/Test Split\n",
+      "--------------------------------------------------\n",
+      "Training set size: 133 samples\n",
+      "Test set size: 45 samples\n",
+      "\n",
+      "Question 3: Cross-Validation & Hyperparameter Tuning\n",
+      "Hyperparameter Tuning (10-fold Cross-Validation)\n",
+      "--------------------------------------------------\n",
+      "Optimal number of neighbors (k): 7\n",
+      "Mean CV Accuracy: 0.977\n",
+      "\n",
+      "Question 4: Model Evaluation\n",
+      "Performance on Test Set\n",
+      "--------------------------------------------------\n",
+      "Test Accuracy: 0.933\n",
+      "\n",
+      "Confusion Matrix\n",
+      "--------------------------------------------------\n",
+      "Predicted   0   1   2\n",
+      "Actual               \n",
+      "0          15   0   0\n",
+      "1           2  15   1\n",
+      "2           0   0  12 \n",
+      "\n",
+      "Per-Class Metrics\n",
+      "--------------------------------------------------\n",
+      "         Precision  Recall\n",
+      "class_0      0.882   1.000\n",
+      "class_1      1.000   0.833\n",
+      "class_2      0.923   1.000\n",
+      "==================================================\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Final report\n",
+    "print(\"Assignment 1: Wine Dataset KNN Classification\\n\")\n",
+    "\n",
+    "# Dataset summary\n",
+    "print(\"Question 1: Dataset Summary\")\n",
+    "print(f\"Total observations (rows): {wine_df.shape[0]}\")\n",
+    "print(f\"Total variables (columns): {wine_df.shape[1]}\")\n",
+    "print(f\"Total features: {wine_df.shape[1] - 1}\")\n",
+    "print(f\"Target classes: {list(wine_data.target_names)}\")\n",
+    "print(f\"Variable type: {wine_df['class'].dtype}\")     \n",
+    "print(f\"Unique value: {wine_df['class'].nunique()}\")    \n",
+    "print(f\"Value(s): {wine_df['class'].unique()}\\n\")         \n",
+    "\n",
+    "print(\"Question 2: Standardization and data-splitting\")\n",
+    "print(\"Train/Test Split\")\n",
+    "print(\"-\"*50)\n",
+    "print(f\"Training set size: {X_train.shape[0]} samples\")\n",
+    "print(f\"Test set size: {X_test.shape[0]} samples\\n\")\n",
+    "\n",
+    "print(\"Question 3: Cross-Validation & Hyperparameter Tuning\")\n",
+    "# Hyperparameter tuning results\n",
+    "optimal_k = grid_search.best_params_[\"n_neighbors\"]\n",
+    "print(\"Hyperparameter Tuning (10-fold Cross-Validation)\")\n",
+    "print(\"-\"*50)\n",
+    "print(f\"Optimal number of neighbors (k): {optimal_k}\")\n",
+    "print(f\"Mean CV Accuracy: {grid_search.best_score_:.3f}\\n\")\n",
+    "\n",
+    "\n",
+    "# Test performance\n",
+    "print(\"Question 4: Model Evaluation\")\n",
+    "test_accuracy = accuracy_score(y_test, y_pred)\n",
+    "print(\"Performance on Test Set\")\n",
+    "print(\"-\"*50)\n",
+    "print(f\"Test Accuracy: {test_accuracy:.3f}\\n\")\n",
+    "\n",
+    "# Confusion Matrix\n",
+    "print(\"Confusion Matrix\")\n",
+    "print(\"-\"*50)\n",
+    "print(conf_matrix, \"\\n\")\n",
+    "\n",
+    "# Precision & Recall\n",
+    "print(\"Per-Class Metrics\")\n",
+    "print(\"-\"*50)\n",
+    "print(metrics_df)\n",
+    "print(\"=\"*50)\n"
    ]
   },
   {
@@ -354,10 +1203,10 @@
     "    * 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",
+    "- [X] Created a branch with the correct naming convention.\n",
+    "- [X] Ensured that the repository is public.\n",
+    "- [X] Reviewed the PR description guidelines and adhered to them.\n",
+    "- [X] 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"
    ]
@@ -365,7 +1214,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.10.4",
+   "display_name": "dsi_participant",
    "language": "python",
    "name": "python3"
   },
@@ -379,12 +1228,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.19"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "497a84dc8fec8cf8d24e7e87b6d954c9a18a327edc66feb9b9ea7e9e72cc5c7e"
-   }
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,
diff --git a/04_this_cohort/live_code/live_code_02_27_2025.ipynb b/04_this_cohort/live_code/live_code_02_27_2025.ipynb
index f5cc4479b..df4a6d64f 100644
--- a/04_this_cohort/live_code/live_code_02_27_2025.ipynb
+++ b/04_this_cohort/live_code/live_code_02_27_2025.ipynb
@@ -497,6 +497,13 @@
     "cancer['diagnosis'].unique()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": 9,
@@ -511,7 +518,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
diff --git a/04_this_cohort/live_code/live_code_09-02-25.ipynb b/04_this_cohort/live_code/live_code_09-02-25.ipynb
new file mode 100644
index 000000000..9bc3cbb16
--- /dev/null
+++ b/04_this_cohort/live_code/live_code_09-02-25.ipynb
@@ -0,0 +1,3059 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "c25b5ba9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#import our libraries\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.colors as mcolors\n",
+    "from mpl_toolkits import mplot3d\n",
+    "import os\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "8ed98cfe",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>diagnosis</th>\n",
+       "      <th>radius_mean</th>\n",
+       "      <th>texture_mean</th>\n",
+       "      <th>perimeter_mean</th>\n",
+       "      <th>area_mean</th>\n",
+       "      <th>smoothness_mean</th>\n",
+       "      <th>compactness_mean</th>\n",
+       "      <th>concavity_mean</th>\n",
+       "      <th>concave points_mean</th>\n",
+       "      <th>...</th>\n",
+       "      <th>radius_worst</th>\n",
+       "      <th>texture_worst</th>\n",
+       "      <th>perimeter_worst</th>\n",
+       "      <th>area_worst</th>\n",
+       "      <th>smoothness_worst</th>\n",
+       "      <th>compactness_worst</th>\n",
+       "      <th>concavity_worst</th>\n",
+       "      <th>concave points_worst</th>\n",
+       "      <th>symmetry_worst</th>\n",
+       "      <th>fractal_dimension_worst</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>842302</td>\n",
+       "      <td>M</td>\n",
+       "      <td>17.99</td>\n",
+       "      <td>10.38</td>\n",
+       "      <td>122.80</td>\n",
+       "      <td>1001.0</td>\n",
+       "      <td>0.11840</td>\n",
+       "      <td>0.27760</td>\n",
+       "      <td>0.30010</td>\n",
+       "      <td>0.14710</td>\n",
+       "      <td>...</td>\n",
+       "      <td>25.380</td>\n",
+       "      <td>17.33</td>\n",
+       "      <td>184.60</td>\n",
+       "      <td>2019.0</td>\n",
+       "      <td>0.16220</td>\n",
+       "      <td>0.66560</td>\n",
+       "      <td>0.7119</td>\n",
+       "      <td>0.2654</td>\n",
+       "      <td>0.4601</td>\n",
+       "      <td>0.11890</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>842517</td>\n",
+       "      <td>M</td>\n",
+       "      <td>20.57</td>\n",
+       "      <td>17.77</td>\n",
+       "      <td>132.90</td>\n",
+       "      <td>1326.0</td>\n",
+       "      <td>0.08474</td>\n",
+       "      <td>0.07864</td>\n",
+       "      <td>0.08690</td>\n",
+       "      <td>0.07017</td>\n",
+       "      <td>...</td>\n",
+       "      <td>24.990</td>\n",
+       "      <td>23.41</td>\n",
+       "      <td>158.80</td>\n",
+       "      <td>1956.0</td>\n",
+       "      <td>0.12380</td>\n",
+       "      <td>0.18660</td>\n",
+       "      <td>0.2416</td>\n",
+       "      <td>0.1860</td>\n",
+       "      <td>0.2750</td>\n",
+       "      <td>0.08902</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>84300903</td>\n",
+       "      <td>M</td>\n",
+       "      <td>19.69</td>\n",
+       "      <td>21.25</td>\n",
+       "      <td>130.00</td>\n",
+       "      <td>1203.0</td>\n",
+       "      <td>0.10960</td>\n",
+       "      <td>0.15990</td>\n",
+       "      <td>0.19740</td>\n",
+       "      <td>0.12790</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23.570</td>\n",
+       "      <td>25.53</td>\n",
+       "      <td>152.50</td>\n",
+       "      <td>1709.0</td>\n",
+       "      <td>0.14440</td>\n",
+       "      <td>0.42450</td>\n",
+       "      <td>0.4504</td>\n",
+       "      <td>0.2430</td>\n",
+       "      <td>0.3613</td>\n",
+       "      <td>0.08758</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>84348301</td>\n",
+       "      <td>M</td>\n",
+       "      <td>11.42</td>\n",
+       "      <td>20.38</td>\n",
+       "      <td>77.58</td>\n",
+       "      <td>386.1</td>\n",
+       "      <td>0.14250</td>\n",
+       "      <td>0.28390</td>\n",
+       "      <td>0.24140</td>\n",
+       "      <td>0.10520</td>\n",
+       "      <td>...</td>\n",
+       "      <td>14.910</td>\n",
+       "      <td>26.50</td>\n",
+       "      <td>98.87</td>\n",
+       "      <td>567.7</td>\n",
+       "      <td>0.20980</td>\n",
+       "      <td>0.86630</td>\n",
+       "      <td>0.6869</td>\n",
+       "      <td>0.2575</td>\n",
+       "      <td>0.6638</td>\n",
+       "      <td>0.17300</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>84358402</td>\n",
+       "      <td>M</td>\n",
+       "      <td>20.29</td>\n",
+       "      <td>14.34</td>\n",
+       "      <td>135.10</td>\n",
+       "      <td>1297.0</td>\n",
+       "      <td>0.10030</td>\n",
+       "      <td>0.13280</td>\n",
+       "      <td>0.19800</td>\n",
+       "      <td>0.10430</td>\n",
+       "      <td>...</td>\n",
+       "      <td>22.540</td>\n",
+       "      <td>16.67</td>\n",
+       "      <td>152.20</td>\n",
+       "      <td>1575.0</td>\n",
+       "      <td>0.13740</td>\n",
+       "      <td>0.20500</td>\n",
+       "      <td>0.4000</td>\n",
+       "      <td>0.1625</td>\n",
+       "      <td>0.2364</td>\n",
+       "      <td>0.07678</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>564</th>\n",
+       "      <td>926424</td>\n",
+       "      <td>M</td>\n",
+       "      <td>21.56</td>\n",
+       "      <td>22.39</td>\n",
+       "      <td>142.00</td>\n",
+       "      <td>1479.0</td>\n",
+       "      <td>0.11100</td>\n",
+       "      <td>0.11590</td>\n",
+       "      <td>0.24390</td>\n",
+       "      <td>0.13890</td>\n",
+       "      <td>...</td>\n",
+       "      <td>25.450</td>\n",
+       "      <td>26.40</td>\n",
+       "      <td>166.10</td>\n",
+       "      <td>2027.0</td>\n",
+       "      <td>0.14100</td>\n",
+       "      <td>0.21130</td>\n",
+       "      <td>0.4107</td>\n",
+       "      <td>0.2216</td>\n",
+       "      <td>0.2060</td>\n",
+       "      <td>0.07115</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>565</th>\n",
+       "      <td>926682</td>\n",
+       "      <td>M</td>\n",
+       "      <td>20.13</td>\n",
+       "      <td>28.25</td>\n",
+       "      <td>131.20</td>\n",
+       "      <td>1261.0</td>\n",
+       "      <td>0.09780</td>\n",
+       "      <td>0.10340</td>\n",
+       "      <td>0.14400</td>\n",
+       "      <td>0.09791</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23.690</td>\n",
+       "      <td>38.25</td>\n",
+       "      <td>155.00</td>\n",
+       "      <td>1731.0</td>\n",
+       "      <td>0.11660</td>\n",
+       "      <td>0.19220</td>\n",
+       "      <td>0.3215</td>\n",
+       "      <td>0.1628</td>\n",
+       "      <td>0.2572</td>\n",
+       "      <td>0.06637</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>566</th>\n",
+       "      <td>926954</td>\n",
+       "      <td>M</td>\n",
+       "      <td>16.60</td>\n",
+       "      <td>28.08</td>\n",
+       "      <td>108.30</td>\n",
+       "      <td>858.1</td>\n",
+       "      <td>0.08455</td>\n",
+       "      <td>0.10230</td>\n",
+       "      <td>0.09251</td>\n",
+       "      <td>0.05302</td>\n",
+       "      <td>...</td>\n",
+       "      <td>18.980</td>\n",
+       "      <td>34.12</td>\n",
+       "      <td>126.70</td>\n",
+       "      <td>1124.0</td>\n",
+       "      <td>0.11390</td>\n",
+       "      <td>0.30940</td>\n",
+       "      <td>0.3403</td>\n",
+       "      <td>0.1418</td>\n",
+       "      <td>0.2218</td>\n",
+       "      <td>0.07820</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>567</th>\n",
+       "      <td>927241</td>\n",
+       "      <td>M</td>\n",
+       "      <td>20.60</td>\n",
+       "      <td>29.33</td>\n",
+       "      <td>140.10</td>\n",
+       "      <td>1265.0</td>\n",
+       "      <td>0.11780</td>\n",
+       "      <td>0.27700</td>\n",
+       "      <td>0.35140</td>\n",
+       "      <td>0.15200</td>\n",
+       "      <td>...</td>\n",
+       "      <td>25.740</td>\n",
+       "      <td>39.42</td>\n",
+       "      <td>184.60</td>\n",
+       "      <td>1821.0</td>\n",
+       "      <td>0.16500</td>\n",
+       "      <td>0.86810</td>\n",
+       "      <td>0.9387</td>\n",
+       "      <td>0.2650</td>\n",
+       "      <td>0.4087</td>\n",
+       "      <td>0.12400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>568</th>\n",
+       "      <td>92751</td>\n",
+       "      <td>B</td>\n",
+       "      <td>7.76</td>\n",
+       "      <td>24.54</td>\n",
+       "      <td>47.92</td>\n",
+       "      <td>181.0</td>\n",
+       "      <td>0.05263</td>\n",
+       "      <td>0.04362</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>9.456</td>\n",
+       "      <td>30.37</td>\n",
+       "      <td>59.16</td>\n",
+       "      <td>268.6</td>\n",
+       "      <td>0.08996</td>\n",
+       "      <td>0.06444</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.2871</td>\n",
+       "      <td>0.07039</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>569 rows × 32 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
+       "0      842302         M        17.99         10.38          122.80     1001.0   \n",
+       "1      842517         M        20.57         17.77          132.90     1326.0   \n",
+       "2    84300903         M        19.69         21.25          130.00     1203.0   \n",
+       "3    84348301         M        11.42         20.38           77.58      386.1   \n",
+       "4    84358402         M        20.29         14.34          135.10     1297.0   \n",
+       "..        ...       ...          ...           ...             ...        ...   \n",
+       "564    926424         M        21.56         22.39          142.00     1479.0   \n",
+       "565    926682         M        20.13         28.25          131.20     1261.0   \n",
+       "566    926954         M        16.60         28.08          108.30      858.1   \n",
+       "567    927241         M        20.60         29.33          140.10     1265.0   \n",
+       "568     92751         B         7.76         24.54           47.92      181.0   \n",
+       "\n",
+       "     smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
+       "0            0.11840           0.27760         0.30010              0.14710   \n",
+       "1            0.08474           0.07864         0.08690              0.07017   \n",
+       "2            0.10960           0.15990         0.19740              0.12790   \n",
+       "3            0.14250           0.28390         0.24140              0.10520   \n",
+       "4            0.10030           0.13280         0.19800              0.10430   \n",
+       "..               ...               ...             ...                  ...   \n",
+       "564          0.11100           0.11590         0.24390              0.13890   \n",
+       "565          0.09780           0.10340         0.14400              0.09791   \n",
+       "566          0.08455           0.10230         0.09251              0.05302   \n",
+       "567          0.11780           0.27700         0.35140              0.15200   \n",
+       "568          0.05263           0.04362         0.00000              0.00000   \n",
+       "\n",
+       "     ...  radius_worst  texture_worst  perimeter_worst  area_worst  \\\n",
+       "0    ...        25.380          17.33           184.60      2019.0   \n",
+       "1    ...        24.990          23.41           158.80      1956.0   \n",
+       "2    ...        23.570          25.53           152.50      1709.0   \n",
+       "3    ...        14.910          26.50            98.87       567.7   \n",
+       "4    ...        22.540          16.67           152.20      1575.0   \n",
+       "..   ...           ...            ...              ...         ...   \n",
+       "564  ...        25.450          26.40           166.10      2027.0   \n",
+       "565  ...        23.690          38.25           155.00      1731.0   \n",
+       "566  ...        18.980          34.12           126.70      1124.0   \n",
+       "567  ...        25.740          39.42           184.60      1821.0   \n",
+       "568  ...         9.456          30.37            59.16       268.6   \n",
+       "\n",
+       "     smoothness_worst  compactness_worst  concavity_worst  \\\n",
+       "0             0.16220            0.66560           0.7119   \n",
+       "1             0.12380            0.18660           0.2416   \n",
+       "2             0.14440            0.42450           0.4504   \n",
+       "3             0.20980            0.86630           0.6869   \n",
+       "4             0.13740            0.20500           0.4000   \n",
+       "..                ...                ...              ...   \n",
+       "564           0.14100            0.21130           0.4107   \n",
+       "565           0.11660            0.19220           0.3215   \n",
+       "566           0.11390            0.30940           0.3403   \n",
+       "567           0.16500            0.86810           0.9387   \n",
+       "568           0.08996            0.06444           0.0000   \n",
+       "\n",
+       "     concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
+       "0                  0.2654          0.4601                  0.11890  \n",
+       "1                  0.1860          0.2750                  0.08902  \n",
+       "2                  0.2430          0.3613                  0.08758  \n",
+       "3                  0.2575          0.6638                  0.17300  \n",
+       "4                  0.1625          0.2364                  0.07678  \n",
+       "..                    ...             ...                      ...  \n",
+       "564                0.2216          0.2060                  0.07115  \n",
+       "565                0.1628          0.2572                  0.06637  \n",
+       "566                0.1418          0.2218                  0.07820  \n",
+       "567                0.2650          0.4087                  0.12400  \n",
+       "568                0.0000          0.2871                  0.07039  \n",
+       "\n",
+       "[569 rows x 32 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cancer = pd.read_csv('/Users/vincent/dsi_lcr/LCR/01_materials/notebooks/dataset/wdbc.csv')\n",
+    "cancer\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "85d93b5c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 569 entries, 0 to 568\n",
+      "Data columns (total 32 columns):\n",
+      " #   Column                   Non-Null Count  Dtype  \n",
+      "---  ------                   --------------  -----  \n",
+      " 0   id                       569 non-null    int64  \n",
+      " 1   diagnosis                569 non-null    object \n",
+      " 2   radius_mean              569 non-null    float64\n",
+      " 3   texture_mean             569 non-null    float64\n",
+      " 4   perimeter_mean           569 non-null    float64\n",
+      " 5   area_mean                569 non-null    float64\n",
+      " 6   smoothness_mean          569 non-null    float64\n",
+      " 7   compactness_mean         569 non-null    float64\n",
+      " 8   concavity_mean           569 non-null    float64\n",
+      " 9   concave points_mean      569 non-null    float64\n",
+      " 10  symmetry_mean            569 non-null    float64\n",
+      " 11  fractal_dimension_mean   569 non-null    float64\n",
+      " 12  radius_se                569 non-null    float64\n",
+      " 13  texture_se               569 non-null    float64\n",
+      " 14  perimeter_se             569 non-null    float64\n",
+      " 15  area_se                  569 non-null    float64\n",
+      " 16  smoothness_se            569 non-null    float64\n",
+      " 17  compactness_se           569 non-null    float64\n",
+      " 18  concavity_se             569 non-null    float64\n",
+      " 19  concave points_se        569 non-null    float64\n",
+      " 20  symmetry_se              569 non-null    float64\n",
+      " 21  fractal_dimension_se     569 non-null    float64\n",
+      " 22  radius_worst             569 non-null    float64\n",
+      " 23  texture_worst            569 non-null    float64\n",
+      " 24  perimeter_worst          569 non-null    float64\n",
+      " 25  area_worst               569 non-null    float64\n",
+      " 26  smoothness_worst         569 non-null    float64\n",
+      " 27  compactness_worst        569 non-null    float64\n",
+      " 28  concavity_worst          569 non-null    float64\n",
+      " 29  concave points_worst     569 non-null    float64\n",
+      " 30  symmetry_worst           569 non-null    float64\n",
+      " 31  fractal_dimension_worst  569 non-null    float64\n",
+      "dtypes: float64(30), int64(1), object(1)\n",
+      "memory usage: 142.4+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Look at our data using .info()\n",
+    "cancer.info()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "0e0d8c47",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2\n",
+      "['M' 'B']\n"
+     ]
+    }
+   ],
+   "source": [
+    "#How many unique values are in the diagnosis column?\n",
+    "print(cancer['diagnosis'].nunique())\n",
+    "\n",
+    "#Output should be array(['M', 'B'], dtype=object)\n",
+    "print(cancer['diagnosis'].unique())\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "1cbf07dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Malignant', 'Benign'], dtype=object)"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Replace M with Malignant, B with Benign\n",
+    "cancer['diagnosis'] = cancer['diagnosis'].replace({'M': 'Malignant', 'B': 'Benign'})\n",
+    "cancer['diagnosis'].unique()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "c87d6c1c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "diagnosis\n",
+       "Benign       357\n",
+       "Malignant    212\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cancer['diagnosis'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "f8946340",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "diagnosis\n",
+       "Benign       0.627417\n",
+       "Malignant    0.372583\n",
+       "Name: proportion, dtype: float64"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cancer['diagnosis'].value_counts(normalize=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "e199dc29",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Now, let’s create a scatter plot to visualize the relationship between perimeter mean and concavity mean. This will help us see how these features relate to whether a tumor is benign or malignant.\n",
+    "\n",
+    "# Create mapping between values and colors\n",
+    "labels = cancer[\"diagnosis\"].unique().tolist() #['Malignant', 'Benign']\n",
+    "colors = list(mcolors.TABLEAU_COLORS.keys())    #['tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple', 'tab:brown', 'tab:pink', 'tab:gray', 'tab:olive', 'tab:cyan']\n",
+    "color_map = {l: colors[i % len(colors)] for i, l in enumerate(labels)} #{'Malignant': 'tab:blue', 'Benign': 'tab:orange'}\n",
+    "\n",
+    "# Plot\n",
+    "# Scatter plot of perimeter_mean vs concavity_mean, colored by diagnosis\n",
+    "plt.scatter(cancer[\"perimeter_mean\"], cancer['concavity_mean'], \n",
+    "            color=cancer[\"diagnosis\"].map(color_map))\n",
+    "\n",
+    "# Create custom legend handles\n",
+    "# Plot legend\n",
+    "handles = [plt.Line2D([0], [0], marker='o', color='w', label=label,\n",
+    "                      markersize=10, markerfacecolor=color_map[label])\n",
+    "           for label in labels]\n",
+    "\n",
+    "# Add labels and legend\n",
+    "plt.xlabel('Perimeter Mean')\n",
+    "plt.ylabel('Concavity Mean')\n",
+    "plt.title('Scatter Plot of Perimeter Mean vs Concavity Mean')\n",
+    "plt.legend(handles=handles, title='Diagnosis')\n",
+    "plt.show()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "e7f88d77",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot existing data\n",
+    "plt.scatter(cancer[\"perimeter_mean\"], cancer['concavity_mean'], \n",
+    "            color=cancer[\"diagnosis\"].map(color_map))\n",
+    "\n",
+    "# Create custom legend handles\n",
+    "handles = [plt.Line2D([0], [0], marker='o', color='w', label=label,\n",
+    "                      markersize=10, markerfacecolor=color_map[label])\n",
+    "           for label in labels]\n",
+    "\n",
+    "# Add new observation\n",
+    "new_observation = {'perimeter_mean': 97, 'concavity_mean': 0.20}\n",
+    "plt.scatter(new_observation['perimeter_mean'], new_observation['concavity_mean'],\n",
+    "            color='red', edgecolor='black', s=100, label='New Observation')\n",
+    "\n",
+    "# Add labels and legend\n",
+    "plt.xlabel('Perimeter Mean')\n",
+    "plt.ylabel('Concavity Mean')\n",
+    "plt.title('Scatter Plot of Perimeter Mean vs Concavity Mean')\n",
+    "plt.legend(handles=handles + [plt.Line2D([0], [0], marker='o', color='w', \n",
+    "                                          markerfacecolor='red', markeredgecolor='black', \n",
+    "                                          markersize=10, label='New Observation')], \n",
+    "           title='Diagnosis')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "3cb834c9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "new_obs_Perimeter = 97\n",
+    "new_obs_Concavity = 0.20\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "d12c9452",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "cancer[\"dist_from_new\"] = ((cancer['perimeter_mean'] - new_obs_Perimeter)**2 + \n",
+    "(cancer['concavity_mean'] - new_obs_Concavity)**2) ** (1/2)\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "6bc21a95",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>diagnosis</th>\n",
+       "      <th>radius_mean</th>\n",
+       "      <th>texture_mean</th>\n",
+       "      <th>perimeter_mean</th>\n",
+       "      <th>area_mean</th>\n",
+       "      <th>smoothness_mean</th>\n",
+       "      <th>compactness_mean</th>\n",
+       "      <th>concavity_mean</th>\n",
+       "      <th>concave points_mean</th>\n",
+       "      <th>...</th>\n",
+       "      <th>texture_worst</th>\n",
+       "      <th>perimeter_worst</th>\n",
+       "      <th>area_worst</th>\n",
+       "      <th>smoothness_worst</th>\n",
+       "      <th>compactness_worst</th>\n",
+       "      <th>concavity_worst</th>\n",
+       "      <th>concave points_worst</th>\n",
+       "      <th>symmetry_worst</th>\n",
+       "      <th>fractal_dimension_worst</th>\n",
+       "      <th>dist_from_new</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>842302</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>17.99</td>\n",
+       "      <td>10.38</td>\n",
+       "      <td>122.80</td>\n",
+       "      <td>1001.0</td>\n",
+       "      <td>0.11840</td>\n",
+       "      <td>0.27760</td>\n",
+       "      <td>0.30010</td>\n",
+       "      <td>0.14710</td>\n",
+       "      <td>...</td>\n",
+       "      <td>17.33</td>\n",
+       "      <td>184.60</td>\n",
+       "      <td>2019.0</td>\n",
+       "      <td>0.16220</td>\n",
+       "      <td>0.66560</td>\n",
+       "      <td>0.7119</td>\n",
+       "      <td>0.2654</td>\n",
+       "      <td>0.4601</td>\n",
+       "      <td>0.11890</td>\n",
+       "      <td>25.800194</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>842517</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>20.57</td>\n",
+       "      <td>17.77</td>\n",
+       "      <td>132.90</td>\n",
+       "      <td>1326.0</td>\n",
+       "      <td>0.08474</td>\n",
+       "      <td>0.07864</td>\n",
+       "      <td>0.08690</td>\n",
+       "      <td>0.07017</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23.41</td>\n",
+       "      <td>158.80</td>\n",
+       "      <td>1956.0</td>\n",
+       "      <td>0.12380</td>\n",
+       "      <td>0.18660</td>\n",
+       "      <td>0.2416</td>\n",
+       "      <td>0.1860</td>\n",
+       "      <td>0.2750</td>\n",
+       "      <td>0.08902</td>\n",
+       "      <td>35.900178</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>84300903</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>19.69</td>\n",
+       "      <td>21.25</td>\n",
+       "      <td>130.00</td>\n",
+       "      <td>1203.0</td>\n",
+       "      <td>0.10960</td>\n",
+       "      <td>0.15990</td>\n",
+       "      <td>0.19740</td>\n",
+       "      <td>0.12790</td>\n",
+       "      <td>...</td>\n",
+       "      <td>25.53</td>\n",
+       "      <td>152.50</td>\n",
+       "      <td>1709.0</td>\n",
+       "      <td>0.14440</td>\n",
+       "      <td>0.42450</td>\n",
+       "      <td>0.4504</td>\n",
+       "      <td>0.2430</td>\n",
+       "      <td>0.3613</td>\n",
+       "      <td>0.08758</td>\n",
+       "      <td>33.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>84348301</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>11.42</td>\n",
+       "      <td>20.38</td>\n",
+       "      <td>77.58</td>\n",
+       "      <td>386.1</td>\n",
+       "      <td>0.14250</td>\n",
+       "      <td>0.28390</td>\n",
+       "      <td>0.24140</td>\n",
+       "      <td>0.10520</td>\n",
+       "      <td>...</td>\n",
+       "      <td>26.50</td>\n",
+       "      <td>98.87</td>\n",
+       "      <td>567.7</td>\n",
+       "      <td>0.20980</td>\n",
+       "      <td>0.86630</td>\n",
+       "      <td>0.6869</td>\n",
+       "      <td>0.2575</td>\n",
+       "      <td>0.6638</td>\n",
+       "      <td>0.17300</td>\n",
+       "      <td>19.420044</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>84358402</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>20.29</td>\n",
+       "      <td>14.34</td>\n",
+       "      <td>135.10</td>\n",
+       "      <td>1297.0</td>\n",
+       "      <td>0.10030</td>\n",
+       "      <td>0.13280</td>\n",
+       "      <td>0.19800</td>\n",
+       "      <td>0.10430</td>\n",
+       "      <td>...</td>\n",
+       "      <td>16.67</td>\n",
+       "      <td>152.20</td>\n",
+       "      <td>1575.0</td>\n",
+       "      <td>0.13740</td>\n",
+       "      <td>0.20500</td>\n",
+       "      <td>0.4000</td>\n",
+       "      <td>0.1625</td>\n",
+       "      <td>0.2364</td>\n",
+       "      <td>0.07678</td>\n",
+       "      <td>38.100000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>564</th>\n",
+       "      <td>926424</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>21.56</td>\n",
+       "      <td>22.39</td>\n",
+       "      <td>142.00</td>\n",
+       "      <td>1479.0</td>\n",
+       "      <td>0.11100</td>\n",
+       "      <td>0.11590</td>\n",
+       "      <td>0.24390</td>\n",
+       "      <td>0.13890</td>\n",
+       "      <td>...</td>\n",
+       "      <td>26.40</td>\n",
+       "      <td>166.10</td>\n",
+       "      <td>2027.0</td>\n",
+       "      <td>0.14100</td>\n",
+       "      <td>0.21130</td>\n",
+       "      <td>0.4107</td>\n",
+       "      <td>0.2216</td>\n",
+       "      <td>0.2060</td>\n",
+       "      <td>0.07115</td>\n",
+       "      <td>45.000021</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>565</th>\n",
+       "      <td>926682</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>20.13</td>\n",
+       "      <td>28.25</td>\n",
+       "      <td>131.20</td>\n",
+       "      <td>1261.0</td>\n",
+       "      <td>0.09780</td>\n",
+       "      <td>0.10340</td>\n",
+       "      <td>0.14400</td>\n",
+       "      <td>0.09791</td>\n",
+       "      <td>...</td>\n",
+       "      <td>38.25</td>\n",
+       "      <td>155.00</td>\n",
+       "      <td>1731.0</td>\n",
+       "      <td>0.11660</td>\n",
+       "      <td>0.19220</td>\n",
+       "      <td>0.3215</td>\n",
+       "      <td>0.1628</td>\n",
+       "      <td>0.2572</td>\n",
+       "      <td>0.06637</td>\n",
+       "      <td>34.200046</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>566</th>\n",
+       "      <td>926954</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>16.60</td>\n",
+       "      <td>28.08</td>\n",
+       "      <td>108.30</td>\n",
+       "      <td>858.1</td>\n",
+       "      <td>0.08455</td>\n",
+       "      <td>0.10230</td>\n",
+       "      <td>0.09251</td>\n",
+       "      <td>0.05302</td>\n",
+       "      <td>...</td>\n",
+       "      <td>34.12</td>\n",
+       "      <td>126.70</td>\n",
+       "      <td>1124.0</td>\n",
+       "      <td>0.11390</td>\n",
+       "      <td>0.30940</td>\n",
+       "      <td>0.3403</td>\n",
+       "      <td>0.1418</td>\n",
+       "      <td>0.2218</td>\n",
+       "      <td>0.07820</td>\n",
+       "      <td>11.300511</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>567</th>\n",
+       "      <td>927241</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>20.60</td>\n",
+       "      <td>29.33</td>\n",
+       "      <td>140.10</td>\n",
+       "      <td>1265.0</td>\n",
+       "      <td>0.11780</td>\n",
+       "      <td>0.27700</td>\n",
+       "      <td>0.35140</td>\n",
+       "      <td>0.15200</td>\n",
+       "      <td>...</td>\n",
+       "      <td>39.42</td>\n",
+       "      <td>184.60</td>\n",
+       "      <td>1821.0</td>\n",
+       "      <td>0.16500</td>\n",
+       "      <td>0.86810</td>\n",
+       "      <td>0.9387</td>\n",
+       "      <td>0.2650</td>\n",
+       "      <td>0.4087</td>\n",
+       "      <td>0.12400</td>\n",
+       "      <td>43.100266</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>568</th>\n",
+       "      <td>92751</td>\n",
+       "      <td>Benign</td>\n",
+       "      <td>7.76</td>\n",
+       "      <td>24.54</td>\n",
+       "      <td>47.92</td>\n",
+       "      <td>181.0</td>\n",
+       "      <td>0.05263</td>\n",
+       "      <td>0.04362</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>30.37</td>\n",
+       "      <td>59.16</td>\n",
+       "      <td>268.6</td>\n",
+       "      <td>0.08996</td>\n",
+       "      <td>0.06444</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.2871</td>\n",
+       "      <td>0.07039</td>\n",
+       "      <td>49.080407</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>569 rows × 33 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           id  diagnosis  radius_mean  texture_mean  perimeter_mean  \\\n",
+       "0      842302  Malignant        17.99         10.38          122.80   \n",
+       "1      842517  Malignant        20.57         17.77          132.90   \n",
+       "2    84300903  Malignant        19.69         21.25          130.00   \n",
+       "3    84348301  Malignant        11.42         20.38           77.58   \n",
+       "4    84358402  Malignant        20.29         14.34          135.10   \n",
+       "..        ...        ...          ...           ...             ...   \n",
+       "564    926424  Malignant        21.56         22.39          142.00   \n",
+       "565    926682  Malignant        20.13         28.25          131.20   \n",
+       "566    926954  Malignant        16.60         28.08          108.30   \n",
+       "567    927241  Malignant        20.60         29.33          140.10   \n",
+       "568     92751     Benign         7.76         24.54           47.92   \n",
+       "\n",
+       "     area_mean  smoothness_mean  compactness_mean  concavity_mean  \\\n",
+       "0       1001.0          0.11840           0.27760         0.30010   \n",
+       "1       1326.0          0.08474           0.07864         0.08690   \n",
+       "2       1203.0          0.10960           0.15990         0.19740   \n",
+       "3        386.1          0.14250           0.28390         0.24140   \n",
+       "4       1297.0          0.10030           0.13280         0.19800   \n",
+       "..         ...              ...               ...             ...   \n",
+       "564     1479.0          0.11100           0.11590         0.24390   \n",
+       "565     1261.0          0.09780           0.10340         0.14400   \n",
+       "566      858.1          0.08455           0.10230         0.09251   \n",
+       "567     1265.0          0.11780           0.27700         0.35140   \n",
+       "568      181.0          0.05263           0.04362         0.00000   \n",
+       "\n",
+       "     concave points_mean  ...  texture_worst  perimeter_worst  area_worst  \\\n",
+       "0                0.14710  ...          17.33           184.60      2019.0   \n",
+       "1                0.07017  ...          23.41           158.80      1956.0   \n",
+       "2                0.12790  ...          25.53           152.50      1709.0   \n",
+       "3                0.10520  ...          26.50            98.87       567.7   \n",
+       "4                0.10430  ...          16.67           152.20      1575.0   \n",
+       "..                   ...  ...            ...              ...         ...   \n",
+       "564              0.13890  ...          26.40           166.10      2027.0   \n",
+       "565              0.09791  ...          38.25           155.00      1731.0   \n",
+       "566              0.05302  ...          34.12           126.70      1124.0   \n",
+       "567              0.15200  ...          39.42           184.60      1821.0   \n",
+       "568              0.00000  ...          30.37            59.16       268.6   \n",
+       "\n",
+       "     smoothness_worst  compactness_worst  concavity_worst  \\\n",
+       "0             0.16220            0.66560           0.7119   \n",
+       "1             0.12380            0.18660           0.2416   \n",
+       "2             0.14440            0.42450           0.4504   \n",
+       "3             0.20980            0.86630           0.6869   \n",
+       "4             0.13740            0.20500           0.4000   \n",
+       "..                ...                ...              ...   \n",
+       "564           0.14100            0.21130           0.4107   \n",
+       "565           0.11660            0.19220           0.3215   \n",
+       "566           0.11390            0.30940           0.3403   \n",
+       "567           0.16500            0.86810           0.9387   \n",
+       "568           0.08996            0.06444           0.0000   \n",
+       "\n",
+       "     concave points_worst  symmetry_worst  fractal_dimension_worst  \\\n",
+       "0                  0.2654          0.4601                  0.11890   \n",
+       "1                  0.1860          0.2750                  0.08902   \n",
+       "2                  0.2430          0.3613                  0.08758   \n",
+       "3                  0.2575          0.6638                  0.17300   \n",
+       "4                  0.1625          0.2364                  0.07678   \n",
+       "..                    ...             ...                      ...   \n",
+       "564                0.2216          0.2060                  0.07115   \n",
+       "565                0.1628          0.2572                  0.06637   \n",
+       "566                0.1418          0.2218                  0.07820   \n",
+       "567                0.2650          0.4087                  0.12400   \n",
+       "568                0.0000          0.2871                  0.07039   \n",
+       "\n",
+       "     dist_from_new  \n",
+       "0        25.800194  \n",
+       "1        35.900178  \n",
+       "2        33.000000  \n",
+       "3        19.420044  \n",
+       "4        38.100000  \n",
+       "..             ...  \n",
+       "564      45.000021  \n",
+       "565      34.200046  \n",
+       "566      11.300511  \n",
+       "567      43.100266  \n",
+       "568      49.080407  \n",
+       "\n",
+       "[569 rows x 33 columns]"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cancer\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3bcac4cf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>perimeter_mean</th>\n",
+       "      <th>concavity_mean</th>\n",
+       "      <th>diagnosis</th>\n",
+       "      <th>dist_from_new</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>291</th>\n",
+       "      <td>97.03</td>\n",
+       "      <td>0.05940</td>\n",
+       "      <td>Benign</td>\n",
+       "      <td>0.143765</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>138</th>\n",
+       "      <td>96.85</td>\n",
+       "      <td>0.15390</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>0.156924</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>96.73</td>\n",
+       "      <td>0.16390</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>0.272403</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>514</th>\n",
+       "      <td>97.26</td>\n",
+       "      <td>0.07486</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>0.288548</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54</th>\n",
+       "      <td>97.26</td>\n",
+       "      <td>0.05253</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>0.298910</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     perimeter_mean  concavity_mean  diagnosis  dist_from_new\n",
+       "291           97.03         0.05940     Benign       0.143765\n",
+       "138           96.85         0.15390  Malignant       0.156924\n",
+       "15            96.73         0.16390  Malignant       0.272403\n",
+       "514           97.26         0.07486  Malignant       0.288548\n",
+       "54            97.26         0.05253  Malignant       0.298910"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Find the 5 closest points to our new observation, and look at the perimter mean, concavity mean, diagnosis, distance from new\n",
+    "cancer.nsmallest(5, 'dist_from_new')[['perimeter_mean', 'concavity_mean', 'diagnosis', 'dist_from_new']]    \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "1cd11e6e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<module 'matplotlib.pyplot' from '/opt/anaconda3/envs/dsi_participant/lib/python3.12/site-packages/matplotlib/pyplot.py'>"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Incorporate more features to improve accuracy\n",
+    "#Include symmetry_mean as a third dimension\n",
+    "\n",
+    "# Create mapping between values and colors\n",
+    "labels = cancer[\"diagnosis\"].unique().tolist()\n",
+    "colors = list(mcolors.TABLEAU_COLORS.keys())\n",
+    "color_map = {l: colors[i % len(colors)] for i, l in enumerate(labels)}\n",
+    "\n",
+    "# Create a 3D plot\n",
+    "ax = plt.axes(projection=\"3d\")\n",
+    "\n",
+    "# Plot data points with color corresponding to diagnosis\n",
+    "sc = ax.scatter3D(cancer['perimeter_mean'], cancer['concavity_mean'], cancer['symmetry_mean'], \n",
+    "                  c=cancer['diagnosis'].map(color_map), marker='o')\n",
+    "\n",
+    "# Define the new observation\n",
+    "new_observation = {'perimeter_mean': 97, 'concavity_mean': 0.20, 'symmetry_mean': 0.22}\n",
+    "\n",
+    "# Plot the new observation\n",
+    "ax.scatter3D(new_observation['perimeter_mean'], new_observation['concavity_mean'], \n",
+    "             new_observation['symmetry_mean'], color='red', edgecolor='black', \n",
+    "             s=100, marker='o', label='New Observation')\n",
+    "\n",
+    "# Add axis labels\n",
+    "ax.set_xlabel('Perimeter Mean')\n",
+    "ax.set_ylabel('Concavity Mean')\n",
+    "ax.set_zlabel('Symmetry Mean')\n",
+    "ax.set_title('3D Scatter Plot of Perimeter Mean, Concavity Mean, and Symmetry Mean')\n",
+    "\n",
+    "# Create custom legend handles\n",
+    "handles = [plt.Line2D([0], [0], marker='o', color='w', label=label,\n",
+    "                      markersize=10, markerfacecolor=color_map[label])\n",
+    "           for label in labels]\n",
+    "\n",
+    "# Add custom legend for new observation\n",
+    "handles.append(plt.Line2D([0], [0], marker='o', color='red', label='New Observation', \n",
+    "                          markersize=10, markeredgecolor='black'))\n",
+    "\n",
+    "# Add legend\n",
+    "plt.legend(handles=handles, title='Diagnosis')\n",
+    "\n",
+    "# Show plot\n",
+    "plt\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "900d79d7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>perimeter_mean</th>\n",
+       "      <th>concavity_mean</th>\n",
+       "      <th>symmetry_mean</th>\n",
+       "      <th>diagnosis</th>\n",
+       "      <th>dist_from_new</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>291</th>\n",
+       "      <td>97.03</td>\n",
+       "      <td>0.05940</td>\n",
+       "      <td>0.1879</td>\n",
+       "      <td>Benign</td>\n",
+       "      <td>0.147305</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>138</th>\n",
+       "      <td>96.85</td>\n",
+       "      <td>0.15390</td>\n",
+       "      <td>0.1957</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>0.158795</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>96.73</td>\n",
+       "      <td>0.16390</td>\n",
+       "      <td>0.2303</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>0.272597</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>514</th>\n",
+       "      <td>97.26</td>\n",
+       "      <td>0.07486</td>\n",
+       "      <td>0.1561</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>0.295539</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54</th>\n",
+       "      <td>97.26</td>\n",
+       "      <td>0.05253</td>\n",
+       "      <td>0.1616</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>0.304562</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     perimeter_mean  concavity_mean  symmetry_mean  diagnosis  dist_from_new\n",
+       "291           97.03         0.05940         0.1879     Benign       0.147305\n",
+       "138           96.85         0.15390         0.1957  Malignant       0.158795\n",
+       "15            96.73         0.16390         0.2303  Malignant       0.272597\n",
+       "514           97.26         0.07486         0.1561  Malignant       0.295539\n",
+       "54            97.26         0.05253         0.1616  Malignant       0.304562"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#New observation of perimeter_mean = 97, concavity_mean = 0.20, symmetry_mean = 0.22\n",
+    "\n",
+    "new_obs_Perimeter = 97\n",
+    "new_obs_Concavity = 0.20\n",
+    "new_obs_Symmetry = 0.22\n",
+    "\n",
+    "cancer[\"dist_from_new\"] = ((cancer['perimeter_mean'] - new_obs_Perimeter)**2 + \n",
+    "(cancer['concavity_mean'] - new_obs_Concavity)**2 +\n",
+    "(cancer['symmetry_mean'] - new_obs_Symmetry)**2) ** (1/2)\n",
+    "\n",
+    "#Find the 5 closest points to our new observation, and look at the perimter mean, concavity mean, diagnosis, distance from new\n",
+    "cancer.nsmallest(5, 'dist_from_new')[['perimeter_mean', 'concavity_mean', 'symmetry_mean', 'diagnosis', 'dist_from_new']]\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "eee634be",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0      25.800203\n",
+       "1      35.900199\n",
+       "2      33.000003\n",
+       "3      19.420085\n",
+       "4      38.100020\n",
+       "         ...    \n",
+       "564    45.000046\n",
+       "565    34.200075\n",
+       "566    11.300676\n",
+       "567    43.100270\n",
+       "568    49.080446\n",
+       "Name: dist_from_new, Length: 569, dtype: float64"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cancer['dist_from_new']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "7bc1c8f0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nearest_5 = cancer.nsmallest(5, \"dist_from_new\")[[\n",
+    "    \"perimeter_mean\",\n",
+    "    \"concavity_mean\",\n",
+    "    \"symmetry_mean\",\n",
+    "    \"diagnosis\",\n",
+    "    \"dist_from_new\"\n",
+    "]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "36637a59",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>perimeter_mean</th>\n",
+       "      <th>concavity_mean</th>\n",
+       "      <th>symmetry_mean</th>\n",
+       "      <th>diagnosis</th>\n",
+       "      <th>dist_from_new</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>291</th>\n",
+       "      <td>97.03</td>\n",
+       "      <td>0.05940</td>\n",
+       "      <td>0.1879</td>\n",
+       "      <td>Benign</td>\n",
+       "      <td>0.147305</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>138</th>\n",
+       "      <td>96.85</td>\n",
+       "      <td>0.15390</td>\n",
+       "      <td>0.1957</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>0.158795</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>96.73</td>\n",
+       "      <td>0.16390</td>\n",
+       "      <td>0.2303</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>0.272597</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>514</th>\n",
+       "      <td>97.26</td>\n",
+       "      <td>0.07486</td>\n",
+       "      <td>0.1561</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>0.295539</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54</th>\n",
+       "      <td>97.26</td>\n",
+       "      <td>0.05253</td>\n",
+       "      <td>0.1616</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>0.304562</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     perimeter_mean  concavity_mean  symmetry_mean  diagnosis  dist_from_new\n",
+       "291           97.03         0.05940         0.1879     Benign       0.147305\n",
+       "138           96.85         0.15390         0.1957  Malignant       0.158795\n",
+       "15            96.73         0.16390         0.2303  Malignant       0.272597\n",
+       "514           97.26         0.07486         0.1561  Malignant       0.295539\n",
+       "54            97.26         0.05253         0.1616  Malignant       0.304562"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nearest_5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "39076891",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#import sklearn's KNeighborsClassifier\n",
+    "from sklearn import set_config\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "baa0f1e0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Output dataframes instead of arrays\n",
+    "set_config(transform_output=\"pandas\")  # Other option is 'text'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "a2d7cdb4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "b320e010",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>diagnosis</th>\n",
+       "      <th>perimeter_mean</th>\n",
+       "      <th>concavity_mean</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>122.80</td>\n",
+       "      <td>0.30010</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>132.90</td>\n",
+       "      <td>0.08690</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>130.00</td>\n",
+       "      <td>0.19740</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>77.58</td>\n",
+       "      <td>0.24140</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>135.10</td>\n",
+       "      <td>0.19800</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>564</th>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>142.00</td>\n",
+       "      <td>0.24390</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>565</th>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>131.20</td>\n",
+       "      <td>0.14400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>566</th>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>108.30</td>\n",
+       "      <td>0.09251</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>567</th>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>140.10</td>\n",
+       "      <td>0.35140</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>568</th>\n",
+       "      <td>Benign</td>\n",
+       "      <td>47.92</td>\n",
+       "      <td>0.00000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>569 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     diagnosis  perimeter_mean  concavity_mean\n",
+       "0    Malignant          122.80         0.30010\n",
+       "1    Malignant          132.90         0.08690\n",
+       "2    Malignant          130.00         0.19740\n",
+       "3    Malignant           77.58         0.24140\n",
+       "4    Malignant          135.10         0.19800\n",
+       "..         ...             ...             ...\n",
+       "564  Malignant          142.00         0.24390\n",
+       "565  Malignant          131.20         0.14400\n",
+       "566  Malignant          108.30         0.09251\n",
+       "567  Malignant          140.10         0.35140\n",
+       "568     Benign           47.92         0.00000\n",
+       "\n",
+       "[569 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cancer_train = cancer[[\"diagnosis\", \"perimeter_mean\", \"concavity_mean\"]]\n",
+    "cancer_train\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "1c31219d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #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-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: 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-1 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-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 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-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\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-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><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": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "knn = KNeighborsClassifier(n_neighbors=5)\n",
+    "knn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "fbf5c0c2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#define predictors and response variable\n",
+    "X = cancer_train[[\"perimeter_mean\", \"concavity_mean\"]]\n",
+    "y = cancer_train[\"diagnosis\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "18bd9018",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-2 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #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-2 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-2 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: 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-2 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-2 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-2 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-2 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 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-2 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\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",
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+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
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+       "}\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",
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+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
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+       "}\n",
+       "\n",
+       ".user-set td i,\n",
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+       "    color: black;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
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+       "    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-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><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=\"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",
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+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
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+       "\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",
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+       "\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": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "knn.fit(X, y)   #We are fitting the model to our data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "e86da175",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>perimeter_mean</th>\n",
+       "      <th>concavity_mean</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>97</td>\n",
+       "      <td>0.2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   perimeter_mean  concavity_mean\n",
+       "0              97             0.2"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "new_obs = pd.DataFrame({\"perimeter_mean\" :[97],\n",
+    "                        \"concavity_mean\": [0.20]})\n",
+    "new_obs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "97aad866",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Malignant'], dtype=object)"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Predict the diagnosis for our new observation\n",
+    "knn.predict(new_obs)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "dsi_participant",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
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+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/04_this_cohort/live_code/live_code_09-03-25.ipynb b/04_this_cohort/live_code/live_code_09-03-25.ipynb
new file mode 100644
index 000000000..a3c525047
--- /dev/null
+++ b/04_this_cohort/live_code/live_code_09-03-25.ipynb
@@ -0,0 +1,6845 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "8b7a39f4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.metrics import recall_score, precision_score\n",
+    "from sklearn.model_selection import cross_validate\n",
+    "from sklearn.model_selection import GridSearchCV"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "bf78297c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>diagnosis</th>\n",
+       "      <th>radius_mean</th>\n",
+       "      <th>texture_mean</th>\n",
+       "      <th>perimeter_mean</th>\n",
+       "      <th>area_mean</th>\n",
+       "      <th>smoothness_mean</th>\n",
+       "      <th>compactness_mean</th>\n",
+       "      <th>concavity_mean</th>\n",
+       "      <th>concave points_mean</th>\n",
+       "      <th>...</th>\n",
+       "      <th>radius_worst</th>\n",
+       "      <th>texture_worst</th>\n",
+       "      <th>perimeter_worst</th>\n",
+       "      <th>area_worst</th>\n",
+       "      <th>smoothness_worst</th>\n",
+       "      <th>compactness_worst</th>\n",
+       "      <th>concavity_worst</th>\n",
+       "      <th>concave points_worst</th>\n",
+       "      <th>symmetry_worst</th>\n",
+       "      <th>fractal_dimension_worst</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>842302</td>\n",
+       "      <td>M</td>\n",
+       "      <td>17.99</td>\n",
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+       "      <td>1001.0</td>\n",
+       "      <td>0.11840</td>\n",
+       "      <td>0.27760</td>\n",
+       "      <td>0.30010</td>\n",
+       "      <td>0.14710</td>\n",
+       "      <td>...</td>\n",
+       "      <td>25.380</td>\n",
+       "      <td>17.33</td>\n",
+       "      <td>184.60</td>\n",
+       "      <td>2019.0</td>\n",
+       "      <td>0.16220</td>\n",
+       "      <td>0.66560</td>\n",
+       "      <td>0.7119</td>\n",
+       "      <td>0.2654</td>\n",
+       "      <td>0.4601</td>\n",
+       "      <td>0.11890</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>842517</td>\n",
+       "      <td>M</td>\n",
+       "      <td>20.57</td>\n",
+       "      <td>17.77</td>\n",
+       "      <td>132.90</td>\n",
+       "      <td>1326.0</td>\n",
+       "      <td>0.08474</td>\n",
+       "      <td>0.07864</td>\n",
+       "      <td>0.08690</td>\n",
+       "      <td>0.07017</td>\n",
+       "      <td>...</td>\n",
+       "      <td>24.990</td>\n",
+       "      <td>23.41</td>\n",
+       "      <td>158.80</td>\n",
+       "      <td>1956.0</td>\n",
+       "      <td>0.12380</td>\n",
+       "      <td>0.18660</td>\n",
+       "      <td>0.2416</td>\n",
+       "      <td>0.1860</td>\n",
+       "      <td>0.2750</td>\n",
+       "      <td>0.08902</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>84300903</td>\n",
+       "      <td>M</td>\n",
+       "      <td>19.69</td>\n",
+       "      <td>21.25</td>\n",
+       "      <td>130.00</td>\n",
+       "      <td>1203.0</td>\n",
+       "      <td>0.10960</td>\n",
+       "      <td>0.15990</td>\n",
+       "      <td>0.19740</td>\n",
+       "      <td>0.12790</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23.570</td>\n",
+       "      <td>25.53</td>\n",
+       "      <td>152.50</td>\n",
+       "      <td>1709.0</td>\n",
+       "      <td>0.14440</td>\n",
+       "      <td>0.42450</td>\n",
+       "      <td>0.4504</td>\n",
+       "      <td>0.2430</td>\n",
+       "      <td>0.3613</td>\n",
+       "      <td>0.08758</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>84348301</td>\n",
+       "      <td>M</td>\n",
+       "      <td>11.42</td>\n",
+       "      <td>20.38</td>\n",
+       "      <td>77.58</td>\n",
+       "      <td>386.1</td>\n",
+       "      <td>0.14250</td>\n",
+       "      <td>0.28390</td>\n",
+       "      <td>0.24140</td>\n",
+       "      <td>0.10520</td>\n",
+       "      <td>...</td>\n",
+       "      <td>14.910</td>\n",
+       "      <td>26.50</td>\n",
+       "      <td>98.87</td>\n",
+       "      <td>567.7</td>\n",
+       "      <td>0.20980</td>\n",
+       "      <td>0.86630</td>\n",
+       "      <td>0.6869</td>\n",
+       "      <td>0.2575</td>\n",
+       "      <td>0.6638</td>\n",
+       "      <td>0.17300</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>84358402</td>\n",
+       "      <td>M</td>\n",
+       "      <td>20.29</td>\n",
+       "      <td>14.34</td>\n",
+       "      <td>135.10</td>\n",
+       "      <td>1297.0</td>\n",
+       "      <td>0.10030</td>\n",
+       "      <td>0.13280</td>\n",
+       "      <td>0.19800</td>\n",
+       "      <td>0.10430</td>\n",
+       "      <td>...</td>\n",
+       "      <td>22.540</td>\n",
+       "      <td>16.67</td>\n",
+       "      <td>152.20</td>\n",
+       "      <td>1575.0</td>\n",
+       "      <td>0.13740</td>\n",
+       "      <td>0.20500</td>\n",
+       "      <td>0.4000</td>\n",
+       "      <td>0.1625</td>\n",
+       "      <td>0.2364</td>\n",
+       "      <td>0.07678</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>564</th>\n",
+       "      <td>926424</td>\n",
+       "      <td>M</td>\n",
+       "      <td>21.56</td>\n",
+       "      <td>22.39</td>\n",
+       "      <td>142.00</td>\n",
+       "      <td>1479.0</td>\n",
+       "      <td>0.11100</td>\n",
+       "      <td>0.11590</td>\n",
+       "      <td>0.24390</td>\n",
+       "      <td>0.13890</td>\n",
+       "      <td>...</td>\n",
+       "      <td>25.450</td>\n",
+       "      <td>26.40</td>\n",
+       "      <td>166.10</td>\n",
+       "      <td>2027.0</td>\n",
+       "      <td>0.14100</td>\n",
+       "      <td>0.21130</td>\n",
+       "      <td>0.4107</td>\n",
+       "      <td>0.2216</td>\n",
+       "      <td>0.2060</td>\n",
+       "      <td>0.07115</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>565</th>\n",
+       "      <td>926682</td>\n",
+       "      <td>M</td>\n",
+       "      <td>20.13</td>\n",
+       "      <td>28.25</td>\n",
+       "      <td>131.20</td>\n",
+       "      <td>1261.0</td>\n",
+       "      <td>0.09780</td>\n",
+       "      <td>0.10340</td>\n",
+       "      <td>0.14400</td>\n",
+       "      <td>0.09791</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23.690</td>\n",
+       "      <td>38.25</td>\n",
+       "      <td>155.00</td>\n",
+       "      <td>1731.0</td>\n",
+       "      <td>0.11660</td>\n",
+       "      <td>0.19220</td>\n",
+       "      <td>0.3215</td>\n",
+       "      <td>0.1628</td>\n",
+       "      <td>0.2572</td>\n",
+       "      <td>0.06637</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>566</th>\n",
+       "      <td>926954</td>\n",
+       "      <td>M</td>\n",
+       "      <td>16.60</td>\n",
+       "      <td>28.08</td>\n",
+       "      <td>108.30</td>\n",
+       "      <td>858.1</td>\n",
+       "      <td>0.08455</td>\n",
+       "      <td>0.10230</td>\n",
+       "      <td>0.09251</td>\n",
+       "      <td>0.05302</td>\n",
+       "      <td>...</td>\n",
+       "      <td>18.980</td>\n",
+       "      <td>34.12</td>\n",
+       "      <td>126.70</td>\n",
+       "      <td>1124.0</td>\n",
+       "      <td>0.11390</td>\n",
+       "      <td>0.30940</td>\n",
+       "      <td>0.3403</td>\n",
+       "      <td>0.1418</td>\n",
+       "      <td>0.2218</td>\n",
+       "      <td>0.07820</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>567</th>\n",
+       "      <td>927241</td>\n",
+       "      <td>M</td>\n",
+       "      <td>20.60</td>\n",
+       "      <td>29.33</td>\n",
+       "      <td>140.10</td>\n",
+       "      <td>1265.0</td>\n",
+       "      <td>0.11780</td>\n",
+       "      <td>0.27700</td>\n",
+       "      <td>0.35140</td>\n",
+       "      <td>0.15200</td>\n",
+       "      <td>...</td>\n",
+       "      <td>25.740</td>\n",
+       "      <td>39.42</td>\n",
+       "      <td>184.60</td>\n",
+       "      <td>1821.0</td>\n",
+       "      <td>0.16500</td>\n",
+       "      <td>0.86810</td>\n",
+       "      <td>0.9387</td>\n",
+       "      <td>0.2650</td>\n",
+       "      <td>0.4087</td>\n",
+       "      <td>0.12400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>568</th>\n",
+       "      <td>92751</td>\n",
+       "      <td>B</td>\n",
+       "      <td>7.76</td>\n",
+       "      <td>24.54</td>\n",
+       "      <td>47.92</td>\n",
+       "      <td>181.0</td>\n",
+       "      <td>0.05263</td>\n",
+       "      <td>0.04362</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>9.456</td>\n",
+       "      <td>30.37</td>\n",
+       "      <td>59.16</td>\n",
+       "      <td>268.6</td>\n",
+       "      <td>0.08996</td>\n",
+       "      <td>0.06444</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.2871</td>\n",
+       "      <td>0.07039</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>569 rows × 32 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
+       "0      842302         M        17.99         10.38          122.80     1001.0   \n",
+       "1      842517         M        20.57         17.77          132.90     1326.0   \n",
+       "2    84300903         M        19.69         21.25          130.00     1203.0   \n",
+       "3    84348301         M        11.42         20.38           77.58      386.1   \n",
+       "4    84358402         M        20.29         14.34          135.10     1297.0   \n",
+       "..        ...       ...          ...           ...             ...        ...   \n",
+       "564    926424         M        21.56         22.39          142.00     1479.0   \n",
+       "565    926682         M        20.13         28.25          131.20     1261.0   \n",
+       "566    926954         M        16.60         28.08          108.30      858.1   \n",
+       "567    927241         M        20.60         29.33          140.10     1265.0   \n",
+       "568     92751         B         7.76         24.54           47.92      181.0   \n",
+       "\n",
+       "     smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
+       "0            0.11840           0.27760         0.30010              0.14710   \n",
+       "1            0.08474           0.07864         0.08690              0.07017   \n",
+       "2            0.10960           0.15990         0.19740              0.12790   \n",
+       "3            0.14250           0.28390         0.24140              0.10520   \n",
+       "4            0.10030           0.13280         0.19800              0.10430   \n",
+       "..               ...               ...             ...                  ...   \n",
+       "564          0.11100           0.11590         0.24390              0.13890   \n",
+       "565          0.09780           0.10340         0.14400              0.09791   \n",
+       "566          0.08455           0.10230         0.09251              0.05302   \n",
+       "567          0.11780           0.27700         0.35140              0.15200   \n",
+       "568          0.05263           0.04362         0.00000              0.00000   \n",
+       "\n",
+       "     ...  radius_worst  texture_worst  perimeter_worst  area_worst  \\\n",
+       "0    ...        25.380          17.33           184.60      2019.0   \n",
+       "1    ...        24.990          23.41           158.80      1956.0   \n",
+       "2    ...        23.570          25.53           152.50      1709.0   \n",
+       "3    ...        14.910          26.50            98.87       567.7   \n",
+       "4    ...        22.540          16.67           152.20      1575.0   \n",
+       "..   ...           ...            ...              ...         ...   \n",
+       "564  ...        25.450          26.40           166.10      2027.0   \n",
+       "565  ...        23.690          38.25           155.00      1731.0   \n",
+       "566  ...        18.980          34.12           126.70      1124.0   \n",
+       "567  ...        25.740          39.42           184.60      1821.0   \n",
+       "568  ...         9.456          30.37            59.16       268.6   \n",
+       "\n",
+       "     smoothness_worst  compactness_worst  concavity_worst  \\\n",
+       "0             0.16220            0.66560           0.7119   \n",
+       "1             0.12380            0.18660           0.2416   \n",
+       "2             0.14440            0.42450           0.4504   \n",
+       "3             0.20980            0.86630           0.6869   \n",
+       "4             0.13740            0.20500           0.4000   \n",
+       "..                ...                ...              ...   \n",
+       "564           0.14100            0.21130           0.4107   \n",
+       "565           0.11660            0.19220           0.3215   \n",
+       "566           0.11390            0.30940           0.3403   \n",
+       "567           0.16500            0.86810           0.9387   \n",
+       "568           0.08996            0.06444           0.0000   \n",
+       "\n",
+       "     concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
+       "0                  0.2654          0.4601                  0.11890  \n",
+       "1                  0.1860          0.2750                  0.08902  \n",
+       "2                  0.2430          0.3613                  0.08758  \n",
+       "3                  0.2575          0.6638                  0.17300  \n",
+       "4                  0.1625          0.2364                  0.07678  \n",
+       "..                    ...             ...                      ...  \n",
+       "564                0.2216          0.2060                  0.07115  \n",
+       "565                0.1628          0.2572                  0.06637  \n",
+       "566                0.1418          0.2218                  0.07820  \n",
+       "567                0.2650          0.4087                  0.12400  \n",
+       "568                0.0000          0.2871                  0.07039  \n",
+       "\n",
+       "[569 rows x 32 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cancer = pd.read_csv('/Users/vincent/dsi_lcr/LCR/01_materials/notebooks/dataset/wdbc.csv')\n",
+    "cancer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "1a13f212",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
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+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>diagnosis</th>\n",
+       "      <th>radius_mean</th>\n",
+       "      <th>texture_mean</th>\n",
+       "      <th>perimeter_mean</th>\n",
+       "      <th>area_mean</th>\n",
+       "      <th>smoothness_mean</th>\n",
+       "      <th>compactness_mean</th>\n",
+       "      <th>concavity_mean</th>\n",
+       "      <th>concave points_mean</th>\n",
+       "      <th>...</th>\n",
+       "      <th>radius_worst</th>\n",
+       "      <th>texture_worst</th>\n",
+       "      <th>perimeter_worst</th>\n",
+       "      <th>area_worst</th>\n",
+       "      <th>smoothness_worst</th>\n",
+       "      <th>compactness_worst</th>\n",
+       "      <th>concavity_worst</th>\n",
+       "      <th>concave points_worst</th>\n",
+       "      <th>symmetry_worst</th>\n",
+       "      <th>fractal_dimension_worst</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>842302</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>17.99</td>\n",
+       "      <td>10.38</td>\n",
+       "      <td>122.80</td>\n",
+       "      <td>1001.0</td>\n",
+       "      <td>0.11840</td>\n",
+       "      <td>0.27760</td>\n",
+       "      <td>0.30010</td>\n",
+       "      <td>0.14710</td>\n",
+       "      <td>...</td>\n",
+       "      <td>25.380</td>\n",
+       "      <td>17.33</td>\n",
+       "      <td>184.60</td>\n",
+       "      <td>2019.0</td>\n",
+       "      <td>0.16220</td>\n",
+       "      <td>0.66560</td>\n",
+       "      <td>0.7119</td>\n",
+       "      <td>0.2654</td>\n",
+       "      <td>0.4601</td>\n",
+       "      <td>0.11890</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>842517</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>20.57</td>\n",
+       "      <td>17.77</td>\n",
+       "      <td>132.90</td>\n",
+       "      <td>1326.0</td>\n",
+       "      <td>0.08474</td>\n",
+       "      <td>0.07864</td>\n",
+       "      <td>0.08690</td>\n",
+       "      <td>0.07017</td>\n",
+       "      <td>...</td>\n",
+       "      <td>24.990</td>\n",
+       "      <td>23.41</td>\n",
+       "      <td>158.80</td>\n",
+       "      <td>1956.0</td>\n",
+       "      <td>0.12380</td>\n",
+       "      <td>0.18660</td>\n",
+       "      <td>0.2416</td>\n",
+       "      <td>0.1860</td>\n",
+       "      <td>0.2750</td>\n",
+       "      <td>0.08902</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>84300903</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>19.69</td>\n",
+       "      <td>21.25</td>\n",
+       "      <td>130.00</td>\n",
+       "      <td>1203.0</td>\n",
+       "      <td>0.10960</td>\n",
+       "      <td>0.15990</td>\n",
+       "      <td>0.19740</td>\n",
+       "      <td>0.12790</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23.570</td>\n",
+       "      <td>25.53</td>\n",
+       "      <td>152.50</td>\n",
+       "      <td>1709.0</td>\n",
+       "      <td>0.14440</td>\n",
+       "      <td>0.42450</td>\n",
+       "      <td>0.4504</td>\n",
+       "      <td>0.2430</td>\n",
+       "      <td>0.3613</td>\n",
+       "      <td>0.08758</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>84348301</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>11.42</td>\n",
+       "      <td>20.38</td>\n",
+       "      <td>77.58</td>\n",
+       "      <td>386.1</td>\n",
+       "      <td>0.14250</td>\n",
+       "      <td>0.28390</td>\n",
+       "      <td>0.24140</td>\n",
+       "      <td>0.10520</td>\n",
+       "      <td>...</td>\n",
+       "      <td>14.910</td>\n",
+       "      <td>26.50</td>\n",
+       "      <td>98.87</td>\n",
+       "      <td>567.7</td>\n",
+       "      <td>0.20980</td>\n",
+       "      <td>0.86630</td>\n",
+       "      <td>0.6869</td>\n",
+       "      <td>0.2575</td>\n",
+       "      <td>0.6638</td>\n",
+       "      <td>0.17300</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>84358402</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>20.29</td>\n",
+       "      <td>14.34</td>\n",
+       "      <td>135.10</td>\n",
+       "      <td>1297.0</td>\n",
+       "      <td>0.10030</td>\n",
+       "      <td>0.13280</td>\n",
+       "      <td>0.19800</td>\n",
+       "      <td>0.10430</td>\n",
+       "      <td>...</td>\n",
+       "      <td>22.540</td>\n",
+       "      <td>16.67</td>\n",
+       "      <td>152.20</td>\n",
+       "      <td>1575.0</td>\n",
+       "      <td>0.13740</td>\n",
+       "      <td>0.20500</td>\n",
+       "      <td>0.4000</td>\n",
+       "      <td>0.1625</td>\n",
+       "      <td>0.2364</td>\n",
+       "      <td>0.07678</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>564</th>\n",
+       "      <td>926424</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>21.56</td>\n",
+       "      <td>22.39</td>\n",
+       "      <td>142.00</td>\n",
+       "      <td>1479.0</td>\n",
+       "      <td>0.11100</td>\n",
+       "      <td>0.11590</td>\n",
+       "      <td>0.24390</td>\n",
+       "      <td>0.13890</td>\n",
+       "      <td>...</td>\n",
+       "      <td>25.450</td>\n",
+       "      <td>26.40</td>\n",
+       "      <td>166.10</td>\n",
+       "      <td>2027.0</td>\n",
+       "      <td>0.14100</td>\n",
+       "      <td>0.21130</td>\n",
+       "      <td>0.4107</td>\n",
+       "      <td>0.2216</td>\n",
+       "      <td>0.2060</td>\n",
+       "      <td>0.07115</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>565</th>\n",
+       "      <td>926682</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>20.13</td>\n",
+       "      <td>28.25</td>\n",
+       "      <td>131.20</td>\n",
+       "      <td>1261.0</td>\n",
+       "      <td>0.09780</td>\n",
+       "      <td>0.10340</td>\n",
+       "      <td>0.14400</td>\n",
+       "      <td>0.09791</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23.690</td>\n",
+       "      <td>38.25</td>\n",
+       "      <td>155.00</td>\n",
+       "      <td>1731.0</td>\n",
+       "      <td>0.11660</td>\n",
+       "      <td>0.19220</td>\n",
+       "      <td>0.3215</td>\n",
+       "      <td>0.1628</td>\n",
+       "      <td>0.2572</td>\n",
+       "      <td>0.06637</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>566</th>\n",
+       "      <td>926954</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>16.60</td>\n",
+       "      <td>28.08</td>\n",
+       "      <td>108.30</td>\n",
+       "      <td>858.1</td>\n",
+       "      <td>0.08455</td>\n",
+       "      <td>0.10230</td>\n",
+       "      <td>0.09251</td>\n",
+       "      <td>0.05302</td>\n",
+       "      <td>...</td>\n",
+       "      <td>18.980</td>\n",
+       "      <td>34.12</td>\n",
+       "      <td>126.70</td>\n",
+       "      <td>1124.0</td>\n",
+       "      <td>0.11390</td>\n",
+       "      <td>0.30940</td>\n",
+       "      <td>0.3403</td>\n",
+       "      <td>0.1418</td>\n",
+       "      <td>0.2218</td>\n",
+       "      <td>0.07820</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>567</th>\n",
+       "      <td>927241</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>20.60</td>\n",
+       "      <td>29.33</td>\n",
+       "      <td>140.10</td>\n",
+       "      <td>1265.0</td>\n",
+       "      <td>0.11780</td>\n",
+       "      <td>0.27700</td>\n",
+       "      <td>0.35140</td>\n",
+       "      <td>0.15200</td>\n",
+       "      <td>...</td>\n",
+       "      <td>25.740</td>\n",
+       "      <td>39.42</td>\n",
+       "      <td>184.60</td>\n",
+       "      <td>1821.0</td>\n",
+       "      <td>0.16500</td>\n",
+       "      <td>0.86810</td>\n",
+       "      <td>0.9387</td>\n",
+       "      <td>0.2650</td>\n",
+       "      <td>0.4087</td>\n",
+       "      <td>0.12400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>568</th>\n",
+       "      <td>92751</td>\n",
+       "      <td>Benign</td>\n",
+       "      <td>7.76</td>\n",
+       "      <td>24.54</td>\n",
+       "      <td>47.92</td>\n",
+       "      <td>181.0</td>\n",
+       "      <td>0.05263</td>\n",
+       "      <td>0.04362</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>9.456</td>\n",
+       "      <td>30.37</td>\n",
+       "      <td>59.16</td>\n",
+       "      <td>268.6</td>\n",
+       "      <td>0.08996</td>\n",
+       "      <td>0.06444</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.2871</td>\n",
+       "      <td>0.07039</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>569 rows × 32 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           id  diagnosis  radius_mean  texture_mean  perimeter_mean  \\\n",
+       "0      842302  Malignant        17.99         10.38          122.80   \n",
+       "1      842517  Malignant        20.57         17.77          132.90   \n",
+       "2    84300903  Malignant        19.69         21.25          130.00   \n",
+       "3    84348301  Malignant        11.42         20.38           77.58   \n",
+       "4    84358402  Malignant        20.29         14.34          135.10   \n",
+       "..        ...        ...          ...           ...             ...   \n",
+       "564    926424  Malignant        21.56         22.39          142.00   \n",
+       "565    926682  Malignant        20.13         28.25          131.20   \n",
+       "566    926954  Malignant        16.60         28.08          108.30   \n",
+       "567    927241  Malignant        20.60         29.33          140.10   \n",
+       "568     92751     Benign         7.76         24.54           47.92   \n",
+       "\n",
+       "     area_mean  smoothness_mean  compactness_mean  concavity_mean  \\\n",
+       "0       1001.0          0.11840           0.27760         0.30010   \n",
+       "1       1326.0          0.08474           0.07864         0.08690   \n",
+       "2       1203.0          0.10960           0.15990         0.19740   \n",
+       "3        386.1          0.14250           0.28390         0.24140   \n",
+       "4       1297.0          0.10030           0.13280         0.19800   \n",
+       "..         ...              ...               ...             ...   \n",
+       "564     1479.0          0.11100           0.11590         0.24390   \n",
+       "565     1261.0          0.09780           0.10340         0.14400   \n",
+       "566      858.1          0.08455           0.10230         0.09251   \n",
+       "567     1265.0          0.11780           0.27700         0.35140   \n",
+       "568      181.0          0.05263           0.04362         0.00000   \n",
+       "\n",
+       "     concave points_mean  ...  radius_worst  texture_worst  perimeter_worst  \\\n",
+       "0                0.14710  ...        25.380          17.33           184.60   \n",
+       "1                0.07017  ...        24.990          23.41           158.80   \n",
+       "2                0.12790  ...        23.570          25.53           152.50   \n",
+       "3                0.10520  ...        14.910          26.50            98.87   \n",
+       "4                0.10430  ...        22.540          16.67           152.20   \n",
+       "..                   ...  ...           ...            ...              ...   \n",
+       "564              0.13890  ...        25.450          26.40           166.10   \n",
+       "565              0.09791  ...        23.690          38.25           155.00   \n",
+       "566              0.05302  ...        18.980          34.12           126.70   \n",
+       "567              0.15200  ...        25.740          39.42           184.60   \n",
+       "568              0.00000  ...         9.456          30.37            59.16   \n",
+       "\n",
+       "     area_worst  smoothness_worst  compactness_worst  concavity_worst  \\\n",
+       "0        2019.0           0.16220            0.66560           0.7119   \n",
+       "1        1956.0           0.12380            0.18660           0.2416   \n",
+       "2        1709.0           0.14440            0.42450           0.4504   \n",
+       "3         567.7           0.20980            0.86630           0.6869   \n",
+       "4        1575.0           0.13740            0.20500           0.4000   \n",
+       "..          ...               ...                ...              ...   \n",
+       "564      2027.0           0.14100            0.21130           0.4107   \n",
+       "565      1731.0           0.11660            0.19220           0.3215   \n",
+       "566      1124.0           0.11390            0.30940           0.3403   \n",
+       "567      1821.0           0.16500            0.86810           0.9387   \n",
+       "568       268.6           0.08996            0.06444           0.0000   \n",
+       "\n",
+       "     concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
+       "0                  0.2654          0.4601                  0.11890  \n",
+       "1                  0.1860          0.2750                  0.08902  \n",
+       "2                  0.2430          0.3613                  0.08758  \n",
+       "3                  0.2575          0.6638                  0.17300  \n",
+       "4                  0.1625          0.2364                  0.07678  \n",
+       "..                    ...             ...                      ...  \n",
+       "564                0.2216          0.2060                  0.07115  \n",
+       "565                0.1628          0.2572                  0.06637  \n",
+       "566                0.1418          0.2218                  0.07820  \n",
+       "567                0.2650          0.4087                  0.12400  \n",
+       "568                0.0000          0.2871                  0.07039  \n",
+       "\n",
+       "[569 rows x 32 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cancer['diagnosis'] = cancer['diagnosis'].replace({\n",
+    "    \"M\": \"Malignant\",\n",
+    "    \"B\" :\"Benign\"\n",
+    "})\n",
+    "\n",
+    "cancer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8d2ae588",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "standardized_cancer = cancer.copy() #Creating a copy of the original dataframe\n",
+    "\n",
+    "#Exclude the ID and diagnosis columns from standardization\n",
+    "columns_to_exclude = ['id', 'diagnosis']\n",
+    "\n",
+    "#Selecting columns to standardize \n",
+    "columns_to_scale = standardized_cancer.columns.difference(columns_to_exclude) \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "eb9a976a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>diagnosis</th>\n",
+       "      <th>radius_mean</th>\n",
+       "      <th>texture_mean</th>\n",
+       "      <th>perimeter_mean</th>\n",
+       "      <th>area_mean</th>\n",
+       "      <th>smoothness_mean</th>\n",
+       "      <th>compactness_mean</th>\n",
+       "      <th>concavity_mean</th>\n",
+       "      <th>concave points_mean</th>\n",
+       "      <th>...</th>\n",
+       "      <th>radius_worst</th>\n",
+       "      <th>texture_worst</th>\n",
+       "      <th>perimeter_worst</th>\n",
+       "      <th>area_worst</th>\n",
+       "      <th>smoothness_worst</th>\n",
+       "      <th>compactness_worst</th>\n",
+       "      <th>concavity_worst</th>\n",
+       "      <th>concave points_worst</th>\n",
+       "      <th>symmetry_worst</th>\n",
+       "      <th>fractal_dimension_worst</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>842302</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>1.097064</td>\n",
+       "      <td>-2.073335</td>\n",
+       "      <td>1.269934</td>\n",
+       "      <td>0.984375</td>\n",
+       "      <td>1.568466</td>\n",
+       "      <td>3.283515</td>\n",
+       "      <td>2.652874</td>\n",
+       "      <td>2.532475</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.886690</td>\n",
+       "      <td>-1.359293</td>\n",
+       "      <td>2.303601</td>\n",
+       "      <td>2.001237</td>\n",
+       "      <td>1.307686</td>\n",
+       "      <td>2.616665</td>\n",
+       "      <td>2.109526</td>\n",
+       "      <td>2.296076</td>\n",
+       "      <td>2.750622</td>\n",
+       "      <td>1.937015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>842517</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>1.829821</td>\n",
+       "      <td>-0.353632</td>\n",
+       "      <td>1.685955</td>\n",
+       "      <td>1.908708</td>\n",
+       "      <td>-0.826962</td>\n",
+       "      <td>-0.487072</td>\n",
+       "      <td>-0.023846</td>\n",
+       "      <td>0.548144</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.805927</td>\n",
+       "      <td>-0.369203</td>\n",
+       "      <td>1.535126</td>\n",
+       "      <td>1.890489</td>\n",
+       "      <td>-0.375612</td>\n",
+       "      <td>-0.430444</td>\n",
+       "      <td>-0.146749</td>\n",
+       "      <td>1.087084</td>\n",
+       "      <td>-0.243890</td>\n",
+       "      <td>0.281190</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>84300903</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>1.579888</td>\n",
+       "      <td>0.456187</td>\n",
+       "      <td>1.566503</td>\n",
+       "      <td>1.558884</td>\n",
+       "      <td>0.942210</td>\n",
+       "      <td>1.052926</td>\n",
+       "      <td>1.363478</td>\n",
+       "      <td>2.037231</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.511870</td>\n",
+       "      <td>-0.023974</td>\n",
+       "      <td>1.347475</td>\n",
+       "      <td>1.456285</td>\n",
+       "      <td>0.527407</td>\n",
+       "      <td>1.082932</td>\n",
+       "      <td>0.854974</td>\n",
+       "      <td>1.955000</td>\n",
+       "      <td>1.152255</td>\n",
+       "      <td>0.201391</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>84348301</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>-0.768909</td>\n",
+       "      <td>0.253732</td>\n",
+       "      <td>-0.592687</td>\n",
+       "      <td>-0.764464</td>\n",
+       "      <td>3.283553</td>\n",
+       "      <td>3.402909</td>\n",
+       "      <td>1.915897</td>\n",
+       "      <td>1.451707</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.281464</td>\n",
+       "      <td>0.133984</td>\n",
+       "      <td>-0.249939</td>\n",
+       "      <td>-0.550021</td>\n",
+       "      <td>3.394275</td>\n",
+       "      <td>3.893397</td>\n",
+       "      <td>1.989588</td>\n",
+       "      <td>2.175786</td>\n",
+       "      <td>6.046041</td>\n",
+       "      <td>4.935010</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>84358402</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>1.750297</td>\n",
+       "      <td>-1.151816</td>\n",
+       "      <td>1.776573</td>\n",
+       "      <td>1.826229</td>\n",
+       "      <td>0.280372</td>\n",
+       "      <td>0.539340</td>\n",
+       "      <td>1.371011</td>\n",
+       "      <td>1.428493</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.298575</td>\n",
+       "      <td>-1.466770</td>\n",
+       "      <td>1.338539</td>\n",
+       "      <td>1.220724</td>\n",
+       "      <td>0.220556</td>\n",
+       "      <td>-0.313395</td>\n",
+       "      <td>0.613179</td>\n",
+       "      <td>0.729259</td>\n",
+       "      <td>-0.868353</td>\n",
+       "      <td>-0.397100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>564</th>\n",
+       "      <td>926424</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>2.110995</td>\n",
+       "      <td>0.721473</td>\n",
+       "      <td>2.060786</td>\n",
+       "      <td>2.343856</td>\n",
+       "      <td>1.041842</td>\n",
+       "      <td>0.219060</td>\n",
+       "      <td>1.947285</td>\n",
+       "      <td>2.320965</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.901185</td>\n",
+       "      <td>0.117700</td>\n",
+       "      <td>1.752563</td>\n",
+       "      <td>2.015301</td>\n",
+       "      <td>0.378365</td>\n",
+       "      <td>-0.273318</td>\n",
+       "      <td>0.664512</td>\n",
+       "      <td>1.629151</td>\n",
+       "      <td>-1.360158</td>\n",
+       "      <td>-0.709091</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>565</th>\n",
+       "      <td>926682</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>1.704854</td>\n",
+       "      <td>2.085134</td>\n",
+       "      <td>1.615931</td>\n",
+       "      <td>1.723842</td>\n",
+       "      <td>0.102458</td>\n",
+       "      <td>-0.017833</td>\n",
+       "      <td>0.693043</td>\n",
+       "      <td>1.263669</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.536720</td>\n",
+       "      <td>2.047399</td>\n",
+       "      <td>1.421940</td>\n",
+       "      <td>1.494959</td>\n",
+       "      <td>-0.691230</td>\n",
+       "      <td>-0.394820</td>\n",
+       "      <td>0.236573</td>\n",
+       "      <td>0.733827</td>\n",
+       "      <td>-0.531855</td>\n",
+       "      <td>-0.973978</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>566</th>\n",
+       "      <td>926954</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>0.702284</td>\n",
+       "      <td>2.045574</td>\n",
+       "      <td>0.672676</td>\n",
+       "      <td>0.577953</td>\n",
+       "      <td>-0.840484</td>\n",
+       "      <td>-0.038680</td>\n",
+       "      <td>0.046588</td>\n",
+       "      <td>0.105777</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.561361</td>\n",
+       "      <td>1.374854</td>\n",
+       "      <td>0.579001</td>\n",
+       "      <td>0.427906</td>\n",
+       "      <td>-0.809587</td>\n",
+       "      <td>0.350735</td>\n",
+       "      <td>0.326767</td>\n",
+       "      <td>0.414069</td>\n",
+       "      <td>-1.104549</td>\n",
+       "      <td>-0.318409</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>567</th>\n",
+       "      <td>927241</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>1.838341</td>\n",
+       "      <td>2.336457</td>\n",
+       "      <td>1.982524</td>\n",
+       "      <td>1.735218</td>\n",
+       "      <td>1.525767</td>\n",
+       "      <td>3.272144</td>\n",
+       "      <td>3.296944</td>\n",
+       "      <td>2.658866</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.961239</td>\n",
+       "      <td>2.237926</td>\n",
+       "      <td>2.303601</td>\n",
+       "      <td>1.653171</td>\n",
+       "      <td>1.430427</td>\n",
+       "      <td>3.904848</td>\n",
+       "      <td>3.197605</td>\n",
+       "      <td>2.289985</td>\n",
+       "      <td>1.919083</td>\n",
+       "      <td>2.219635</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>568</th>\n",
+       "      <td>92751</td>\n",
+       "      <td>Benign</td>\n",
+       "      <td>-1.808401</td>\n",
+       "      <td>1.221792</td>\n",
+       "      <td>-1.814389</td>\n",
+       "      <td>-1.347789</td>\n",
+       "      <td>-3.112085</td>\n",
+       "      <td>-1.150752</td>\n",
+       "      <td>-1.114873</td>\n",
+       "      <td>-1.261820</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-1.410893</td>\n",
+       "      <td>0.764190</td>\n",
+       "      <td>-1.432735</td>\n",
+       "      <td>-1.075813</td>\n",
+       "      <td>-1.859019</td>\n",
+       "      <td>-1.207552</td>\n",
+       "      <td>-1.305831</td>\n",
+       "      <td>-1.745063</td>\n",
+       "      <td>-0.048138</td>\n",
+       "      <td>-0.751207</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>569 rows × 32 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           id  diagnosis  radius_mean  texture_mean  perimeter_mean  \\\n",
+       "0      842302  Malignant     1.097064     -2.073335        1.269934   \n",
+       "1      842517  Malignant     1.829821     -0.353632        1.685955   \n",
+       "2    84300903  Malignant     1.579888      0.456187        1.566503   \n",
+       "3    84348301  Malignant    -0.768909      0.253732       -0.592687   \n",
+       "4    84358402  Malignant     1.750297     -1.151816        1.776573   \n",
+       "..        ...        ...          ...           ...             ...   \n",
+       "564    926424  Malignant     2.110995      0.721473        2.060786   \n",
+       "565    926682  Malignant     1.704854      2.085134        1.615931   \n",
+       "566    926954  Malignant     0.702284      2.045574        0.672676   \n",
+       "567    927241  Malignant     1.838341      2.336457        1.982524   \n",
+       "568     92751     Benign    -1.808401      1.221792       -1.814389   \n",
+       "\n",
+       "     area_mean  smoothness_mean  compactness_mean  concavity_mean  \\\n",
+       "0     0.984375         1.568466          3.283515        2.652874   \n",
+       "1     1.908708        -0.826962         -0.487072       -0.023846   \n",
+       "2     1.558884         0.942210          1.052926        1.363478   \n",
+       "3    -0.764464         3.283553          3.402909        1.915897   \n",
+       "4     1.826229         0.280372          0.539340        1.371011   \n",
+       "..         ...              ...               ...             ...   \n",
+       "564   2.343856         1.041842          0.219060        1.947285   \n",
+       "565   1.723842         0.102458         -0.017833        0.693043   \n",
+       "566   0.577953        -0.840484         -0.038680        0.046588   \n",
+       "567   1.735218         1.525767          3.272144        3.296944   \n",
+       "568  -1.347789        -3.112085         -1.150752       -1.114873   \n",
+       "\n",
+       "     concave points_mean  ...  radius_worst  texture_worst  perimeter_worst  \\\n",
+       "0               2.532475  ...      1.886690      -1.359293         2.303601   \n",
+       "1               0.548144  ...      1.805927      -0.369203         1.535126   \n",
+       "2               2.037231  ...      1.511870      -0.023974         1.347475   \n",
+       "3               1.451707  ...     -0.281464       0.133984        -0.249939   \n",
+       "4               1.428493  ...      1.298575      -1.466770         1.338539   \n",
+       "..                   ...  ...           ...            ...              ...   \n",
+       "564             2.320965  ...      1.901185       0.117700         1.752563   \n",
+       "565             1.263669  ...      1.536720       2.047399         1.421940   \n",
+       "566             0.105777  ...      0.561361       1.374854         0.579001   \n",
+       "567             2.658866  ...      1.961239       2.237926         2.303601   \n",
+       "568            -1.261820  ...     -1.410893       0.764190        -1.432735   \n",
+       "\n",
+       "     area_worst  smoothness_worst  compactness_worst  concavity_worst  \\\n",
+       "0      2.001237          1.307686           2.616665         2.109526   \n",
+       "1      1.890489         -0.375612          -0.430444        -0.146749   \n",
+       "2      1.456285          0.527407           1.082932         0.854974   \n",
+       "3     -0.550021          3.394275           3.893397         1.989588   \n",
+       "4      1.220724          0.220556          -0.313395         0.613179   \n",
+       "..          ...               ...                ...              ...   \n",
+       "564    2.015301          0.378365          -0.273318         0.664512   \n",
+       "565    1.494959         -0.691230          -0.394820         0.236573   \n",
+       "566    0.427906         -0.809587           0.350735         0.326767   \n",
+       "567    1.653171          1.430427           3.904848         3.197605   \n",
+       "568   -1.075813         -1.859019          -1.207552        -1.305831   \n",
+       "\n",
+       "     concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
+       "0                2.296076        2.750622                 1.937015  \n",
+       "1                1.087084       -0.243890                 0.281190  \n",
+       "2                1.955000        1.152255                 0.201391  \n",
+       "3                2.175786        6.046041                 4.935010  \n",
+       "4                0.729259       -0.868353                -0.397100  \n",
+       "..                    ...             ...                      ...  \n",
+       "564              1.629151       -1.360158                -0.709091  \n",
+       "565              0.733827       -0.531855                -0.973978  \n",
+       "566              0.414069       -1.104549                -0.318409  \n",
+       "567              2.289985        1.919083                 2.219635  \n",
+       "568             -1.745063       -0.048138                -0.751207  \n",
+       "\n",
+       "[569 rows x 32 columns]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scaler = StandardScaler()\n",
+    "standardized_cancer[columns_to_scale] = scaler.fit_transform(standardized_cancer[columns_to_scale])\n",
+    "standardized_cancer\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "790e5afe",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>diagnosis</th>\n",
+       "      <th>radius_mean</th>\n",
+       "      <th>texture_mean</th>\n",
+       "      <th>perimeter_mean</th>\n",
+       "      <th>area_mean</th>\n",
+       "      <th>smoothness_mean</th>\n",
+       "      <th>compactness_mean</th>\n",
+       "      <th>concavity_mean</th>\n",
+       "      <th>concave points_mean</th>\n",
+       "      <th>...</th>\n",
+       "      <th>radius_worst</th>\n",
+       "      <th>texture_worst</th>\n",
+       "      <th>perimeter_worst</th>\n",
+       "      <th>area_worst</th>\n",
+       "      <th>smoothness_worst</th>\n",
+       "      <th>compactness_worst</th>\n",
+       "      <th>concavity_worst</th>\n",
+       "      <th>concave points_worst</th>\n",
+       "      <th>symmetry_worst</th>\n",
+       "      <th>fractal_dimension_worst</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>164</th>\n",
+       "      <td>8712289</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>2.596659</td>\n",
+       "      <td>0.640025</td>\n",
+       "      <td>2.476807</td>\n",
+       "      <td>2.932585</td>\n",
+       "      <td>-0.851870</td>\n",
+       "      <td>0.192528</td>\n",
+       "      <td>0.547405</td>\n",
+       "      <td>1.240713</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.431317</td>\n",
+       "      <td>0.414075</td>\n",
+       "      <td>2.291686</td>\n",
+       "      <td>2.676276</td>\n",
+       "      <td>-0.419448</td>\n",
+       "      <td>0.661808</td>\n",
+       "      <td>0.588232</td>\n",
+       "      <td>1.827097</td>\n",
+       "      <td>1.113428</td>\n",
+       "      <td>0.439125</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>852973</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>0.333066</td>\n",
+       "      <td>1.391668</td>\n",
+       "      <td>0.429654</td>\n",
+       "      <td>0.220449</td>\n",
+       "      <td>0.842579</td>\n",
+       "      <td>1.238650</td>\n",
+       "      <td>0.998129</td>\n",
+       "      <td>0.995412</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.828498</td>\n",
+       "      <td>1.796619</td>\n",
+       "      <td>1.252161</td>\n",
+       "      <td>0.682803</td>\n",
+       "      <td>1.390974</td>\n",
+       "      <td>2.269333</td>\n",
+       "      <td>1.733401</td>\n",
+       "      <td>1.336800</td>\n",
+       "      <td>1.822016</td>\n",
+       "      <td>0.820940</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>378</th>\n",
+       "      <td>9013594</td>\n",
+       "      <td>Benign</td>\n",
+       "      <td>-0.132717</td>\n",
+       "      <td>-0.963324</td>\n",
+       "      <td>-0.152364</td>\n",
+       "      <td>-0.211286</td>\n",
+       "      <td>-0.973563</td>\n",
+       "      <td>-0.546958</td>\n",
+       "      <td>-0.581412</td>\n",
+       "      <td>-0.624450</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.358085</td>\n",
+       "      <td>-0.983124</td>\n",
+       "      <td>-0.277044</td>\n",
+       "      <td>-0.393040</td>\n",
+       "      <td>-0.213419</td>\n",
+       "      <td>0.357097</td>\n",
+       "      <td>-0.073347</td>\n",
+       "      <td>-0.140179</td>\n",
+       "      <td>0.786637</td>\n",
+       "      <td>0.689050</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>131</th>\n",
+       "      <td>8670</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>0.378508</td>\n",
+       "      <td>0.044296</td>\n",
+       "      <td>0.400820</td>\n",
+       "      <td>0.267377</td>\n",
+       "      <td>0.913744</td>\n",
+       "      <td>0.340350</td>\n",
+       "      <td>0.725686</td>\n",
+       "      <td>0.824140</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.619345</td>\n",
+       "      <td>0.052562</td>\n",
+       "      <td>0.525386</td>\n",
+       "      <td>0.484159</td>\n",
+       "      <td>0.974533</td>\n",
+       "      <td>-0.094562</td>\n",
+       "      <td>0.512911</td>\n",
+       "      <td>0.560244</td>\n",
+       "      <td>-0.103143</td>\n",
+       "      <td>-0.208132</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>233</th>\n",
+       "      <td>88206102</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>1.812780</td>\n",
+       "      <td>1.982743</td>\n",
+       "      <td>1.747740</td>\n",
+       "      <td>1.888800</td>\n",
+       "      <td>-0.339479</td>\n",
+       "      <td>0.057973</td>\n",
+       "      <td>0.836170</td>\n",
+       "      <td>0.889399</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.698245</td>\n",
+       "      <td>1.905725</td>\n",
+       "      <td>1.651291</td>\n",
+       "      <td>1.742824</td>\n",
+       "      <td>-0.441366</td>\n",
+       "      <td>0.138901</td>\n",
+       "      <td>0.683223</td>\n",
+       "      <td>0.634854</td>\n",
+       "      <td>-0.750255</td>\n",
+       "      <td>-0.036897</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>360</th>\n",
+       "      <td>901034302</td>\n",
+       "      <td>Benign</td>\n",
+       "      <td>-0.450813</td>\n",
+       "      <td>-0.283820</td>\n",
+       "      <td>-0.516897</td>\n",
+       "      <td>-0.463558</td>\n",
+       "      <td>-1.565660</td>\n",
+       "      <td>-1.475202</td>\n",
+       "      <td>-1.099882</td>\n",
+       "      <td>-1.121268</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.527893</td>\n",
+       "      <td>-0.764914</td>\n",
+       "      <td>-0.608859</td>\n",
+       "      <td>-0.518379</td>\n",
+       "      <td>-1.728826</td>\n",
+       "      <td>-1.342223</td>\n",
+       "      <td>-1.288651</td>\n",
+       "      <td>-1.496108</td>\n",
+       "      <td>-1.080282</td>\n",
+       "      <td>-1.592419</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>301</th>\n",
+       "      <td>892604</td>\n",
+       "      <td>Benign</td>\n",
+       "      <td>-0.473535</td>\n",
+       "      <td>0.139706</td>\n",
+       "      <td>-0.475295</td>\n",
+       "      <td>-0.522146</td>\n",
+       "      <td>-0.843330</td>\n",
+       "      <td>-0.055736</td>\n",
+       "      <td>-0.257368</td>\n",
+       "      <td>-0.462464</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.581734</td>\n",
+       "      <td>-0.424570</td>\n",
+       "      <td>-0.569839</td>\n",
+       "      <td>-0.578851</td>\n",
+       "      <td>-1.199727</td>\n",
+       "      <td>-0.244691</td>\n",
+       "      <td>-0.392382</td>\n",
+       "      <td>-0.584035</td>\n",
+       "      <td>-0.349045</td>\n",
+       "      <td>-0.349442</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>406</th>\n",
+       "      <td>905189</td>\n",
+       "      <td>Benign</td>\n",
+       "      <td>0.571638</td>\n",
+       "      <td>-1.030809</td>\n",
+       "      <td>0.507915</td>\n",
+       "      <td>0.412710</td>\n",
+       "      <td>-0.100363</td>\n",
+       "      <td>-0.366351</td>\n",
+       "      <td>-0.424349</td>\n",
+       "      <td>-0.093868</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.298367</td>\n",
+       "      <td>-0.992895</td>\n",
+       "      <td>0.257314</td>\n",
+       "      <td>0.118337</td>\n",
+       "      <td>-0.515887</td>\n",
+       "      <td>-0.522048</td>\n",
+       "      <td>-0.197603</td>\n",
+       "      <td>-0.025980</td>\n",
+       "      <td>-0.198592</td>\n",
+       "      <td>-0.766169</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>852781</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>1.273153</td>\n",
+       "      <td>0.223480</td>\n",
+       "      <td>1.241101</td>\n",
+       "      <td>1.248876</td>\n",
+       "      <td>-0.139504</td>\n",
+       "      <td>0.042812</td>\n",
+       "      <td>0.755818</td>\n",
+       "      <td>0.732313</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.043864</td>\n",
+       "      <td>0.257745</td>\n",
+       "      <td>0.972174</td>\n",
+       "      <td>0.918363</td>\n",
+       "      <td>0.062747</td>\n",
+       "      <td>-0.270773</td>\n",
+       "      <td>0.347396</td>\n",
+       "      <td>0.523700</td>\n",
+       "      <td>-0.905562</td>\n",
+       "      <td>-0.539518</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>284</th>\n",
+       "      <td>8912284</td>\n",
+       "      <td>Benign</td>\n",
+       "      <td>-0.351408</td>\n",
+       "      <td>-0.835335</td>\n",
+       "      <td>-0.324951</td>\n",
+       "      <td>-0.393308</td>\n",
+       "      <td>-1.293808</td>\n",
+       "      <td>-0.161864</td>\n",
+       "      <td>0.285006</td>\n",
+       "      <td>-0.387404</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.490618</td>\n",
+       "      <td>-0.974982</td>\n",
+       "      <td>-0.450994</td>\n",
+       "      <td>-0.500975</td>\n",
+       "      <td>-1.451345</td>\n",
+       "      <td>-0.143545</td>\n",
+       "      <td>0.298461</td>\n",
+       "      <td>-0.196518</td>\n",
+       "      <td>-1.458843</td>\n",
+       "      <td>-0.702441</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>426 rows × 32 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            id  diagnosis  radius_mean  texture_mean  perimeter_mean  \\\n",
+       "164    8712289  Malignant     2.596659      0.640025        2.476807   \n",
+       "28      852973  Malignant     0.333066      1.391668        0.429654   \n",
+       "378    9013594     Benign    -0.132717     -0.963324       -0.152364   \n",
+       "131       8670  Malignant     0.378508      0.044296        0.400820   \n",
+       "233   88206102  Malignant     1.812780      1.982743        1.747740   \n",
+       "..         ...        ...          ...           ...             ...   \n",
+       "360  901034302     Benign    -0.450813     -0.283820       -0.516897   \n",
+       "301     892604     Benign    -0.473535      0.139706       -0.475295   \n",
+       "406     905189     Benign     0.571638     -1.030809        0.507915   \n",
+       "27      852781  Malignant     1.273153      0.223480        1.241101   \n",
+       "284    8912284     Benign    -0.351408     -0.835335       -0.324951   \n",
+       "\n",
+       "     area_mean  smoothness_mean  compactness_mean  concavity_mean  \\\n",
+       "164   2.932585        -0.851870          0.192528        0.547405   \n",
+       "28    0.220449         0.842579          1.238650        0.998129   \n",
+       "378  -0.211286        -0.973563         -0.546958       -0.581412   \n",
+       "131   0.267377         0.913744          0.340350        0.725686   \n",
+       "233   1.888800        -0.339479          0.057973        0.836170   \n",
+       "..         ...              ...               ...             ...   \n",
+       "360  -0.463558        -1.565660         -1.475202       -1.099882   \n",
+       "301  -0.522146        -0.843330         -0.055736       -0.257368   \n",
+       "406   0.412710        -0.100363         -0.366351       -0.424349   \n",
+       "27    1.248876        -0.139504          0.042812        0.755818   \n",
+       "284  -0.393308        -1.293808         -0.161864        0.285006   \n",
+       "\n",
+       "     concave points_mean  ...  radius_worst  texture_worst  perimeter_worst  \\\n",
+       "164             1.240713  ...      2.431317       0.414075         2.291686   \n",
+       "28              0.995412  ...      0.828498       1.796619         1.252161   \n",
+       "378            -0.624450  ...     -0.358085      -0.983124        -0.277044   \n",
+       "131             0.824140  ...      0.619345       0.052562         0.525386   \n",
+       "233             0.889399  ...      1.698245       1.905725         1.651291   \n",
+       "..                   ...  ...           ...            ...              ...   \n",
+       "360            -1.121268  ...     -0.527893      -0.764914        -0.608859   \n",
+       "301            -0.462464  ...     -0.581734      -0.424570        -0.569839   \n",
+       "406            -0.093868  ...      0.298367      -0.992895         0.257314   \n",
+       "27              0.732313  ...      1.043864       0.257745         0.972174   \n",
+       "284            -0.387404  ...     -0.490618      -0.974982        -0.450994   \n",
+       "\n",
+       "     area_worst  smoothness_worst  compactness_worst  concavity_worst  \\\n",
+       "164    2.676276         -0.419448           0.661808         0.588232   \n",
+       "28     0.682803          1.390974           2.269333         1.733401   \n",
+       "378   -0.393040         -0.213419           0.357097        -0.073347   \n",
+       "131    0.484159          0.974533          -0.094562         0.512911   \n",
+       "233    1.742824         -0.441366           0.138901         0.683223   \n",
+       "..          ...               ...                ...              ...   \n",
+       "360   -0.518379         -1.728826          -1.342223        -1.288651   \n",
+       "301   -0.578851         -1.199727          -0.244691        -0.392382   \n",
+       "406    0.118337         -0.515887          -0.522048        -0.197603   \n",
+       "27     0.918363          0.062747          -0.270773         0.347396   \n",
+       "284   -0.500975         -1.451345          -0.143545         0.298461   \n",
+       "\n",
+       "     concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
+       "164              1.827097        1.113428                 0.439125  \n",
+       "28               1.336800        1.822016                 0.820940  \n",
+       "378             -0.140179        0.786637                 0.689050  \n",
+       "131              0.560244       -0.103143                -0.208132  \n",
+       "233              0.634854       -0.750255                -0.036897  \n",
+       "..                    ...             ...                      ...  \n",
+       "360             -1.496108       -1.080282                -1.592419  \n",
+       "301             -0.584035       -0.349045                -0.349442  \n",
+       "406             -0.025980       -0.198592                -0.766169  \n",
+       "27               0.523700       -0.905562                -0.539518  \n",
+       "284             -0.196518       -1.458843                -0.702441  \n",
+       "\n",
+       "[426 rows x 32 columns]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Set the random seed\n",
+    "np.random.seed(1)\n",
+    "\n",
+    "# Split the data into a training and testing set. \n",
+    "# Stratify to ensures that the class distribution (benign vs malignant) \n",
+    "cancer_train, cancer_test = train_test_split(\n",
+    "    standardized_cancer, train_size=0.75, shuffle=True, stratify=standardized_cancer['diagnosis']\n",
+    ")\n",
+    "\n",
+    "cancer_train\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "2e235ee6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 426 entries, 164 to 284\n",
+      "Data columns (total 32 columns):\n",
+      " #   Column                   Non-Null Count  Dtype  \n",
+      "---  ------                   --------------  -----  \n",
+      " 0   id                       426 non-null    int64  \n",
+      " 1   diagnosis                426 non-null    object \n",
+      " 2   radius_mean              426 non-null    float64\n",
+      " 3   texture_mean             426 non-null    float64\n",
+      " 4   perimeter_mean           426 non-null    float64\n",
+      " 5   area_mean                426 non-null    float64\n",
+      " 6   smoothness_mean          426 non-null    float64\n",
+      " 7   compactness_mean         426 non-null    float64\n",
+      " 8   concavity_mean           426 non-null    float64\n",
+      " 9   concave points_mean      426 non-null    float64\n",
+      " 10  symmetry_mean            426 non-null    float64\n",
+      " 11  fractal_dimension_mean   426 non-null    float64\n",
+      " 12  radius_se                426 non-null    float64\n",
+      " 13  texture_se               426 non-null    float64\n",
+      " 14  perimeter_se             426 non-null    float64\n",
+      " 15  area_se                  426 non-null    float64\n",
+      " 16  smoothness_se            426 non-null    float64\n",
+      " 17  compactness_se           426 non-null    float64\n",
+      " 18  concavity_se             426 non-null    float64\n",
+      " 19  concave points_se        426 non-null    float64\n",
+      " 20  symmetry_se              426 non-null    float64\n",
+      " 21  fractal_dimension_se     426 non-null    float64\n",
+      " 22  radius_worst             426 non-null    float64\n",
+      " 23  texture_worst            426 non-null    float64\n",
+      " 24  perimeter_worst          426 non-null    float64\n",
+      " 25  area_worst               426 non-null    float64\n",
+      " 26  smoothness_worst         426 non-null    float64\n",
+      " 27  compactness_worst        426 non-null    float64\n",
+      " 28  concavity_worst          426 non-null    float64\n",
+      " 29  concave points_worst     426 non-null    float64\n",
+      " 30  symmetry_worst           426 non-null    float64\n",
+      " 31  fractal_dimension_worst  426 non-null    float64\n",
+      "dtypes: float64(30), int64(1), object(1)\n",
+      "memory usage: 109.8+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "cancer_train.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "59004d28",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 143 entries, 357 to 332\n",
+      "Data columns (total 32 columns):\n",
+      " #   Column                   Non-Null Count  Dtype  \n",
+      "---  ------                   --------------  -----  \n",
+      " 0   id                       143 non-null    int64  \n",
+      " 1   diagnosis                143 non-null    object \n",
+      " 2   radius_mean              143 non-null    float64\n",
+      " 3   texture_mean             143 non-null    float64\n",
+      " 4   perimeter_mean           143 non-null    float64\n",
+      " 5   area_mean                143 non-null    float64\n",
+      " 6   smoothness_mean          143 non-null    float64\n",
+      " 7   compactness_mean         143 non-null    float64\n",
+      " 8   concavity_mean           143 non-null    float64\n",
+      " 9   concave points_mean      143 non-null    float64\n",
+      " 10  symmetry_mean            143 non-null    float64\n",
+      " 11  fractal_dimension_mean   143 non-null    float64\n",
+      " 12  radius_se                143 non-null    float64\n",
+      " 13  texture_se               143 non-null    float64\n",
+      " 14  perimeter_se             143 non-null    float64\n",
+      " 15  area_se                  143 non-null    float64\n",
+      " 16  smoothness_se            143 non-null    float64\n",
+      " 17  compactness_se           143 non-null    float64\n",
+      " 18  concavity_se             143 non-null    float64\n",
+      " 19  concave points_se        143 non-null    float64\n",
+      " 20  symmetry_se              143 non-null    float64\n",
+      " 21  fractal_dimension_se     143 non-null    float64\n",
+      " 22  radius_worst             143 non-null    float64\n",
+      " 23  texture_worst            143 non-null    float64\n",
+      " 24  perimeter_worst          143 non-null    float64\n",
+      " 25  area_worst               143 non-null    float64\n",
+      " 26  smoothness_worst         143 non-null    float64\n",
+      " 27  compactness_worst        143 non-null    float64\n",
+      " 28  concavity_worst          143 non-null    float64\n",
+      " 29  concave points_worst     143 non-null    float64\n",
+      " 30  symmetry_worst           143 non-null    float64\n",
+      " 31  fractal_dimension_worst  143 non-null    float64\n",
+      "dtypes: float64(30), int64(1), object(1)\n",
+      "memory usage: 36.9+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "cancer_test.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a626e1ca",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #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-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: 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-1 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-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 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-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\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-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><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": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Use the KNN algorithm to classify the tumors in the test set.\n",
+    "knn = KNeighborsClassifier(n_neighbors=5)\n",
+    "knn\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "12bf28a5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define predictor variable (X) and response variable (y)\n",
+    "\n",
+    "X = cancer_train[[\"perimeter_mean\",\"concavity_mean\"]]\n",
+    "y = cancer_train['diagnosis']                                    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "e093ff17",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-3 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #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-3 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-3 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: 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-3 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-3 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-3 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-3 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-3 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 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-3 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\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-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><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=\"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": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "knn.fit(X, y)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b5d7188f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Step 4. Predict on the test data\n",
+    "# Make predictions on the test set\n",
+    "cancer_test['predicted'] = knn.predict(cancer_test[[\"perimeter_mean\",\"concavity_mean\"]])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "13766f35",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>diagnosis</th>\n",
+       "      <th>predicted</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>357</th>\n",
+       "      <td>901028</td>\n",
+       "      <td>Benign</td>\n",
+       "      <td>Benign</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>361</th>\n",
+       "      <td>901041</td>\n",
+       "      <td>Benign</td>\n",
+       "      <td>Benign</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>212</th>\n",
+       "      <td>8810703</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>Malignant</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>527</th>\n",
+       "      <td>91813702</td>\n",
+       "      <td>Benign</td>\n",
+       "      <td>Benign</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>8510824</td>\n",
+       "      <td>Benign</td>\n",
+       "      <td>Benign</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>364</th>\n",
+       "      <td>9010877</td>\n",
+       "      <td>Benign</td>\n",
+       "      <td>Benign</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>434</th>\n",
+       "      <td>908469</td>\n",
+       "      <td>Benign</td>\n",
+       "      <td>Benign</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>299</th>\n",
+       "      <td>892399</td>\n",
+       "      <td>Benign</td>\n",
+       "      <td>Benign</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>488</th>\n",
+       "      <td>913512</td>\n",
+       "      <td>Benign</td>\n",
+       "      <td>Benign</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>332</th>\n",
+       "      <td>897132</td>\n",
+       "      <td>Benign</td>\n",
+       "      <td>Benign</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>143 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           id  diagnosis  predicted\n",
+       "357    901028     Benign     Benign\n",
+       "361    901041     Benign     Benign\n",
+       "212   8810703  Malignant  Malignant\n",
+       "527  91813702     Benign     Benign\n",
+       "21    8510824     Benign     Benign\n",
+       "..        ...        ...        ...\n",
+       "364   9010877     Benign     Benign\n",
+       "434    908469     Benign     Benign\n",
+       "299    892399     Benign     Benign\n",
+       "488    913512     Benign     Benign\n",
+       "332    897132     Benign     Benign\n",
+       "\n",
+       "[143 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Compare the predicted values to the actual values\n",
+    "cancer_test[['id','diagnosis','predicted']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "85dc55a8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9230769230769231"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Calculate the accuracy of the model\n",
+    "knn.score(\n",
+    "    cancer_test[[\"perimeter_mean\",\"concavity_mean\"]], cancer_test['diagnosis']\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "59cf4bb6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>Predicted</th>\n",
+       "      <th>Benign</th>\n",
+       "      <th>Malignant</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Actual</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Benign</th>\n",
+       "      <td>88</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Malignant</th>\n",
+       "      <td>9</td>\n",
+       "      <td>44</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Predicted  Benign  Malignant\n",
+       "Actual                      \n",
+       "Benign         88          2\n",
+       "Malignant       9         44"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Cross-validation to evaluate the model\n",
+    "pd.crosstab(\n",
+    "    cancer_test['diagnosis'],\n",
+    "    cancer_test['predicted'],\n",
+    "    rownames=['Actual'],\n",
+    "    colnames=['Predicted']\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "936aaea1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9565217391304348"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Caluclate precision and recall\n",
+    "precision_score(\n",
+    "    y_true = cancer_test['diagnosis'],\n",
+    "    y_pred = cancer_test['predicted'],\n",
+    "    pos_label= \"Malignant\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "4ea43ebe",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.8301886792452831"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Calculate recall\n",
+    "recall_score(\n",
+    "    y_true = cancer_test['diagnosis'],\n",
+    "    y_pred = cancer_test['predicted'],\n",
+    "    pos_label = \"Malignant\"\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "id": "11524e1f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Split the data into train and valudation splits\n",
+    "\n",
+    "np.random.seed(1)\n",
+    "cancer_subtrain, cancer_validation = train_test_split(\n",
+    "    cancer_train, train_size = 0.75,shuffle=True, stratify=cancer_train['diagnosis']\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "id": "007e01cc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-28 {\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-28 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-28 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-28 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-28 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-28 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-28 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-28 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-28 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-28 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-28 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-28 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-28 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-28 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-28 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-28 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-28 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-28 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-28 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-28 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-28 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-28 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-28 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-28 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-28 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-28 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-28 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-28 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-28 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-28 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-28 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-28 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-28 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-28 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-28 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-28 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-28 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-28 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-28 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-28 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-28 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-28 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-28 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-28\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=4)</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-40\" type=\"checkbox\" checked><label for=\"sk-estimator-id-40\" 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\">4</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=4)"
+      ]
+     },
+     "execution_count": 114,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Fit the model on the training data\n",
+    "# Step 1. Initialize the model\n",
+    "knn = KNeighborsClassifier(n_neighbors=4)\n",
+    "\n",
+    "#Step 2. Define the model X and y\n",
+    "X = cancer_subtrain[[\"perimeter_mean\",\"concavity_mean\"]]\n",
+    "y = cancer_subtrain['diagnosis']\n",
+    "\n",
+    "#Step 3. Fit the model to our data\n",
+    "knn.fit(X,y)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "id": "d9a73931",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.875"
+      ]
+     },
+     "execution_count": 115,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "# Step 4. Evaluate the model on the validation set\n",
+    "acc = knn.score(\n",
+    "    cancer_validation[[\"perimeter_mean\",\"concavity_mean\"]],\n",
+    "    cancer_validation['diagnosis']\n",
+    ")\n",
+    "acc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "id": "aecad8a0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>fit_time</th>\n",
+       "      <th>score_time</th>\n",
+       "      <th>test_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.002325</td>\n",
+       "      <td>0.007612</td>\n",
+       "      <td>0.906250</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.002293</td>\n",
+       "      <td>0.007731</td>\n",
+       "      <td>0.937500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.001985</td>\n",
+       "      <td>0.011554</td>\n",
+       "      <td>0.859375</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.002731</td>\n",
+       "      <td>0.009120</td>\n",
+       "      <td>0.843750</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.003233</td>\n",
+       "      <td>0.009578</td>\n",
+       "      <td>0.936508</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   fit_time  score_time  test_score\n",
+       "0  0.002325    0.007612    0.906250\n",
+       "1  0.002293    0.007731    0.937500\n",
+       "2  0.001985    0.011554    0.859375\n",
+       "3  0.002731    0.009120    0.843750\n",
+       "4  0.003233    0.009578    0.936508"
+      ]
+     },
+     "execution_count": 116,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Step 5. Cross-validate the model on the validation set\n",
+    "\n",
+    "knn = KNeighborsClassifier(n_neighbors=3)\n",
+    "X = cancer_train[[\"perimeter_mean\",\"concavity_mean\"]]   #Predictor variables\n",
+    "y = cancer_train['diagnosis']                           #Response variable      \n",
+    "\n",
+    "# Cross-validate the model\n",
+    "returned_dictionary = cross_validate(\n",
+    "    estimator= knn,     #The model to evaluate\n",
+    "    cv = 5,             #Number of folds in the cross-validation    \n",
+    "    X = X,              #Predictor variables    \n",
+    "    y = y               #Response variable\n",
+    ")\n",
+    "\n",
+    "cv_5_df  = pd.DataFrame(returned_dictionary)\n",
+    "cv_5_df\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "id": "725902ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>fit_time</th>\n",
+       "      <th>score_time</th>\n",
+       "      <th>test_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>0.002513</td>\n",
+       "      <td>0.009119</td>\n",
+       "      <td>0.896677</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sem</th>\n",
+       "      <td>0.000215</td>\n",
+       "      <td>0.000719</td>\n",
+       "      <td>0.019413</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      fit_time  score_time  test_score\n",
+       "mean  0.002513    0.009119    0.896677\n",
+       "sem   0.000215    0.000719    0.019413"
+      ]
+     },
+     "execution_count": 117,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Display the agg mean and sem\n",
+    "cv_5_metrics = cv_5_df.agg(['mean','sem'])\n",
+    "cv_5_metrics\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "id": "0cc58e75",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Step 6. Hyperparameter tuning with GridSearchCV, start with k = 1 to 385 and step by 5\n",
+    "parameter_grid = {\n",
+    "    \"n_neighbors\" : range(1,255,5)      \n",
+    "}\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "id": "c09c28e9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Use GridSearchCV to search for the best hyperparameter with 10-fold cross-validation\n",
+    "cancer_tune_grid = GridSearchCV(\n",
+    "    estimator = knn,   \n",
+    "    param_grid = parameter_grid,          \n",
+    "    cv = 5                               \n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "id": "8c171e02",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-32 {\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-32 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-32 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-32 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-32 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-32 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-32 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-32 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-32 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-32 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-32 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-32 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-32 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-32 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-32 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-32 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-32 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-32 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-32 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-32 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-32 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-32 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-32 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-32 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-32 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-32 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-32 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-32 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-32 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-32 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-32 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-32 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-32 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-32 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-32 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-32 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-32 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-32 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-32 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-32 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-32 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-32 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-32 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-32\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=KNeighborsClassifier(n_neighbors=31),\n",
+       "             param_grid={&#x27;n_neighbors&#x27;: range(1, 255, 5)})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-48\" type=\"checkbox\" ><label for=\"sk-estimator-id-48\" 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\">KNeighborsCla..._neighbors=31)</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;: range(1, 255, 5)}</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\">5</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-49\" type=\"checkbox\" ><label for=\"sk-estimator-id-49\" 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=31)</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-50\" type=\"checkbox\" ><label for=\"sk-estimator-id-50\" 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\">31</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=5, estimator=KNeighborsClassifier(n_neighbors=31),\n",
+       "             param_grid={'n_neighbors': range(1, 255, 5)})"
+      ]
+     },
+     "execution_count": 131,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Fit the model to the training data\n",
+    "cancer_tune_grid.fit(\n",
+    "    cancer_train[[\"perimeter_mean\",\"concavity_mean\"]],\n",
+    "    cancer_train['diagnosis']\n",
+    ")   \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "id": "bb1aef37",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mean_fit_time</th>\n",
+       "      <th>std_fit_time</th>\n",
+       "      <th>mean_score_time</th>\n",
+       "      <th>std_score_time</th>\n",
+       "      <th>param_n_neighbors</th>\n",
+       "      <th>params</th>\n",
+       "      <th>split0_test_score</th>\n",
+       "      <th>split1_test_score</th>\n",
+       "      <th>split2_test_score</th>\n",
+       "      <th>split3_test_score</th>\n",
+       "      <th>split4_test_score</th>\n",
+       "      <th>mean_test_score</th>\n",
+       "      <th>std_test_score</th>\n",
+       "      <th>rank_test_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.002609</td>\n",
+       "      <td>0.000246</td>\n",
+       "      <td>0.009980</td>\n",
+       "      <td>0.001516</td>\n",
+       "      <td>1</td>\n",
+       "      <td>{'n_neighbors': 1}</td>\n",
+       "      <td>0.843750</td>\n",
+       "      <td>0.906250</td>\n",
+       "      <td>0.843750</td>\n",
+       "      <td>0.843750</td>\n",
+       "      <td>0.920635</td>\n",
+       "      <td>0.871627</td>\n",
+       "      <td>0.034444</td>\n",
+       "      <td>33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.011544</td>\n",
+       "      <td>0.016487</td>\n",
+       "      <td>0.023447</td>\n",
+       "      <td>0.026740</td>\n",
+       "      <td>6</td>\n",
+       "      <td>{'n_neighbors': 6}</td>\n",
+       "      <td>0.890625</td>\n",
+       "      <td>0.937500</td>\n",
+       "      <td>0.921875</td>\n",
+       "      <td>0.890625</td>\n",
+       "      <td>0.984127</td>\n",
+       "      <td>0.924950</td>\n",
+       "      <td>0.034714</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.005711</td>\n",
+       "      <td>0.004610</td>\n",
+       "      <td>0.014418</td>\n",
+       "      <td>0.010756</td>\n",
+       "      <td>11</td>\n",
+       "      <td>{'n_neighbors': 11}</td>\n",
+       "      <td>0.890625</td>\n",
+       "      <td>0.953125</td>\n",
+       "      <td>0.906250</td>\n",
+       "      <td>0.906250</td>\n",
+       "      <td>0.968254</td>\n",
+       "      <td>0.924901</td>\n",
+       "      <td>0.030155</td>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.002304</td>\n",
+       "      <td>0.000251</td>\n",
+       "      <td>0.007419</td>\n",
+       "      <td>0.000800</td>\n",
+       "      <td>16</td>\n",
+       "      <td>{'n_neighbors': 16}</td>\n",
+       "      <td>0.875000</td>\n",
+       "      <td>0.953125</td>\n",
+       "      <td>0.906250</td>\n",
+       "      <td>0.875000</td>\n",
+       "      <td>0.968254</td>\n",
+       "      <td>0.915526</td>\n",
+       "      <td>0.038896</td>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.002186</td>\n",
+       "      <td>0.000142</td>\n",
+       "      <td>0.007026</td>\n",
+       "      <td>0.000476</td>\n",
+       "      <td>21</td>\n",
+       "      <td>{'n_neighbors': 21}</td>\n",
+       "      <td>0.906250</td>\n",
+       "      <td>0.953125</td>\n",
+       "      <td>0.906250</td>\n",
+       "      <td>0.906250</td>\n",
+       "      <td>0.984127</td>\n",
+       "      <td>0.931200</td>\n",
+       "      <td>0.032092</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0.002180</td>\n",
+       "      <td>0.000211</td>\n",
+       "      <td>0.007034</td>\n",
+       "      <td>0.000330</td>\n",
+       "      <td>26</td>\n",
+       "      <td>{'n_neighbors': 26}</td>\n",
+       "      <td>0.890625</td>\n",
+       "      <td>0.937500</td>\n",
+       "      <td>0.921875</td>\n",
+       "      <td>0.906250</td>\n",
+       "      <td>0.984127</td>\n",
+       "      <td>0.928075</td>\n",
+       "      <td>0.032087</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>0.002234</td>\n",
+       "      <td>0.000103</td>\n",
+       "      <td>0.007128</td>\n",
+       "      <td>0.000402</td>\n",
+       "      <td>31</td>\n",
+       "      <td>{'n_neighbors': 31}</td>\n",
+       "      <td>0.906250</td>\n",
+       "      <td>0.953125</td>\n",
+       "      <td>0.921875</td>\n",
+       "      <td>0.906250</td>\n",
+       "      <td>0.984127</td>\n",
+       "      <td>0.934325</td>\n",
+       "      <td>0.030216</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>0.002200</td>\n",
+       "      <td>0.000226</td>\n",
+       "      <td>0.006886</td>\n",
+       "      <td>0.000553</td>\n",
+       "      <td>36</td>\n",
+       "      <td>{'n_neighbors': 36}</td>\n",
+       "      <td>0.906250</td>\n",
+       "      <td>0.953125</td>\n",
+       "      <td>0.921875</td>\n",
+       "      <td>0.921875</td>\n",
+       "      <td>0.968254</td>\n",
+       "      <td>0.934276</td>\n",
+       "      <td>0.022816</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>0.002400</td>\n",
+       "      <td>0.000193</td>\n",
+       "      <td>0.006815</td>\n",
+       "      <td>0.000586</td>\n",
+       "      <td>41</td>\n",
+       "      <td>{'n_neighbors': 41}</td>\n",
+       "      <td>0.890625</td>\n",
+       "      <td>0.953125</td>\n",
+       "      <td>0.921875</td>\n",
+       "      <td>0.921875</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.927976</td>\n",
+       "      <td>0.023228</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>0.002117</td>\n",
+       "      <td>0.000280</td>\n",
+       "      <td>0.007015</td>\n",
+       "      <td>0.000471</td>\n",
+       "      <td>46</td>\n",
+       "      <td>{'n_neighbors': 46}</td>\n",
+       "      <td>0.906250</td>\n",
+       "      <td>0.953125</td>\n",
+       "      <td>0.921875</td>\n",
+       "      <td>0.921875</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.931101</td>\n",
+       "      <td>0.018578</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>0.002149</td>\n",
+       "      <td>0.000243</td>\n",
+       "      <td>0.007212</td>\n",
+       "      <td>0.000606</td>\n",
+       "      <td>51</td>\n",
+       "      <td>{'n_neighbors': 51}</td>\n",
+       "      <td>0.890625</td>\n",
+       "      <td>0.953125</td>\n",
+       "      <td>0.921875</td>\n",
+       "      <td>0.921875</td>\n",
+       "      <td>0.936508</td>\n",
+       "      <td>0.924802</td>\n",
+       "      <td>0.020613</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>0.002071</td>\n",
+       "      <td>0.000129</td>\n",
+       "      <td>0.007128</td>\n",
+       "      <td>0.000501</td>\n",
+       "      <td>56</td>\n",
+       "      <td>{'n_neighbors': 56}</td>\n",
+       "      <td>0.890625</td>\n",
+       "      <td>0.953125</td>\n",
+       "      <td>0.921875</td>\n",
+       "      <td>0.921875</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.927976</td>\n",
+       "      <td>0.023228</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>0.002441</td>\n",
+       "      <td>0.000247</td>\n",
+       "      <td>0.006698</td>\n",
+       "      <td>0.000326</td>\n",
+       "      <td>61</td>\n",
+       "      <td>{'n_neighbors': 61}</td>\n",
+       "      <td>0.890625</td>\n",
+       "      <td>0.953125</td>\n",
+       "      <td>0.921875</td>\n",
+       "      <td>0.921875</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.927976</td>\n",
+       "      <td>0.023228</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>0.002148</td>\n",
+       "      <td>0.000188</td>\n",
+       "      <td>0.007167</td>\n",
+       "      <td>0.000445</td>\n",
+       "      <td>66</td>\n",
+       "      <td>{'n_neighbors': 66}</td>\n",
+       "      <td>0.890625</td>\n",
+       "      <td>0.953125</td>\n",
+       "      <td>0.921875</td>\n",
+       "      <td>0.921875</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.927976</td>\n",
+       "      <td>0.023228</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>0.002049</td>\n",
+       "      <td>0.000120</td>\n",
+       "      <td>0.007403</td>\n",
+       "      <td>0.000662</td>\n",
+       "      <td>71</td>\n",
+       "      <td>{'n_neighbors': 71}</td>\n",
+       "      <td>0.875000</td>\n",
+       "      <td>0.953125</td>\n",
+       "      <td>0.890625</td>\n",
+       "      <td>0.921875</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.918601</td>\n",
+       "      <td>0.031709</td>\n",
+       "      <td>13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>0.002354</td>\n",
+       "      <td>0.000246</td>\n",
+       "      <td>0.007194</td>\n",
+       "      <td>0.000414</td>\n",
+       "      <td>76</td>\n",
+       "      <td>{'n_neighbors': 76}</td>\n",
+       "      <td>0.875000</td>\n",
+       "      <td>0.953125</td>\n",
+       "      <td>0.875000</td>\n",
+       "      <td>0.921875</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.915476</td>\n",
+       "      <td>0.034920</td>\n",
+       "      <td>15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>0.002192</td>\n",
+       "      <td>0.000317</td>\n",
+       "      <td>0.007325</td>\n",
+       "      <td>0.000605</td>\n",
+       "      <td>81</td>\n",
+       "      <td>{'n_neighbors': 81}</td>\n",
+       "      <td>0.875000</td>\n",
+       "      <td>0.953125</td>\n",
+       "      <td>0.875000</td>\n",
+       "      <td>0.921875</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.915476</td>\n",
+       "      <td>0.034920</td>\n",
+       "      <td>15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>0.002220</td>\n",
+       "      <td>0.000217</td>\n",
+       "      <td>0.007212</td>\n",
+       "      <td>0.000495</td>\n",
+       "      <td>86</td>\n",
+       "      <td>{'n_neighbors': 86}</td>\n",
+       "      <td>0.875000</td>\n",
+       "      <td>0.953125</td>\n",
+       "      <td>0.875000</td>\n",
+       "      <td>0.921875</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.915476</td>\n",
+       "      <td>0.034920</td>\n",
+       "      <td>15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>0.002165</td>\n",
+       "      <td>0.000154</td>\n",
+       "      <td>0.007708</td>\n",
+       "      <td>0.000911</td>\n",
+       "      <td>91</td>\n",
+       "      <td>{'n_neighbors': 91}</td>\n",
+       "      <td>0.875000</td>\n",
+       "      <td>0.953125</td>\n",
+       "      <td>0.875000</td>\n",
+       "      <td>0.921875</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.915476</td>\n",
+       "      <td>0.034920</td>\n",
+       "      <td>15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>0.002115</td>\n",
+       "      <td>0.000173</td>\n",
+       "      <td>0.007518</td>\n",
+       "      <td>0.000693</td>\n",
+       "      <td>96</td>\n",
+       "      <td>{'n_neighbors': 96}</td>\n",
+       "      <td>0.875000</td>\n",
+       "      <td>0.953125</td>\n",
+       "      <td>0.875000</td>\n",
+       "      <td>0.921875</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.915476</td>\n",
+       "      <td>0.034920</td>\n",
+       "      <td>15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>0.002282</td>\n",
+       "      <td>0.000260</td>\n",
+       "      <td>0.007648</td>\n",
+       "      <td>0.000725</td>\n",
+       "      <td>101</td>\n",
+       "      <td>{'n_neighbors': 101}</td>\n",
+       "      <td>0.859375</td>\n",
+       "      <td>0.953125</td>\n",
+       "      <td>0.875000</td>\n",
+       "      <td>0.921875</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.912351</td>\n",
+       "      <td>0.038877</td>\n",
+       "      <td>20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>0.002574</td>\n",
+       "      <td>0.000440</td>\n",
+       "      <td>0.007548</td>\n",
+       "      <td>0.000593</td>\n",
+       "      <td>106</td>\n",
+       "      <td>{'n_neighbors': 106}</td>\n",
+       "      <td>0.859375</td>\n",
+       "      <td>0.921875</td>\n",
+       "      <td>0.875000</td>\n",
+       "      <td>0.921875</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.906101</td>\n",
+       "      <td>0.034030</td>\n",
+       "      <td>21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>0.002415</td>\n",
+       "      <td>0.000198</td>\n",
+       "      <td>0.007326</td>\n",
+       "      <td>0.000485</td>\n",
+       "      <td>111</td>\n",
+       "      <td>{'n_neighbors': 111}</td>\n",
+       "      <td>0.859375</td>\n",
+       "      <td>0.921875</td>\n",
+       "      <td>0.875000</td>\n",
+       "      <td>0.906250</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.902976</td>\n",
+       "      <td>0.033143</td>\n",
+       "      <td>22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>0.002109</td>\n",
+       "      <td>0.000240</td>\n",
+       "      <td>0.007405</td>\n",
+       "      <td>0.000745</td>\n",
+       "      <td>116</td>\n",
+       "      <td>{'n_neighbors': 116}</td>\n",
+       "      <td>0.859375</td>\n",
+       "      <td>0.906250</td>\n",
+       "      <td>0.875000</td>\n",
+       "      <td>0.890625</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.896726</td>\n",
+       "      <td>0.031914</td>\n",
+       "      <td>23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>0.002204</td>\n",
+       "      <td>0.000204</td>\n",
+       "      <td>0.007408</td>\n",
+       "      <td>0.000670</td>\n",
+       "      <td>121</td>\n",
+       "      <td>{'n_neighbors': 121}</td>\n",
+       "      <td>0.859375</td>\n",
+       "      <td>0.906250</td>\n",
+       "      <td>0.875000</td>\n",
+       "      <td>0.875000</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.893601</td>\n",
+       "      <td>0.033101</td>\n",
+       "      <td>25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>0.002139</td>\n",
+       "      <td>0.000232</td>\n",
+       "      <td>0.007379</td>\n",
+       "      <td>0.000371</td>\n",
+       "      <td>126</td>\n",
+       "      <td>{'n_neighbors': 126}</td>\n",
+       "      <td>0.843750</td>\n",
+       "      <td>0.906250</td>\n",
+       "      <td>0.875000</td>\n",
+       "      <td>0.875000</td>\n",
+       "      <td>0.968254</td>\n",
+       "      <td>0.893651</td>\n",
+       "      <td>0.042214</td>\n",
+       "      <td>24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>0.002162</td>\n",
+       "      <td>0.000327</td>\n",
+       "      <td>0.008320</td>\n",
+       "      <td>0.000892</td>\n",
+       "      <td>131</td>\n",
+       "      <td>{'n_neighbors': 131}</td>\n",
+       "      <td>0.828125</td>\n",
+       "      <td>0.890625</td>\n",
+       "      <td>0.875000</td>\n",
+       "      <td>0.890625</td>\n",
+       "      <td>0.968254</td>\n",
+       "      <td>0.890526</td>\n",
+       "      <td>0.045115</td>\n",
+       "      <td>26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>0.002056</td>\n",
+       "      <td>0.000193</td>\n",
+       "      <td>0.008932</td>\n",
+       "      <td>0.000954</td>\n",
+       "      <td>136</td>\n",
+       "      <td>{'n_neighbors': 136}</td>\n",
+       "      <td>0.843750</td>\n",
+       "      <td>0.890625</td>\n",
+       "      <td>0.875000</td>\n",
+       "      <td>0.859375</td>\n",
+       "      <td>0.968254</td>\n",
+       "      <td>0.887401</td>\n",
+       "      <td>0.043341</td>\n",
+       "      <td>27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>0.002151</td>\n",
+       "      <td>0.000429</td>\n",
+       "      <td>0.008031</td>\n",
+       "      <td>0.000780</td>\n",
+       "      <td>141</td>\n",
+       "      <td>{'n_neighbors': 141}</td>\n",
+       "      <td>0.843750</td>\n",
+       "      <td>0.890625</td>\n",
+       "      <td>0.875000</td>\n",
+       "      <td>0.859375</td>\n",
+       "      <td>0.968254</td>\n",
+       "      <td>0.887401</td>\n",
+       "      <td>0.043341</td>\n",
+       "      <td>27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>0.002066</td>\n",
+       "      <td>0.000140</td>\n",
+       "      <td>0.008744</td>\n",
+       "      <td>0.000615</td>\n",
+       "      <td>146</td>\n",
+       "      <td>{'n_neighbors': 146}</td>\n",
+       "      <td>0.843750</td>\n",
+       "      <td>0.890625</td>\n",
+       "      <td>0.875000</td>\n",
+       "      <td>0.859375</td>\n",
+       "      <td>0.968254</td>\n",
+       "      <td>0.887401</td>\n",
+       "      <td>0.043341</td>\n",
+       "      <td>27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>0.002283</td>\n",
+       "      <td>0.000485</td>\n",
+       "      <td>0.008331</td>\n",
+       "      <td>0.000218</td>\n",
+       "      <td>151</td>\n",
+       "      <td>{'n_neighbors': 151}</td>\n",
+       "      <td>0.843750</td>\n",
+       "      <td>0.890625</td>\n",
+       "      <td>0.875000</td>\n",
+       "      <td>0.859375</td>\n",
+       "      <td>0.968254</td>\n",
+       "      <td>0.887401</td>\n",
+       "      <td>0.043341</td>\n",
+       "      <td>27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>0.002121</td>\n",
+       "      <td>0.000321</td>\n",
+       "      <td>0.008194</td>\n",
+       "      <td>0.000715</td>\n",
+       "      <td>156</td>\n",
+       "      <td>{'n_neighbors': 156}</td>\n",
+       "      <td>0.843750</td>\n",
+       "      <td>0.890625</td>\n",
+       "      <td>0.843750</td>\n",
+       "      <td>0.828125</td>\n",
+       "      <td>0.968254</td>\n",
+       "      <td>0.874901</td>\n",
+       "      <td>0.051168</td>\n",
+       "      <td>31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>0.002066</td>\n",
+       "      <td>0.000341</td>\n",
+       "      <td>0.008414</td>\n",
+       "      <td>0.000868</td>\n",
+       "      <td>161</td>\n",
+       "      <td>{'n_neighbors': 161}</td>\n",
+       "      <td>0.843750</td>\n",
+       "      <td>0.875000</td>\n",
+       "      <td>0.843750</td>\n",
+       "      <td>0.828125</td>\n",
+       "      <td>0.968254</td>\n",
+       "      <td>0.871776</td>\n",
+       "      <td>0.050586</td>\n",
+       "      <td>32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>0.002474</td>\n",
+       "      <td>0.000517</td>\n",
+       "      <td>0.008915</td>\n",
+       "      <td>0.000519</td>\n",
+       "      <td>166</td>\n",
+       "      <td>{'n_neighbors': 166}</td>\n",
+       "      <td>0.812500</td>\n",
+       "      <td>0.796875</td>\n",
+       "      <td>0.812500</td>\n",
+       "      <td>0.812500</td>\n",
+       "      <td>0.920635</td>\n",
+       "      <td>0.831002</td>\n",
+       "      <td>0.045223</td>\n",
+       "      <td>34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>0.002139</td>\n",
+       "      <td>0.000287</td>\n",
+       "      <td>0.008356</td>\n",
+       "      <td>0.000553</td>\n",
+       "      <td>171</td>\n",
+       "      <td>{'n_neighbors': 171}</td>\n",
+       "      <td>0.812500</td>\n",
+       "      <td>0.781250</td>\n",
+       "      <td>0.812500</td>\n",
+       "      <td>0.765625</td>\n",
+       "      <td>0.920635</td>\n",
+       "      <td>0.818502</td>\n",
+       "      <td>0.054198</td>\n",
+       "      <td>35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>0.002228</td>\n",
+       "      <td>0.000189</td>\n",
+       "      <td>0.008763</td>\n",
+       "      <td>0.000891</td>\n",
+       "      <td>176</td>\n",
+       "      <td>{'n_neighbors': 176}</td>\n",
+       "      <td>0.765625</td>\n",
+       "      <td>0.718750</td>\n",
+       "      <td>0.718750</td>\n",
+       "      <td>0.765625</td>\n",
+       "      <td>0.904762</td>\n",
+       "      <td>0.774702</td>\n",
+       "      <td>0.068325</td>\n",
+       "      <td>36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <td>0.002152</td>\n",
+       "      <td>0.000288</td>\n",
+       "      <td>0.008477</td>\n",
+       "      <td>0.000637</td>\n",
+       "      <td>181</td>\n",
+       "      <td>{'n_neighbors': 181}</td>\n",
+       "      <td>0.750000</td>\n",
+       "      <td>0.703125</td>\n",
+       "      <td>0.718750</td>\n",
+       "      <td>0.750000</td>\n",
+       "      <td>0.888889</td>\n",
+       "      <td>0.762153</td>\n",
+       "      <td>0.065917</td>\n",
+       "      <td>37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37</th>\n",
+       "      <td>0.002122</td>\n",
+       "      <td>0.000253</td>\n",
+       "      <td>0.009112</td>\n",
+       "      <td>0.000844</td>\n",
+       "      <td>186</td>\n",
+       "      <td>{'n_neighbors': 186}</td>\n",
+       "      <td>0.703125</td>\n",
+       "      <td>0.671875</td>\n",
+       "      <td>0.687500</td>\n",
+       "      <td>0.734375</td>\n",
+       "      <td>0.746032</td>\n",
+       "      <td>0.708581</td>\n",
+       "      <td>0.027890</td>\n",
+       "      <td>38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38</th>\n",
+       "      <td>0.002202</td>\n",
+       "      <td>0.000379</td>\n",
+       "      <td>0.009179</td>\n",
+       "      <td>0.001082</td>\n",
+       "      <td>191</td>\n",
+       "      <td>{'n_neighbors': 191}</td>\n",
+       "      <td>0.625000</td>\n",
+       "      <td>0.625000</td>\n",
+       "      <td>0.625000</td>\n",
+       "      <td>0.625000</td>\n",
+       "      <td>0.634921</td>\n",
+       "      <td>0.626984</td>\n",
+       "      <td>0.003968</td>\n",
+       "      <td>39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39</th>\n",
+       "      <td>0.002145</td>\n",
+       "      <td>0.000238</td>\n",
+       "      <td>0.008900</td>\n",
+       "      <td>0.000476</td>\n",
+       "      <td>196</td>\n",
+       "      <td>{'n_neighbors': 196}</td>\n",
+       "      <td>0.625000</td>\n",
+       "      <td>0.625000</td>\n",
+       "      <td>0.625000</td>\n",
+       "      <td>0.625000</td>\n",
+       "      <td>0.634921</td>\n",
+       "      <td>0.626984</td>\n",
+       "      <td>0.003968</td>\n",
+       "      <td>39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>0.002211</td>\n",
+       "      <td>0.000353</td>\n",
+       "      <td>0.009486</td>\n",
+       "      <td>0.000353</td>\n",
+       "      <td>201</td>\n",
+       "      <td>{'n_neighbors': 201}</td>\n",
+       "      <td>0.625000</td>\n",
+       "      <td>0.625000</td>\n",
+       "      <td>0.625000</td>\n",
+       "      <td>0.625000</td>\n",
+       "      <td>0.634921</td>\n",
+       "      <td>0.626984</td>\n",
+       "      <td>0.003968</td>\n",
+       "      <td>39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>41</th>\n",
+       "      <td>0.002042</td>\n",
+       "      <td>0.000122</td>\n",
+       "      <td>0.008638</td>\n",
+       "      <td>0.000671</td>\n",
+       "      <td>206</td>\n",
+       "      <td>{'n_neighbors': 206}</td>\n",
+       "      <td>0.625000</td>\n",
+       "      <td>0.625000</td>\n",
+       "      <td>0.625000</td>\n",
+       "      <td>0.625000</td>\n",
+       "      <td>0.634921</td>\n",
+       "      <td>0.626984</td>\n",
+       "      <td>0.003968</td>\n",
+       "      <td>39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>42</th>\n",
+       "      <td>0.002573</td>\n",
+       "      <td>0.000444</td>\n",
+       "      <td>0.009005</td>\n",
+       "      <td>0.000865</td>\n",
+       "      <td>211</td>\n",
+       "      <td>{'n_neighbors': 211}</td>\n",
+       "      <td>0.625000</td>\n",
+       "      <td>0.625000</td>\n",
+       "      <td>0.625000</td>\n",
+       "      <td>0.625000</td>\n",
+       "      <td>0.634921</td>\n",
+       "      <td>0.626984</td>\n",
+       "      <td>0.003968</td>\n",
+       "      <td>39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43</th>\n",
+       "      <td>0.002330</td>\n",
+       "      <td>0.000511</td>\n",
+       "      <td>0.008435</td>\n",
+       "      <td>0.000403</td>\n",
+       "      <td>216</td>\n",
+       "      <td>{'n_neighbors': 216}</td>\n",
+       "      <td>0.625000</td>\n",
+       "      <td>0.625000</td>\n",
+       "      <td>0.625000</td>\n",
+       "      <td>0.625000</td>\n",
+       "      <td>0.634921</td>\n",
+       "      <td>0.626984</td>\n",
+       "      <td>0.003968</td>\n",
+       "      <td>39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>44</th>\n",
+       "      <td>0.002261</td>\n",
+       "      <td>0.000237</td>\n",
+       "      <td>0.009381</td>\n",
+       "      <td>0.000628</td>\n",
+       "      <td>221</td>\n",
+       "      <td>{'n_neighbors': 221}</td>\n",
+       "      <td>0.625000</td>\n",
+       "      <td>0.625000</td>\n",
+       "      <td>0.625000</td>\n",
+       "      <td>0.625000</td>\n",
+       "      <td>0.634921</td>\n",
+       "      <td>0.626984</td>\n",
+       "      <td>0.003968</td>\n",
+       "      <td>39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45</th>\n",
+       "      <td>0.002127</td>\n",
+       "      <td>0.000263</td>\n",
+       "      <td>0.008574</td>\n",
+       "      <td>0.000681</td>\n",
+       "      <td>226</td>\n",
+       "      <td>{'n_neighbors': 226}</td>\n",
+       "      <td>0.625000</td>\n",
+       "      <td>0.625000</td>\n",
+       "      <td>0.625000</td>\n",
+       "      <td>0.625000</td>\n",
+       "      <td>0.634921</td>\n",
+       "      <td>0.626984</td>\n",
+       "      <td>0.003968</td>\n",
+       "      <td>39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46</th>\n",
+       "      <td>0.002387</td>\n",
+       "      <td>0.000437</td>\n",
+       "      <td>0.009161</td>\n",
+       "      <td>0.000595</td>\n",
+       "      <td>231</td>\n",
+       "      <td>{'n_neighbors': 231}</td>\n",
+       "      <td>0.625000</td>\n",
+       "      <td>0.625000</td>\n",
+       "      <td>0.625000</td>\n",
+       "      <td>0.625000</td>\n",
+       "      <td>0.634921</td>\n",
+       "      <td>0.626984</td>\n",
+       "      <td>0.003968</td>\n",
+       "      <td>39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>47</th>\n",
+       "      <td>0.002075</td>\n",
+       "      <td>0.000287</td>\n",
+       "      <td>0.009161</td>\n",
+       "      <td>0.000459</td>\n",
+       "      <td>236</td>\n",
+       "      <td>{'n_neighbors': 236}</td>\n",
+       "      <td>0.625000</td>\n",
+       "      <td>0.625000</td>\n",
+       "      <td>0.625000</td>\n",
+       "      <td>0.625000</td>\n",
+       "      <td>0.634921</td>\n",
+       "      <td>0.626984</td>\n",
+       "      <td>0.003968</td>\n",
+       "      <td>39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48</th>\n",
+       "      <td>0.002147</td>\n",
+       "      <td>0.000393</td>\n",
+       "      <td>0.009507</td>\n",
+       "      <td>0.000920</td>\n",
+       "      <td>241</td>\n",
+       "      <td>{'n_neighbors': 241}</td>\n",
+       "      <td>0.625000</td>\n",
+       "      <td>0.625000</td>\n",
+       "      <td>0.625000</td>\n",
+       "      <td>0.625000</td>\n",
+       "      <td>0.634921</td>\n",
+       "      <td>0.626984</td>\n",
+       "      <td>0.003968</td>\n",
+       "      <td>39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49</th>\n",
+       "      <td>0.002278</td>\n",
+       "      <td>0.000460</td>\n",
+       "      <td>0.009616</td>\n",
+       "      <td>0.000783</td>\n",
+       "      <td>246</td>\n",
+       "      <td>{'n_neighbors': 246}</td>\n",
+       "      <td>0.625000</td>\n",
+       "      <td>0.625000</td>\n",
+       "      <td>0.625000</td>\n",
+       "      <td>0.625000</td>\n",
+       "      <td>0.634921</td>\n",
+       "      <td>0.626984</td>\n",
+       "      <td>0.003968</td>\n",
+       "      <td>39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50</th>\n",
+       "      <td>0.002102</td>\n",
+       "      <td>0.000234</td>\n",
+       "      <td>0.010030</td>\n",
+       "      <td>0.000446</td>\n",
+       "      <td>251</td>\n",
+       "      <td>{'n_neighbors': 251}</td>\n",
+       "      <td>0.625000</td>\n",
+       "      <td>0.625000</td>\n",
+       "      <td>0.625000</td>\n",
+       "      <td>0.625000</td>\n",
+       "      <td>0.634921</td>\n",
+       "      <td>0.626984</td>\n",
+       "      <td>0.003968</td>\n",
+       "      <td>39</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
+       "0        0.002609      0.000246         0.009980        0.001516   \n",
+       "1        0.011544      0.016487         0.023447        0.026740   \n",
+       "2        0.005711      0.004610         0.014418        0.010756   \n",
+       "3        0.002304      0.000251         0.007419        0.000800   \n",
+       "4        0.002186      0.000142         0.007026        0.000476   \n",
+       "5        0.002180      0.000211         0.007034        0.000330   \n",
+       "6        0.002234      0.000103         0.007128        0.000402   \n",
+       "7        0.002200      0.000226         0.006886        0.000553   \n",
+       "8        0.002400      0.000193         0.006815        0.000586   \n",
+       "9        0.002117      0.000280         0.007015        0.000471   \n",
+       "10       0.002149      0.000243         0.007212        0.000606   \n",
+       "11       0.002071      0.000129         0.007128        0.000501   \n",
+       "12       0.002441      0.000247         0.006698        0.000326   \n",
+       "13       0.002148      0.000188         0.007167        0.000445   \n",
+       "14       0.002049      0.000120         0.007403        0.000662   \n",
+       "15       0.002354      0.000246         0.007194        0.000414   \n",
+       "16       0.002192      0.000317         0.007325        0.000605   \n",
+       "17       0.002220      0.000217         0.007212        0.000495   \n",
+       "18       0.002165      0.000154         0.007708        0.000911   \n",
+       "19       0.002115      0.000173         0.007518        0.000693   \n",
+       "20       0.002282      0.000260         0.007648        0.000725   \n",
+       "21       0.002574      0.000440         0.007548        0.000593   \n",
+       "22       0.002415      0.000198         0.007326        0.000485   \n",
+       "23       0.002109      0.000240         0.007405        0.000745   \n",
+       "24       0.002204      0.000204         0.007408        0.000670   \n",
+       "25       0.002139      0.000232         0.007379        0.000371   \n",
+       "26       0.002162      0.000327         0.008320        0.000892   \n",
+       "27       0.002056      0.000193         0.008932        0.000954   \n",
+       "28       0.002151      0.000429         0.008031        0.000780   \n",
+       "29       0.002066      0.000140         0.008744        0.000615   \n",
+       "30       0.002283      0.000485         0.008331        0.000218   \n",
+       "31       0.002121      0.000321         0.008194        0.000715   \n",
+       "32       0.002066      0.000341         0.008414        0.000868   \n",
+       "33       0.002474      0.000517         0.008915        0.000519   \n",
+       "34       0.002139      0.000287         0.008356        0.000553   \n",
+       "35       0.002228      0.000189         0.008763        0.000891   \n",
+       "36       0.002152      0.000288         0.008477        0.000637   \n",
+       "37       0.002122      0.000253         0.009112        0.000844   \n",
+       "38       0.002202      0.000379         0.009179        0.001082   \n",
+       "39       0.002145      0.000238         0.008900        0.000476   \n",
+       "40       0.002211      0.000353         0.009486        0.000353   \n",
+       "41       0.002042      0.000122         0.008638        0.000671   \n",
+       "42       0.002573      0.000444         0.009005        0.000865   \n",
+       "43       0.002330      0.000511         0.008435        0.000403   \n",
+       "44       0.002261      0.000237         0.009381        0.000628   \n",
+       "45       0.002127      0.000263         0.008574        0.000681   \n",
+       "46       0.002387      0.000437         0.009161        0.000595   \n",
+       "47       0.002075      0.000287         0.009161        0.000459   \n",
+       "48       0.002147      0.000393         0.009507        0.000920   \n",
+       "49       0.002278      0.000460         0.009616        0.000783   \n",
+       "50       0.002102      0.000234         0.010030        0.000446   \n",
+       "\n",
+       "    param_n_neighbors                params  split0_test_score  \\\n",
+       "0                   1    {'n_neighbors': 1}           0.843750   \n",
+       "1                   6    {'n_neighbors': 6}           0.890625   \n",
+       "2                  11   {'n_neighbors': 11}           0.890625   \n",
+       "3                  16   {'n_neighbors': 16}           0.875000   \n",
+       "4                  21   {'n_neighbors': 21}           0.906250   \n",
+       "5                  26   {'n_neighbors': 26}           0.890625   \n",
+       "6                  31   {'n_neighbors': 31}           0.906250   \n",
+       "7                  36   {'n_neighbors': 36}           0.906250   \n",
+       "8                  41   {'n_neighbors': 41}           0.890625   \n",
+       "9                  46   {'n_neighbors': 46}           0.906250   \n",
+       "10                 51   {'n_neighbors': 51}           0.890625   \n",
+       "11                 56   {'n_neighbors': 56}           0.890625   \n",
+       "12                 61   {'n_neighbors': 61}           0.890625   \n",
+       "13                 66   {'n_neighbors': 66}           0.890625   \n",
+       "14                 71   {'n_neighbors': 71}           0.875000   \n",
+       "15                 76   {'n_neighbors': 76}           0.875000   \n",
+       "16                 81   {'n_neighbors': 81}           0.875000   \n",
+       "17                 86   {'n_neighbors': 86}           0.875000   \n",
+       "18                 91   {'n_neighbors': 91}           0.875000   \n",
+       "19                 96   {'n_neighbors': 96}           0.875000   \n",
+       "20                101  {'n_neighbors': 101}           0.859375   \n",
+       "21                106  {'n_neighbors': 106}           0.859375   \n",
+       "22                111  {'n_neighbors': 111}           0.859375   \n",
+       "23                116  {'n_neighbors': 116}           0.859375   \n",
+       "24                121  {'n_neighbors': 121}           0.859375   \n",
+       "25                126  {'n_neighbors': 126}           0.843750   \n",
+       "26                131  {'n_neighbors': 131}           0.828125   \n",
+       "27                136  {'n_neighbors': 136}           0.843750   \n",
+       "28                141  {'n_neighbors': 141}           0.843750   \n",
+       "29                146  {'n_neighbors': 146}           0.843750   \n",
+       "30                151  {'n_neighbors': 151}           0.843750   \n",
+       "31                156  {'n_neighbors': 156}           0.843750   \n",
+       "32                161  {'n_neighbors': 161}           0.843750   \n",
+       "33                166  {'n_neighbors': 166}           0.812500   \n",
+       "34                171  {'n_neighbors': 171}           0.812500   \n",
+       "35                176  {'n_neighbors': 176}           0.765625   \n",
+       "36                181  {'n_neighbors': 181}           0.750000   \n",
+       "37                186  {'n_neighbors': 186}           0.703125   \n",
+       "38                191  {'n_neighbors': 191}           0.625000   \n",
+       "39                196  {'n_neighbors': 196}           0.625000   \n",
+       "40                201  {'n_neighbors': 201}           0.625000   \n",
+       "41                206  {'n_neighbors': 206}           0.625000   \n",
+       "42                211  {'n_neighbors': 211}           0.625000   \n",
+       "43                216  {'n_neighbors': 216}           0.625000   \n",
+       "44                221  {'n_neighbors': 221}           0.625000   \n",
+       "45                226  {'n_neighbors': 226}           0.625000   \n",
+       "46                231  {'n_neighbors': 231}           0.625000   \n",
+       "47                236  {'n_neighbors': 236}           0.625000   \n",
+       "48                241  {'n_neighbors': 241}           0.625000   \n",
+       "49                246  {'n_neighbors': 246}           0.625000   \n",
+       "50                251  {'n_neighbors': 251}           0.625000   \n",
+       "\n",
+       "    split1_test_score  split2_test_score  split3_test_score  \\\n",
+       "0            0.906250           0.843750           0.843750   \n",
+       "1            0.937500           0.921875           0.890625   \n",
+       "2            0.953125           0.906250           0.906250   \n",
+       "3            0.953125           0.906250           0.875000   \n",
+       "4            0.953125           0.906250           0.906250   \n",
+       "5            0.937500           0.921875           0.906250   \n",
+       "6            0.953125           0.921875           0.906250   \n",
+       "7            0.953125           0.921875           0.921875   \n",
+       "8            0.953125           0.921875           0.921875   \n",
+       "9            0.953125           0.921875           0.921875   \n",
+       "10           0.953125           0.921875           0.921875   \n",
+       "11           0.953125           0.921875           0.921875   \n",
+       "12           0.953125           0.921875           0.921875   \n",
+       "13           0.953125           0.921875           0.921875   \n",
+       "14           0.953125           0.890625           0.921875   \n",
+       "15           0.953125           0.875000           0.921875   \n",
+       "16           0.953125           0.875000           0.921875   \n",
+       "17           0.953125           0.875000           0.921875   \n",
+       "18           0.953125           0.875000           0.921875   \n",
+       "19           0.953125           0.875000           0.921875   \n",
+       "20           0.953125           0.875000           0.921875   \n",
+       "21           0.921875           0.875000           0.921875   \n",
+       "22           0.921875           0.875000           0.906250   \n",
+       "23           0.906250           0.875000           0.890625   \n",
+       "24           0.906250           0.875000           0.875000   \n",
+       "25           0.906250           0.875000           0.875000   \n",
+       "26           0.890625           0.875000           0.890625   \n",
+       "27           0.890625           0.875000           0.859375   \n",
+       "28           0.890625           0.875000           0.859375   \n",
+       "29           0.890625           0.875000           0.859375   \n",
+       "30           0.890625           0.875000           0.859375   \n",
+       "31           0.890625           0.843750           0.828125   \n",
+       "32           0.875000           0.843750           0.828125   \n",
+       "33           0.796875           0.812500           0.812500   \n",
+       "34           0.781250           0.812500           0.765625   \n",
+       "35           0.718750           0.718750           0.765625   \n",
+       "36           0.703125           0.718750           0.750000   \n",
+       "37           0.671875           0.687500           0.734375   \n",
+       "38           0.625000           0.625000           0.625000   \n",
+       "39           0.625000           0.625000           0.625000   \n",
+       "40           0.625000           0.625000           0.625000   \n",
+       "41           0.625000           0.625000           0.625000   \n",
+       "42           0.625000           0.625000           0.625000   \n",
+       "43           0.625000           0.625000           0.625000   \n",
+       "44           0.625000           0.625000           0.625000   \n",
+       "45           0.625000           0.625000           0.625000   \n",
+       "46           0.625000           0.625000           0.625000   \n",
+       "47           0.625000           0.625000           0.625000   \n",
+       "48           0.625000           0.625000           0.625000   \n",
+       "49           0.625000           0.625000           0.625000   \n",
+       "50           0.625000           0.625000           0.625000   \n",
+       "\n",
+       "    split4_test_score  mean_test_score  std_test_score  rank_test_score  \n",
+       "0            0.920635         0.871627        0.034444               33  \n",
+       "1            0.984127         0.924950        0.034714               10  \n",
+       "2            0.968254         0.924901        0.030155               11  \n",
+       "3            0.968254         0.915526        0.038896               14  \n",
+       "4            0.984127         0.931200        0.032092                3  \n",
+       "5            0.984127         0.928075        0.032087                5  \n",
+       "6            0.984127         0.934325        0.030216                1  \n",
+       "7            0.968254         0.934276        0.022816                2  \n",
+       "8            0.952381         0.927976        0.023228                6  \n",
+       "9            0.952381         0.931101        0.018578                4  \n",
+       "10           0.936508         0.924802        0.020613               12  \n",
+       "11           0.952381         0.927976        0.023228                6  \n",
+       "12           0.952381         0.927976        0.023228                6  \n",
+       "13           0.952381         0.927976        0.023228                6  \n",
+       "14           0.952381         0.918601        0.031709               13  \n",
+       "15           0.952381         0.915476        0.034920               15  \n",
+       "16           0.952381         0.915476        0.034920               15  \n",
+       "17           0.952381         0.915476        0.034920               15  \n",
+       "18           0.952381         0.915476        0.034920               15  \n",
+       "19           0.952381         0.915476        0.034920               15  \n",
+       "20           0.952381         0.912351        0.038877               20  \n",
+       "21           0.952381         0.906101        0.034030               21  \n",
+       "22           0.952381         0.902976        0.033143               22  \n",
+       "23           0.952381         0.896726        0.031914               23  \n",
+       "24           0.952381         0.893601        0.033101               25  \n",
+       "25           0.968254         0.893651        0.042214               24  \n",
+       "26           0.968254         0.890526        0.045115               26  \n",
+       "27           0.968254         0.887401        0.043341               27  \n",
+       "28           0.968254         0.887401        0.043341               27  \n",
+       "29           0.968254         0.887401        0.043341               27  \n",
+       "30           0.968254         0.887401        0.043341               27  \n",
+       "31           0.968254         0.874901        0.051168               31  \n",
+       "32           0.968254         0.871776        0.050586               32  \n",
+       "33           0.920635         0.831002        0.045223               34  \n",
+       "34           0.920635         0.818502        0.054198               35  \n",
+       "35           0.904762         0.774702        0.068325               36  \n",
+       "36           0.888889         0.762153        0.065917               37  \n",
+       "37           0.746032         0.708581        0.027890               38  \n",
+       "38           0.634921         0.626984        0.003968               39  \n",
+       "39           0.634921         0.626984        0.003968               39  \n",
+       "40           0.634921         0.626984        0.003968               39  \n",
+       "41           0.634921         0.626984        0.003968               39  \n",
+       "42           0.634921         0.626984        0.003968               39  \n",
+       "43           0.634921         0.626984        0.003968               39  \n",
+       "44           0.634921         0.626984        0.003968               39  \n",
+       "45           0.634921         0.626984        0.003968               39  \n",
+       "46           0.634921         0.626984        0.003968               39  \n",
+       "47           0.634921         0.626984        0.003968               39  \n",
+       "48           0.634921         0.626984        0.003968               39  \n",
+       "49           0.634921         0.626984        0.003968               39  \n",
+       "50           0.634921         0.626984        0.003968               39  "
+      ]
+     },
+     "execution_count": 132,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "accuracy_grid = pd.DataFrame(cancer_tune_grid.cv_results_)\n",
+    "accuracy_grid"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "id": "dcf6b287",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create the plot\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "\n",
+    "# Plot mean test scores with error bars\n",
+    "plt.plot(accuracy_grid['param_n_neighbors'], accuracy_grid['mean_test_score'], '-o', color='blue')\n",
+    "\n",
+    "# Add labels and legend\n",
+    "plt.xlabel('Number of Neighbors')\n",
+    "plt.ylabel('Accuracy estimate')\n",
+    "plt.title('K-Nearest Neighbors Performance')\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "id": "a3f91076",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'n_neighbors': 31}"
+      ]
+     },
+     "execution_count": 134,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Get the best hyperparameter\n",
+    "cancer_tune_grid.best_params_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "id": "e4446fcc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-33 {\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-33 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-33 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-33 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-33 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-33 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-33 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-33 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-33 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-33 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-33 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-33 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-33 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-33 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-33 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-33 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-33 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-33 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-33 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-33 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-33 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-33 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-33 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-33 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-33 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-33 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-33 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-33 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-33 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-33 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-33 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-33 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-33 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-33 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-33 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-33 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-33 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-33 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-33 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-33 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-33 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-33 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-33 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-33\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=31)</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-51\" type=\"checkbox\" checked><label for=\"sk-estimator-id-51\" 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\">31</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=31)"
+      ]
+     },
+     "execution_count": 135,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "knn = KNeighborsClassifier(n_neighbors= 31)\n",
+    "X = cancer_train[[\"perimeter_mean\",\"concavity_mean\"]]\n",
+    "y = cancer_train['diagnosis']\n",
+    "\n",
+    "knn.fit(X,y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "id": "3a604b18",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9370629370629371"
+      ]
+     },
+     "execution_count": 136,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "knn.score(\n",
+    "    cancer_test[[\"perimeter_mean\",\"concavity_mean\"]],\n",
+    "    cancer_test['diagnosis']\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cc338bb6",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "dsi_participant",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}