diff --git a/01_materials/notebooks/classwork_dec 11.ipynb b/01_materials/notebooks/classwork_dec 11.ipynb
new file mode 100644
index 000000000..f1c74d718
--- /dev/null
+++ b/01_materials/notebooks/classwork_dec 11.ipynb	
@@ -0,0 +1,3172 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ed591505",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load in 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"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fde7c3ca",
+   "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": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#load in our data set\n",
+    "cancer = pd.read_csv(\"dataset/wdbc.csv\")\n",
+    "cancer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "b392a8e3",
+   "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": [
+    "cancer.info() #give us a summary of our dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "062b49fb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['M', 'B'], dtype=object)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cancer[\"diagnosis\"].unique() #what catergories we have"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "81f82c62",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cancer[\"diagnosis\"] = cancer[\"diagnosis\"].replace({\n",
+    "    \"M\" : \"Malignant\",\n",
+    "    \"B\" : \"Benign\"\n",
+    "})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "14e86820",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Malignant', 'Benign'], dtype=object)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cancer[\"diagnosis\"].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "d011aa52",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "diagnosis\n",
+       "Benign       357\n",
+       "Malignant    212\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cancer[\"diagnosis\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cec4730c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "diagnosis\n",
+       "Benign       0.627417\n",
+       "Malignant    0.372583\n",
+       "Name: proportion, dtype: float64"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cancer[\"diagnosis\"].value_counts(normalize= True) # get counts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "a14b72b7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create mapping between values and colors\n",
+    "labels = cancer[\"diagnosis\"].unique().tolist()\n",
+    "colors = list(mcolors.TABLEAU_COLORS.keys())\n",
+    "color_map = {l: colors[i % len(colors)] for i, l in enumerate(labels)}\n",
+    "\n",
+    "# Plot\n",
+    "plt.scatter(cancer[\"perimeter_mean\"], cancer['concavity_mean'], \n",
+    "            color=cancer[\"diagnosis\"].map(color_map))\n",
+    "\n",
+    "# Create custom legend handles\n",
+    "handles = [plt.Line2D([0], [0], marker='o', color='w', label=label,\n",
+    "                      markersize=10, markerfacecolor=color_map[label])\n",
+    "           for label in labels]\n",
+    "\n",
+    "# Add labels and legend\n",
+    "plt.xlabel('Perimeter Mean')\n",
+    "plt.ylabel('Concavity Mean')\n",
+    "plt.title('Scatter Plot of Perimeter Mean vs \"Concavity Mean\"')\n",
+    "plt.legend(handles=handles, title='Diagnosis')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "e8a28b3f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot existing data\n",
+    "plt.scatter(cancer[\"perimeter_mean\"], cancer['concavity_mean'], \n",
+    "            color=cancer[\"diagnosis\"].map(color_map))\n",
+    "\n",
+    "# Create custom legend handles\n",
+    "handles = [plt.Line2D([0], [0], marker='o', color='w', label=label,\n",
+    "                      markersize=10, markerfacecolor=color_map[label])\n",
+    "           for label in labels]\n",
+    "\n",
+    "# Add new observation\n",
+    "new_observation = {'perimeter_mean': 97, 'concavity_mean': 0.20}\n",
+    "plt.scatter(new_observation['perimeter_mean'], new_observation['concavity_mean'],\n",
+    "            color='red', edgecolor='black', s=100, label='New Observation')\n",
+    "\n",
+    "# Add labels and legend\n",
+    "plt.xlabel('Perimeter Mean')\n",
+    "plt.ylabel('Concavity Mean')\n",
+    "plt.title('Scatter Plot of Perimeter Mean vs Concavity Mean')\n",
+    "plt.legend(handles=handles + [plt.Line2D([0], [0], marker='o', color='w', \n",
+    "                                          markerfacecolor='red', markeredgecolor='black', \n",
+    "                                          markersize=10, label='New Observation')], \n",
+    "           title='Diagnosis')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8655ddac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# perimeter mean  = 97\n",
+    "# concavity mean = 0.20\n",
+    "#difference in x values\n",
+    "new_obs_Perimeter = 97\n",
+    "new_obs_Concavity = 0.2\n",
+    "\n",
+    "cancer[\"dist_from_new\"] = ((cancer[\"perimeter_mean\"] - new_obs_Perimeter)**2 + \n",
+    "(cancer[\"concavity_mean\"] - new_obs_Concavity)**2)**(1/2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "9cbb5482",
+   "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": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cancer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "8621e8fb",
+   "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>291</th>\n",
+       "      <td>8915</td>\n",
+       "      <td>Benign</td>\n",
+       "      <td>14.96</td>\n",
+       "      <td>19.10</td>\n",
+       "      <td>97.03</td>\n",
+       "      <td>687.3</td>\n",
+       "      <td>0.08992</td>\n",
+       "      <td>0.09823</td>\n",
+       "      <td>0.05940</td>\n",
+       "      <td>0.04819</td>\n",
+       "      <td>...</td>\n",
+       "      <td>26.19</td>\n",
+       "      <td>109.1</td>\n",
+       "      <td>809.8</td>\n",
+       "      <td>0.1313</td>\n",
+       "      <td>0.3030</td>\n",
+       "      <td>0.1804</td>\n",
+       "      <td>0.1489</td>\n",
+       "      <td>0.2962</td>\n",
+       "      <td>0.08472</td>\n",
+       "      <td>0.143765</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>138</th>\n",
+       "      <td>868826</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>14.95</td>\n",
+       "      <td>17.57</td>\n",
+       "      <td>96.85</td>\n",
+       "      <td>678.1</td>\n",
+       "      <td>0.11670</td>\n",
+       "      <td>0.13050</td>\n",
+       "      <td>0.15390</td>\n",
+       "      <td>0.08624</td>\n",
+       "      <td>...</td>\n",
+       "      <td>21.43</td>\n",
+       "      <td>121.4</td>\n",
+       "      <td>971.4</td>\n",
+       "      <td>0.1411</td>\n",
+       "      <td>0.2164</td>\n",
+       "      <td>0.3355</td>\n",
+       "      <td>0.1667</td>\n",
+       "      <td>0.3414</td>\n",
+       "      <td>0.07147</td>\n",
+       "      <td>0.156924</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>84799002</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>14.54</td>\n",
+       "      <td>27.54</td>\n",
+       "      <td>96.73</td>\n",
+       "      <td>658.8</td>\n",
+       "      <td>0.11390</td>\n",
+       "      <td>0.15950</td>\n",
+       "      <td>0.16390</td>\n",
+       "      <td>0.07364</td>\n",
+       "      <td>...</td>\n",
+       "      <td>37.13</td>\n",
+       "      <td>124.1</td>\n",
+       "      <td>943.2</td>\n",
+       "      <td>0.1678</td>\n",
+       "      <td>0.6577</td>\n",
+       "      <td>0.7026</td>\n",
+       "      <td>0.1712</td>\n",
+       "      <td>0.4218</td>\n",
+       "      <td>0.13410</td>\n",
+       "      <td>0.272403</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>514</th>\n",
+       "      <td>91594602</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>15.05</td>\n",
+       "      <td>19.07</td>\n",
+       "      <td>97.26</td>\n",
+       "      <td>701.9</td>\n",
+       "      <td>0.09215</td>\n",
+       "      <td>0.08597</td>\n",
+       "      <td>0.07486</td>\n",
+       "      <td>0.04335</td>\n",
+       "      <td>...</td>\n",
+       "      <td>28.06</td>\n",
+       "      <td>113.8</td>\n",
+       "      <td>967.0</td>\n",
+       "      <td>0.1246</td>\n",
+       "      <td>0.2101</td>\n",
+       "      <td>0.2866</td>\n",
+       "      <td>0.1120</td>\n",
+       "      <td>0.2282</td>\n",
+       "      <td>0.06954</td>\n",
+       "      <td>0.288548</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54</th>\n",
+       "      <td>857438</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>15.10</td>\n",
+       "      <td>22.02</td>\n",
+       "      <td>97.26</td>\n",
+       "      <td>712.8</td>\n",
+       "      <td>0.09056</td>\n",
+       "      <td>0.07081</td>\n",
+       "      <td>0.05253</td>\n",
+       "      <td>0.03334</td>\n",
+       "      <td>...</td>\n",
+       "      <td>31.69</td>\n",
+       "      <td>117.7</td>\n",
+       "      <td>1030.0</td>\n",
+       "      <td>0.1389</td>\n",
+       "      <td>0.2057</td>\n",
+       "      <td>0.2712</td>\n",
+       "      <td>0.1530</td>\n",
+       "      <td>0.2675</td>\n",
+       "      <td>0.07873</td>\n",
+       "      <td>0.298910</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 33 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           id  diagnosis  radius_mean  texture_mean  perimeter_mean  \\\n",
+       "291      8915     Benign        14.96         19.10           97.03   \n",
+       "138    868826  Malignant        14.95         17.57           96.85   \n",
+       "15   84799002  Malignant        14.54         27.54           96.73   \n",
+       "514  91594602  Malignant        15.05         19.07           97.26   \n",
+       "54     857438  Malignant        15.10         22.02           97.26   \n",
+       "\n",
+       "     area_mean  smoothness_mean  compactness_mean  concavity_mean  \\\n",
+       "291      687.3          0.08992           0.09823         0.05940   \n",
+       "138      678.1          0.11670           0.13050         0.15390   \n",
+       "15       658.8          0.11390           0.15950         0.16390   \n",
+       "514      701.9          0.09215           0.08597         0.07486   \n",
+       "54       712.8          0.09056           0.07081         0.05253   \n",
+       "\n",
+       "     concave points_mean  ...  texture_worst  perimeter_worst  area_worst  \\\n",
+       "291              0.04819  ...          26.19            109.1       809.8   \n",
+       "138              0.08624  ...          21.43            121.4       971.4   \n",
+       "15               0.07364  ...          37.13            124.1       943.2   \n",
+       "514              0.04335  ...          28.06            113.8       967.0   \n",
+       "54               0.03334  ...          31.69            117.7      1030.0   \n",
+       "\n",
+       "     smoothness_worst  compactness_worst  concavity_worst  \\\n",
+       "291            0.1313             0.3030           0.1804   \n",
+       "138            0.1411             0.2164           0.3355   \n",
+       "15             0.1678             0.6577           0.7026   \n",
+       "514            0.1246             0.2101           0.2866   \n",
+       "54             0.1389             0.2057           0.2712   \n",
+       "\n",
+       "     concave points_worst  symmetry_worst  fractal_dimension_worst  \\\n",
+       "291                0.1489          0.2962                  0.08472   \n",
+       "138                0.1667          0.3414                  0.07147   \n",
+       "15                 0.1712          0.4218                  0.13410   \n",
+       "514                0.1120          0.2282                  0.06954   \n",
+       "54                 0.1530          0.2675                  0.07873   \n",
+       "\n",
+       "     dist_from_new  \n",
+       "291       0.143765  \n",
+       "138       0.156924  \n",
+       "15        0.272403  \n",
+       "514       0.288548  \n",
+       "54        0.298910  \n",
+       "\n",
+       "[5 rows x 33 columns]"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#k = 5\n",
+    "\n",
+    "cancer.nsmallest(5, \"dist_from_new\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "69f7011c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<module 'matplotlib.pyplot' from 'c:\\\\Users\\\\Brianna\\\\LCR\\\\lcr-env\\\\Lib\\\\site-packages\\\\matplotlib\\\\pyplot.py'>"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create mapping between values and colors\n",
+    "labels = cancer[\"diagnosis\"].unique().tolist()\n",
+    "colors = list(mcolors.TABLEAU_COLORS.keys())\n",
+    "color_map = {l: colors[i % len(colors)] for i, l in enumerate(labels)}\n",
+    "\n",
+    "# Create a 3D plot\n",
+    "ax = plt.axes(projection=\"3d\")\n",
+    "\n",
+    "# Plot data points with color corresponding to diagnosis\n",
+    "sc = ax.scatter3D(cancer['perimeter_mean'], cancer['concavity_mean'], cancer['symmetry_mean'], \n",
+    "                  c=cancer['diagnosis'].map(color_map), marker='o')\n",
+    "\n",
+    "# Define the new observation\n",
+    "new_observation = {'perimeter_mean': 97, 'concavity_mean': 0.20, 'symmetry_mean': 0.22}\n",
+    "\n",
+    "# Plot the new observation\n",
+    "ax.scatter3D(new_observation['perimeter_mean'], new_observation['concavity_mean'], \n",
+    "             new_observation['symmetry_mean'], color='red', edgecolor='black', \n",
+    "             s=100, marker='o', label='New Observation')\n",
+    "\n",
+    "# Add axis labels\n",
+    "ax.set_xlabel('Perimeter Mean')\n",
+    "ax.set_ylabel('Concavity Mean')\n",
+    "ax.set_zlabel('Symmetry Mean')\n",
+    "ax.set_title('3D Scatter Plot of Perimeter Mean, Concavity Mean, and Symmetry Mean')\n",
+    "\n",
+    "# Create custom legend handles\n",
+    "handles = [plt.Line2D([0], [0], marker='o', color='w', label=label,\n",
+    "                      markersize=10, markerfacecolor=color_map[label])\n",
+    "           for label in labels]\n",
+    "\n",
+    "# Add custom legend for new observation\n",
+    "handles.append(plt.Line2D([0], [0], marker='o', color='red', label='New Observation', \n",
+    "                          markersize=10, markeredgecolor='black'))\n",
+    "\n",
+    "# Add legend\n",
+    "plt.legend(handles=handles, title='Diagnosis')\n",
+    "\n",
+    "# Show plot\n",
+    "plt\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "99440348",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#we want to include a third feature\n",
+    "new_obs_Perimeter = 97\n",
+    "new_obs_Concavity = 0.20\n",
+    "new_obs_Symmetry = 0.22"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "f96a2810",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "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"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "cd63257d",
+   "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>25.800203</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>35.900199</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>33.000003</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>19.420085</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>38.100020</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>45.000046</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>34.200075</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>11.300676</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>43.100270</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>49.080446</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        25.800203  \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",
+       "\n",
+       "[569 rows x 33 columns]"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cancer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "d7f84804",
+   "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>291</th>\n",
+       "      <td>8915</td>\n",
+       "      <td>Benign</td>\n",
+       "      <td>14.96</td>\n",
+       "      <td>19.10</td>\n",
+       "      <td>97.03</td>\n",
+       "      <td>687.3</td>\n",
+       "      <td>0.08992</td>\n",
+       "      <td>0.09823</td>\n",
+       "      <td>0.05940</td>\n",
+       "      <td>0.04819</td>\n",
+       "      <td>...</td>\n",
+       "      <td>26.19</td>\n",
+       "      <td>109.1</td>\n",
+       "      <td>809.8</td>\n",
+       "      <td>0.1313</td>\n",
+       "      <td>0.3030</td>\n",
+       "      <td>0.1804</td>\n",
+       "      <td>0.1489</td>\n",
+       "      <td>0.2962</td>\n",
+       "      <td>0.08472</td>\n",
+       "      <td>0.147305</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>138</th>\n",
+       "      <td>868826</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>14.95</td>\n",
+       "      <td>17.57</td>\n",
+       "      <td>96.85</td>\n",
+       "      <td>678.1</td>\n",
+       "      <td>0.11670</td>\n",
+       "      <td>0.13050</td>\n",
+       "      <td>0.15390</td>\n",
+       "      <td>0.08624</td>\n",
+       "      <td>...</td>\n",
+       "      <td>21.43</td>\n",
+       "      <td>121.4</td>\n",
+       "      <td>971.4</td>\n",
+       "      <td>0.1411</td>\n",
+       "      <td>0.2164</td>\n",
+       "      <td>0.3355</td>\n",
+       "      <td>0.1667</td>\n",
+       "      <td>0.3414</td>\n",
+       "      <td>0.07147</td>\n",
+       "      <td>0.158795</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>84799002</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>14.54</td>\n",
+       "      <td>27.54</td>\n",
+       "      <td>96.73</td>\n",
+       "      <td>658.8</td>\n",
+       "      <td>0.11390</td>\n",
+       "      <td>0.15950</td>\n",
+       "      <td>0.16390</td>\n",
+       "      <td>0.07364</td>\n",
+       "      <td>...</td>\n",
+       "      <td>37.13</td>\n",
+       "      <td>124.1</td>\n",
+       "      <td>943.2</td>\n",
+       "      <td>0.1678</td>\n",
+       "      <td>0.6577</td>\n",
+       "      <td>0.7026</td>\n",
+       "      <td>0.1712</td>\n",
+       "      <td>0.4218</td>\n",
+       "      <td>0.13410</td>\n",
+       "      <td>0.272597</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>514</th>\n",
+       "      <td>91594602</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>15.05</td>\n",
+       "      <td>19.07</td>\n",
+       "      <td>97.26</td>\n",
+       "      <td>701.9</td>\n",
+       "      <td>0.09215</td>\n",
+       "      <td>0.08597</td>\n",
+       "      <td>0.07486</td>\n",
+       "      <td>0.04335</td>\n",
+       "      <td>...</td>\n",
+       "      <td>28.06</td>\n",
+       "      <td>113.8</td>\n",
+       "      <td>967.0</td>\n",
+       "      <td>0.1246</td>\n",
+       "      <td>0.2101</td>\n",
+       "      <td>0.2866</td>\n",
+       "      <td>0.1120</td>\n",
+       "      <td>0.2282</td>\n",
+       "      <td>0.06954</td>\n",
+       "      <td>0.295539</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54</th>\n",
+       "      <td>857438</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>15.10</td>\n",
+       "      <td>22.02</td>\n",
+       "      <td>97.26</td>\n",
+       "      <td>712.8</td>\n",
+       "      <td>0.09056</td>\n",
+       "      <td>0.07081</td>\n",
+       "      <td>0.05253</td>\n",
+       "      <td>0.03334</td>\n",
+       "      <td>...</td>\n",
+       "      <td>31.69</td>\n",
+       "      <td>117.7</td>\n",
+       "      <td>1030.0</td>\n",
+       "      <td>0.1389</td>\n",
+       "      <td>0.2057</td>\n",
+       "      <td>0.2712</td>\n",
+       "      <td>0.1530</td>\n",
+       "      <td>0.2675</td>\n",
+       "      <td>0.07873</td>\n",
+       "      <td>0.304562</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 33 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           id  diagnosis  radius_mean  texture_mean  perimeter_mean  \\\n",
+       "291      8915     Benign        14.96         19.10           97.03   \n",
+       "138    868826  Malignant        14.95         17.57           96.85   \n",
+       "15   84799002  Malignant        14.54         27.54           96.73   \n",
+       "514  91594602  Malignant        15.05         19.07           97.26   \n",
+       "54     857438  Malignant        15.10         22.02           97.26   \n",
+       "\n",
+       "     area_mean  smoothness_mean  compactness_mean  concavity_mean  \\\n",
+       "291      687.3          0.08992           0.09823         0.05940   \n",
+       "138      678.1          0.11670           0.13050         0.15390   \n",
+       "15       658.8          0.11390           0.15950         0.16390   \n",
+       "514      701.9          0.09215           0.08597         0.07486   \n",
+       "54       712.8          0.09056           0.07081         0.05253   \n",
+       "\n",
+       "     concave points_mean  ...  texture_worst  perimeter_worst  area_worst  \\\n",
+       "291              0.04819  ...          26.19            109.1       809.8   \n",
+       "138              0.08624  ...          21.43            121.4       971.4   \n",
+       "15               0.07364  ...          37.13            124.1       943.2   \n",
+       "514              0.04335  ...          28.06            113.8       967.0   \n",
+       "54               0.03334  ...          31.69            117.7      1030.0   \n",
+       "\n",
+       "     smoothness_worst  compactness_worst  concavity_worst  \\\n",
+       "291            0.1313             0.3030           0.1804   \n",
+       "138            0.1411             0.2164           0.3355   \n",
+       "15             0.1678             0.6577           0.7026   \n",
+       "514            0.1246             0.2101           0.2866   \n",
+       "54             0.1389             0.2057           0.2712   \n",
+       "\n",
+       "     concave points_worst  symmetry_worst  fractal_dimension_worst  \\\n",
+       "291                0.1489          0.2962                  0.08472   \n",
+       "138                0.1667          0.3414                  0.07147   \n",
+       "15                 0.1712          0.4218                  0.13410   \n",
+       "514                0.1120          0.2282                  0.06954   \n",
+       "54                 0.1530          0.2675                  0.07873   \n",
+       "\n",
+       "     dist_from_new  \n",
+       "291       0.147305  \n",
+       "138       0.158795  \n",
+       "15        0.272597  \n",
+       "514       0.295539  \n",
+       "54        0.304562  \n",
+       "\n",
+       "[5 rows x 33 columns]"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# K = 5\n",
+    "cancer.nsmallest(5, \"dist_from_new\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "1af840a3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn import set_config\n",
+    "#output data frame instead of arrays\n",
+    "set_config(transform_output=\"pandas\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "3c7203c2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.neighbors import KNeighborsClassifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e31e52f7",
+   "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": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#ONLY select relevants columns\n",
+    "cancer_train = cancer[[\"diagnosis\", \"perimeter_mean\", \"concavity_mean\"]]\n",
+    "cancer_train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "09e283af",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Step 1. Initialize our model\n",
+    "knn = KNeighborsClassifier(n_neighbors=5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "d1b63dc7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Step 2 Define our predictors and response variable\n",
+    "X = cancer_train[[\"perimeter_mean\", \"concavity_mean\"]]\n",
+    "y = cancer_train[\"diagnosis\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "18ad1f8a",
+   "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",
+       "\n",
+       "#sk-container-id-1.light {\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: black;\n",
+       "  --sklearn-color-background: white;\n",
+       "  --sklearn-color-border-box: black;\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1.dark {\n",
+       "  --sklearn-color-text-on-default-background: white;\n",
+       "  --sklearn-color-background: #111;\n",
+       "  --sklearn-color-border-box: white;\n",
+       "  --sklearn-color-icon: #878787;\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: center;\n",
+       "  justify-content: center;\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",
+       "  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-unfitted-level-0);\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-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-3);\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",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-3);\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",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-0);\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",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-0);\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-unfitted-level-0);\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",
+       "  background-color: var(--sklearn-color-fitted-level-0);\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 {\n",
+       "    font-family: monospace;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary::marker {\n",
+       "    font-size: 0.7rem;\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",
+       "    margin-top: 0;\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",
+       "/*\n",
+       "    `table td`is set in notebook with right text-align.\n",
+       "    We need to overwrite it.\n",
+       "*/\n",
+       ".estimator-table table td.param {\n",
+       "    text-align: left;\n",
+       "    position: relative;\n",
+       "    padding: 0;\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    background-color: transparent;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    Styles for parameter documentation links\n",
+       "    We need styling for visited so jupyter doesn't overwrite it\n",
+       "*/\n",
+       "a.param-doc-link,\n",
+       "a.param-doc-link:link,\n",
+       "a.param-doc-link:visited {\n",
+       "    text-decoration: underline dashed;\n",
+       "    text-underline-offset: .3em;\n",
+       "    color: inherit;\n",
+       "    display: block;\n",
+       "    padding: .5em;\n",
+       "}\n",
+       "\n",
+       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
+       "a.param-doc-link::before {\n",
+       "    position: absolute;\n",
+       "    content: \"\";\n",
+       "    inset: 0;\n",
+       "}\n",
+       "\n",
+       ".param-doc-description {\n",
+       "    display: none;\n",
+       "    position: absolute;\n",
+       "    z-index: 9999;\n",
+       "    left: 0;\n",
+       "    padding: .5ex;\n",
+       "    margin-left: 1.5em;\n",
+       "    color: var(--sklearn-color-text);\n",
+       "    box-shadow: .3em .3em .4em #999;\n",
+       "    width: max-content;\n",
+       "    text-align: left;\n",
+       "    max-height: 10em;\n",
+       "    overflow-y: auto;\n",
+       "\n",
+       "    /* unfitted */\n",
+       "    background: var(--sklearn-color-unfitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* Fitted state for parameter tooltips */\n",
+       ".fitted .param-doc-description {\n",
+       "    /* fitted */\n",
+       "    background: var(--sklearn-color-fitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".param-doc-link:hover .param-doc-description {\n",
+       "    display: block;\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 fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>KNeighborsClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/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",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=n_neighbors,-int%2C%20default%3D5\">\n",
+       "            n_neighbors\n",
+       "            <span class=\"param-doc-description\">n_neighbors: int, default=5<br><br>Number of neighbors to use by default for :meth:`kneighbors` queries.</span>\n",
+       "        </a>\n",
+       "    </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\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=weights,-%7B%27uniform%27%2C%20%27distance%27%7D%2C%20callable%20or%20None%2C%20default%3D%27uniform%27\">\n",
+       "            weights\n",
+       "            <span class=\"param-doc-description\">weights: {'uniform', 'distance'}, callable or None, default='uniform'<br><br>Weight function used in prediction.  Possible values:<br><br>- 'uniform' : uniform weights.  All points in each neighborhood<br>  are weighted equally.<br>- 'distance' : weight points by the inverse of their distance.<br>  in this case, closer neighbors of a query point will have a<br>  greater influence than neighbors which are further away.<br>- [callable] : a user-defined function which accepts an<br>  array of distances, and returns an array of the same shape<br>  containing the weights.<br><br>Refer to the example entitled<br>:ref:`sphx_glr_auto_examples_neighbors_plot_classification.py`<br>showing the impact of the `weights` parameter on the decision<br>boundary.</span>\n",
+       "        </a>\n",
+       "    </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\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=algorithm,-%7B%27auto%27%2C%20%27ball_tree%27%2C%20%27kd_tree%27%2C%20%27brute%27%7D%2C%20default%3D%27auto%27\">\n",
+       "            algorithm\n",
+       "            <span class=\"param-doc-description\">algorithm: {'auto', 'ball_tree', 'kd_tree', 'brute'}, default='auto'<br><br>Algorithm used to compute the nearest neighbors:<br><br>- 'ball_tree' will use :class:`BallTree`<br>- 'kd_tree' will use :class:`KDTree`<br>- 'brute' will use a brute-force search.<br>- 'auto' will attempt to decide the most appropriate algorithm<br>  based on the values passed to :meth:`fit` method.<br><br>Note: fitting on sparse input will override the setting of<br>this parameter, using brute force.</span>\n",
+       "        </a>\n",
+       "    </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\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=leaf_size,-int%2C%20default%3D30\">\n",
+       "            leaf_size\n",
+       "            <span class=\"param-doc-description\">leaf_size: int, default=30<br><br>Leaf size passed to BallTree or KDTree.  This can affect the<br>speed of the construction and query, as well as the memory<br>required to store the tree.  The optimal value depends on the<br>nature of the problem.</span>\n",
+       "        </a>\n",
+       "    </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\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=p,-float%2C%20default%3D2\">\n",
+       "            p\n",
+       "            <span class=\"param-doc-description\">p: float, default=2<br><br>Power parameter for the Minkowski metric. When p = 1, this is equivalent<br>to using manhattan_distance (l1), and euclidean_distance (l2) for p = 2.<br>For arbitrary p, minkowski_distance (l_p) is used. This parameter is expected<br>to be positive.</span>\n",
+       "        </a>\n",
+       "    </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\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=metric,-str%20or%20callable%2C%20default%3D%27minkowski%27\">\n",
+       "            metric\n",
+       "            <span class=\"param-doc-description\">metric: str or callable, default='minkowski'<br><br>Metric to use for distance computation. Default is \"minkowski\", which<br>results in the standard Euclidean distance when p = 2. See the<br>documentation of `scipy.spatial.distance<br><https://docs.scipy.org/doc/scipy/reference/spatial.distance.html>`_ and<br>the metrics listed in<br>:class:`~sklearn.metrics.pairwise.distance_metrics` for valid metric<br>values.<br><br>If metric is \"precomputed\", X is assumed to be a distance matrix and<br>must be square during fit. X may be a :term:`sparse graph`, in which<br>case only \"nonzero\" elements may be considered neighbors.<br><br>If metric is a callable function, it takes two arrays representing 1D<br>vectors as inputs and must return one value indicating the distance<br>between those vectors. This works for Scipy's metrics, but is less<br>efficient than passing the metric name as a string.</span>\n",
+       "        </a>\n",
+       "    </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\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=metric_params,-dict%2C%20default%3DNone\">\n",
+       "            metric_params\n",
+       "            <span class=\"param-doc-description\">metric_params: dict, default=None<br><br>Additional keyword arguments for the metric function.</span>\n",
+       "        </a>\n",
+       "    </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",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
+       "            n_jobs\n",
+       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>The number of parallel jobs to run for neighbors search.<br>``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.<br>``-1`` means using all processors. See :term:`Glossary <n_jobs>`<br>for more details.<br>Doesn't affect :meth:`fit` method.</span>\n",
+       "        </a>\n",
+       "    </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('.copy-paste-icon').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling\n",
+       "        .textContent.trim().split(' ')[0];\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "\n",
+       "\n",
+       "/**\n",
+       " * Adapted from Skrub\n",
+       " * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
+       " * @returns \"light\" or \"dark\"\n",
+       " */\n",
+       "function detectTheme(element) {\n",
+       "    const body = document.querySelector('body');\n",
+       "\n",
+       "    // Check VSCode theme\n",
+       "    const themeKindAttr = body.getAttribute('data-vscode-theme-kind');\n",
+       "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
+       "\n",
+       "    if (themeKindAttr && themeNameAttr) {\n",
+       "        const themeKind = themeKindAttr.toLowerCase();\n",
+       "        const themeName = themeNameAttr.toLowerCase();\n",
+       "\n",
+       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
+       "            return \"dark\";\n",
+       "        }\n",
+       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
+       "            return \"light\";\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    // Check Jupyter theme\n",
+       "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
+       "        return 'dark';\n",
+       "    } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
+       "        return 'light';\n",
+       "    }\n",
+       "\n",
+       "    // Guess based on a parent element's color\n",
+       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
+       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
+       "    if (match) {\n",
+       "        const [r, g, b] = [\n",
+       "            parseFloat(match[1]),\n",
+       "            parseFloat(match[2]),\n",
+       "            parseFloat(match[3])\n",
+       "        ];\n",
+       "\n",
+       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
+       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
+       "\n",
+       "        if (luma > 180) {\n",
+       "            // If the text is very bright we have a dark theme\n",
+       "            return 'dark';\n",
+       "        }\n",
+       "        if (luma < 75) {\n",
+       "            // If the text is very dark we have a light theme\n",
+       "            return 'light';\n",
+       "        }\n",
+       "        // Otherwise fall back to the next heuristic.\n",
+       "    }\n",
+       "\n",
+       "    // Fallback to system preference\n",
+       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
+       "}\n",
+       "\n",
+       "\n",
+       "function forceTheme(elementId) {\n",
+       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
+       "    if (estimatorElement === null) {\n",
+       "        console.error(`Element with id ${elementId} not found.`);\n",
+       "    } else {\n",
+       "        const theme = detectTheme(estimatorElement);\n",
+       "        estimatorElement.classList.add(theme);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "forceTheme('sk-container-id-1');</script></body>"
+      ],
+      "text/plain": [
+       "KNeighborsClassifier()"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Step 3. Fit our model to the data\n",
+    "knn.fit(X,y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "79c958f1",
+   "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": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "new_obs = pd.DataFrame({\"perimeter_mean\" : [97], \"concavity_mean\" : [0.2]})\n",
+    "new_obs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "080fa894",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Malignant'], dtype=object)"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Step 4. Predict our new obs\n",
+    "knn.predict(new_obs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ae44fad5",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "lcr-env",
+   "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": "3.11.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/01_materials/notebooks/classwork_dec 12.ipynb b/01_materials/notebooks/classwork_dec 12.ipynb
new file mode 100644
index 000000000..df73b2edc
--- /dev/null
+++ b/01_materials/notebooks/classwork_dec 12.ipynb	
@@ -0,0 +1,5579 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "434623a9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load in our libraries\n",
+    "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": "73dd86a7",
+   "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": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# read in our data set\n",
+    "cancer = pd.read_csv(\"dataset/wdbc.csv\")\n",
+    "cancer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "967a406f",
+   "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>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": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cancer[\"diagnosis\"] =  cancer[\"diagnosis\"].replace({\n",
+    "    \"M\" : \"Malignant\",\n",
+    "    \"B\" : \"Benign\"\n",
+    "})\n",
+    "cancer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "caf91cfa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "standardized_cancer = cancer.copy() #make a copy of our data\n",
+    "columns_to_exclude = ['id','diagnosis'] # columns we dont want to scale\n",
+    "columns_to_scale = standardized_cancer.columns.difference(columns_to_exclude)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "3c8da954",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scaler = StandardScaler() #initialze our scaler\n",
+    "standardized_cancer[columns_to_scale] = scaler.fit_transform(standardized_cancer[columns_to_scale])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "c13bb3f6",
+   "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": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "standardized_cancer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "920af663",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# set the seed\n",
+    "np.random.seed(1)\n",
+    "\n",
+    "\n",
+    "# split the data\n",
+    "cancer_train, cancer_test = train_test_split(standardized_cancer, train_size= 0.75, shuffle= True, stratify= standardized_cancer[\"diagnosis\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "14e72efb",
+   "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": 9,
+   "id": "79d6d6a5",
+   "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": 10,
+   "id": "5c8b6ec2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# k = 5, 5 nearest neighbours\n",
+    "knn = KNeighborsClassifier(n_neighbors= 5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "8831ac99",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# define predictors and response variables\n",
+    "# step 2. define our X and y\n",
+    "X = cancer_train[[\"perimeter_mean\", \"concavity_mean\"]]\n",
+    "y = cancer_train[\"diagnosis\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "3d24365a",
+   "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",
+       "\n",
+       "#sk-container-id-1.light {\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: black;\n",
+       "  --sklearn-color-background: white;\n",
+       "  --sklearn-color-border-box: black;\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1.dark {\n",
+       "  --sklearn-color-text-on-default-background: white;\n",
+       "  --sklearn-color-background: #111;\n",
+       "  --sklearn-color-border-box: white;\n",
+       "  --sklearn-color-icon: #878787;\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: center;\n",
+       "  justify-content: center;\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",
+       "  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-unfitted-level-0);\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-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-3);\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",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-3);\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",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-0);\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",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-0);\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-unfitted-level-0);\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",
+       "  background-color: var(--sklearn-color-fitted-level-0);\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 {\n",
+       "    font-family: monospace;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary::marker {\n",
+       "    font-size: 0.7rem;\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",
+       "    margin-top: 0;\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",
+       "/*\n",
+       "    `table td`is set in notebook with right text-align.\n",
+       "    We need to overwrite it.\n",
+       "*/\n",
+       ".estimator-table table td.param {\n",
+       "    text-align: left;\n",
+       "    position: relative;\n",
+       "    padding: 0;\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    background-color: transparent;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    Styles for parameter documentation links\n",
+       "    We need styling for visited so jupyter doesn't overwrite it\n",
+       "*/\n",
+       "a.param-doc-link,\n",
+       "a.param-doc-link:link,\n",
+       "a.param-doc-link:visited {\n",
+       "    text-decoration: underline dashed;\n",
+       "    text-underline-offset: .3em;\n",
+       "    color: inherit;\n",
+       "    display: block;\n",
+       "    padding: .5em;\n",
+       "}\n",
+       "\n",
+       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
+       "a.param-doc-link::before {\n",
+       "    position: absolute;\n",
+       "    content: \"\";\n",
+       "    inset: 0;\n",
+       "}\n",
+       "\n",
+       ".param-doc-description {\n",
+       "    display: none;\n",
+       "    position: absolute;\n",
+       "    z-index: 9999;\n",
+       "    left: 0;\n",
+       "    padding: .5ex;\n",
+       "    margin-left: 1.5em;\n",
+       "    color: var(--sklearn-color-text);\n",
+       "    box-shadow: .3em .3em .4em #999;\n",
+       "    width: max-content;\n",
+       "    text-align: left;\n",
+       "    max-height: 10em;\n",
+       "    overflow-y: auto;\n",
+       "\n",
+       "    /* unfitted */\n",
+       "    background: var(--sklearn-color-unfitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* Fitted state for parameter tooltips */\n",
+       ".fitted .param-doc-description {\n",
+       "    /* fitted */\n",
+       "    background: var(--sklearn-color-fitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".param-doc-link:hover .param-doc-description {\n",
+       "    display: block;\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 fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>KNeighborsClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/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",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=n_neighbors,-int%2C%20default%3D5\">\n",
+       "            n_neighbors\n",
+       "            <span class=\"param-doc-description\">n_neighbors: int, default=5<br><br>Number of neighbors to use by default for :meth:`kneighbors` queries.</span>\n",
+       "        </a>\n",
+       "    </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\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=weights,-%7B%27uniform%27%2C%20%27distance%27%7D%2C%20callable%20or%20None%2C%20default%3D%27uniform%27\">\n",
+       "            weights\n",
+       "            <span class=\"param-doc-description\">weights: {'uniform', 'distance'}, callable or None, default='uniform'<br><br>Weight function used in prediction.  Possible values:<br><br>- 'uniform' : uniform weights.  All points in each neighborhood<br>  are weighted equally.<br>- 'distance' : weight points by the inverse of their distance.<br>  in this case, closer neighbors of a query point will have a<br>  greater influence than neighbors which are further away.<br>- [callable] : a user-defined function which accepts an<br>  array of distances, and returns an array of the same shape<br>  containing the weights.<br><br>Refer to the example entitled<br>:ref:`sphx_glr_auto_examples_neighbors_plot_classification.py`<br>showing the impact of the `weights` parameter on the decision<br>boundary.</span>\n",
+       "        </a>\n",
+       "    </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\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=algorithm,-%7B%27auto%27%2C%20%27ball_tree%27%2C%20%27kd_tree%27%2C%20%27brute%27%7D%2C%20default%3D%27auto%27\">\n",
+       "            algorithm\n",
+       "            <span class=\"param-doc-description\">algorithm: {'auto', 'ball_tree', 'kd_tree', 'brute'}, default='auto'<br><br>Algorithm used to compute the nearest neighbors:<br><br>- 'ball_tree' will use :class:`BallTree`<br>- 'kd_tree' will use :class:`KDTree`<br>- 'brute' will use a brute-force search.<br>- 'auto' will attempt to decide the most appropriate algorithm<br>  based on the values passed to :meth:`fit` method.<br><br>Note: fitting on sparse input will override the setting of<br>this parameter, using brute force.</span>\n",
+       "        </a>\n",
+       "    </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\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=leaf_size,-int%2C%20default%3D30\">\n",
+       "            leaf_size\n",
+       "            <span class=\"param-doc-description\">leaf_size: int, default=30<br><br>Leaf size passed to BallTree or KDTree.  This can affect the<br>speed of the construction and query, as well as the memory<br>required to store the tree.  The optimal value depends on the<br>nature of the problem.</span>\n",
+       "        </a>\n",
+       "    </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\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=p,-float%2C%20default%3D2\">\n",
+       "            p\n",
+       "            <span class=\"param-doc-description\">p: float, default=2<br><br>Power parameter for the Minkowski metric. When p = 1, this is equivalent<br>to using manhattan_distance (l1), and euclidean_distance (l2) for p = 2.<br>For arbitrary p, minkowski_distance (l_p) is used. This parameter is expected<br>to be positive.</span>\n",
+       "        </a>\n",
+       "    </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\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=metric,-str%20or%20callable%2C%20default%3D%27minkowski%27\">\n",
+       "            metric\n",
+       "            <span class=\"param-doc-description\">metric: str or callable, default='minkowski'<br><br>Metric to use for distance computation. Default is \"minkowski\", which<br>results in the standard Euclidean distance when p = 2. See the<br>documentation of `scipy.spatial.distance<br><https://docs.scipy.org/doc/scipy/reference/spatial.distance.html>`_ and<br>the metrics listed in<br>:class:`~sklearn.metrics.pairwise.distance_metrics` for valid metric<br>values.<br><br>If metric is \"precomputed\", X is assumed to be a distance matrix and<br>must be square during fit. X may be a :term:`sparse graph`, in which<br>case only \"nonzero\" elements may be considered neighbors.<br><br>If metric is a callable function, it takes two arrays representing 1D<br>vectors as inputs and must return one value indicating the distance<br>between those vectors. This works for Scipy's metrics, but is less<br>efficient than passing the metric name as a string.</span>\n",
+       "        </a>\n",
+       "    </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\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=metric_params,-dict%2C%20default%3DNone\">\n",
+       "            metric_params\n",
+       "            <span class=\"param-doc-description\">metric_params: dict, default=None<br><br>Additional keyword arguments for the metric function.</span>\n",
+       "        </a>\n",
+       "    </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",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
+       "            n_jobs\n",
+       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>The number of parallel jobs to run for neighbors search.<br>``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.<br>``-1`` means using all processors. See :term:`Glossary <n_jobs>`<br>for more details.<br>Doesn't affect :meth:`fit` method.</span>\n",
+       "        </a>\n",
+       "    </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}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling\n",
+       "        .textContent.trim().split(' ')[0];\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "\n",
+       "\n",
+       "/**\n",
+       " * Adapted from Skrub\n",
+       " * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
+       " * @returns \"light\" or \"dark\"\n",
+       " */\n",
+       "function detectTheme(element) {\n",
+       "    const body = document.querySelector('body');\n",
+       "\n",
+       "    // Check VSCode theme\n",
+       "    const themeKindAttr = body.getAttribute('data-vscode-theme-kind');\n",
+       "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
+       "\n",
+       "    if (themeKindAttr && themeNameAttr) {\n",
+       "        const themeKind = themeKindAttr.toLowerCase();\n",
+       "        const themeName = themeNameAttr.toLowerCase();\n",
+       "\n",
+       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
+       "            return \"dark\";\n",
+       "        }\n",
+       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
+       "            return \"light\";\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    // Check Jupyter theme\n",
+       "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
+       "        return 'dark';\n",
+       "    } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
+       "        return 'light';\n",
+       "    }\n",
+       "\n",
+       "    // Guess based on a parent element's color\n",
+       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
+       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
+       "    if (match) {\n",
+       "        const [r, g, b] = [\n",
+       "            parseFloat(match[1]),\n",
+       "            parseFloat(match[2]),\n",
+       "            parseFloat(match[3])\n",
+       "        ];\n",
+       "\n",
+       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
+       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
+       "\n",
+       "        if (luma > 180) {\n",
+       "            // If the text is very bright we have a dark theme\n",
+       "            return 'dark';\n",
+       "        }\n",
+       "        if (luma < 75) {\n",
+       "            // If the text is very dark we have a light theme\n",
+       "            return 'light';\n",
+       "        }\n",
+       "        // Otherwise fall back to the next heuristic.\n",
+       "    }\n",
+       "\n",
+       "    // Fallback to system preference\n",
+       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
+       "}\n",
+       "\n",
+       "\n",
+       "function forceTheme(elementId) {\n",
+       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
+       "    if (estimatorElement === null) {\n",
+       "        console.error(`Element with id ${elementId} not found.`);\n",
+       "    } else {\n",
+       "        const theme = detectTheme(estimatorElement);\n",
+       "        estimatorElement.classList.add(theme);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "forceTheme('sk-container-id-1');</script></body>"
+      ],
+      "text/plain": [
+       "KNeighborsClassifier()"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# learn from training data\n",
+    "#step 3. fit model to the train data\n",
+    "knn.fit(X,y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "34b4985f",
+   "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>predicited</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>357</th>\n",
+       "      <td>901028</td>\n",
+       "      <td>Benign</td>\n",
+       "      <td>-0.073075</td>\n",
+       "      <td>-0.716655</td>\n",
+       "      <td>-0.142066</td>\n",
+       "      <td>-0.174028</td>\n",
+       "      <td>-0.635527</td>\n",
+       "      <td>-0.936601</td>\n",
+       "      <td>-0.926297</td>\n",
+       "      <td>-0.723241</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.015832</td>\n",
+       "      <td>-0.313383</td>\n",
+       "      <td>-0.327294</td>\n",
+       "      <td>-0.748217</td>\n",
+       "      <td>-0.976252</td>\n",
+       "      <td>-1.052282</td>\n",
+       "      <td>-0.899073</td>\n",
+       "      <td>-0.871589</td>\n",
+       "      <td>-0.710199</td>\n",
+       "      <td>Benign</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>361</th>\n",
+       "      <td>901041</td>\n",
+       "      <td>Benign</td>\n",
+       "      <td>-0.234963</td>\n",
+       "      <td>0.530653</td>\n",
+       "      <td>-0.277170</td>\n",
+       "      <td>-0.309407</td>\n",
+       "      <td>-0.750104</td>\n",
+       "      <td>-0.769638</td>\n",
+       "      <td>-0.695035</td>\n",
+       "      <td>-0.636573</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.573662</td>\n",
+       "      <td>-0.426569</td>\n",
+       "      <td>-0.455973</td>\n",
+       "      <td>-0.805204</td>\n",
+       "      <td>-0.557036</td>\n",
+       "      <td>-0.724371</td>\n",
+       "      <td>-0.890242</td>\n",
+       "      <td>-0.426699</td>\n",
+       "      <td>-0.962341</td>\n",
+       "      <td>Benign</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>212</th>\n",
+       "      <td>8810703</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>3.971288</td>\n",
+       "      <td>-0.190738</td>\n",
+       "      <td>3.976130</td>\n",
+       "      <td>5.244841</td>\n",
+       "      <td>1.269571</td>\n",
+       "      <td>0.895628</td>\n",
+       "      <td>2.903973</td>\n",
+       "      <td>2.852321</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-1.173652</td>\n",
+       "      <td>2.419765</td>\n",
+       "      <td>2.845036</td>\n",
+       "      <td>-0.796437</td>\n",
+       "      <td>-0.653093</td>\n",
+       "      <td>0.229857</td>\n",
+       "      <td>0.683579</td>\n",
+       "      <td>-2.026684</td>\n",
+       "      <td>-1.590202</td>\n",
+       "      <td>Malignant</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>527</th>\n",
+       "      <td>91813702</td>\n",
+       "      <td>Benign</td>\n",
+       "      <td>-0.507616</td>\n",
+       "      <td>-1.633519</td>\n",
+       "      <td>-0.536668</td>\n",
+       "      <td>-0.530110</td>\n",
+       "      <td>-0.450497</td>\n",
+       "      <td>-0.782146</td>\n",
+       "      <td>-0.743497</td>\n",
+       "      <td>-0.579053</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-1.043377</td>\n",
+       "      <td>-0.596944</td>\n",
+       "      <td>-0.554943</td>\n",
+       "      <td>-0.138898</td>\n",
+       "      <td>-0.298127</td>\n",
+       "      <td>-0.446594</td>\n",
+       "      <td>-0.115817</td>\n",
+       "      <td>0.338511</td>\n",
+       "      <td>-0.444757</td>\n",
+       "      <td>Benign</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>8510824</td>\n",
+       "      <td>Benign</td>\n",
+       "      <td>-1.313080</td>\n",
+       "      <td>-1.593959</td>\n",
+       "      <td>-1.302806</td>\n",
+       "      <td>-1.083572</td>\n",
+       "      <td>0.429819</td>\n",
+       "      <td>-0.747086</td>\n",
+       "      <td>-0.743748</td>\n",
+       "      <td>-0.726337</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-1.631243</td>\n",
+       "      <td>-1.254913</td>\n",
+       "      <td>-0.994422</td>\n",
+       "      <td>0.001377</td>\n",
+       "      <td>-0.887193</td>\n",
+       "      <td>-0.880434</td>\n",
+       "      <td>-0.796903</td>\n",
+       "      <td>-0.729224</td>\n",
+       "      <td>-0.344455</td>\n",
+       "      <td>Benign</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>364</th>\n",
+       "      <td>9010877</td>\n",
+       "      <td>Benign</td>\n",
+       "      <td>-0.206561</td>\n",
+       "      <td>-0.544452</td>\n",
+       "      <td>-0.267285</td>\n",
+       "      <td>-0.291490</td>\n",
+       "      <td>-1.209121</td>\n",
+       "      <td>-0.897940</td>\n",
+       "      <td>-0.841049</td>\n",
+       "      <td>-0.881874</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.647666</td>\n",
+       "      <td>-0.402145</td>\n",
+       "      <td>-0.381613</td>\n",
+       "      <td>-0.485202</td>\n",
+       "      <td>-0.551311</td>\n",
+       "      <td>-0.651448</td>\n",
+       "      <td>-0.681180</td>\n",
+       "      <td>-0.258450</td>\n",
+       "      <td>-0.450299</td>\n",
+       "      <td>Benign</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>434</th>\n",
+       "      <td>908469</td>\n",
+       "      <td>Benign</td>\n",
+       "      <td>0.208100</td>\n",
+       "      <td>-0.546779</td>\n",
+       "      <td>0.120315</td>\n",
+       "      <td>0.053500</td>\n",
+       "      <td>-0.506718</td>\n",
+       "      <td>-0.636788</td>\n",
+       "      <td>-0.694784</td>\n",
+       "      <td>-0.519727</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.836565</td>\n",
+       "      <td>-0.147774</td>\n",
+       "      <td>-0.181211</td>\n",
+       "      <td>-0.463284</td>\n",
+       "      <td>-0.631464</td>\n",
+       "      <td>-0.720533</td>\n",
+       "      <td>-0.531351</td>\n",
+       "      <td>-0.607890</td>\n",
+       "      <td>-0.868688</td>\n",
+       "      <td>Benign</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>299</th>\n",
+       "      <td>892399</td>\n",
+       "      <td>Benign</td>\n",
+       "      <td>-1.027362</td>\n",
+       "      <td>0.884367</td>\n",
+       "      <td>-1.034658</td>\n",
+       "      <td>-0.912073</td>\n",
+       "      <td>0.365770</td>\n",
+       "      <td>-0.689284</td>\n",
+       "      <td>-0.801626</td>\n",
+       "      <td>-0.778182</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.237300</td>\n",
+       "      <td>-1.106878</td>\n",
+       "      <td>-0.910393</td>\n",
+       "      <td>-0.792053</td>\n",
+       "      <td>-1.069510</td>\n",
+       "      <td>-1.106350</td>\n",
+       "      <td>-1.269232</td>\n",
+       "      <td>-1.089989</td>\n",
+       "      <td>-0.896396</td>\n",
+       "      <td>Benign</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>488</th>\n",
+       "      <td>913512</td>\n",
+       "      <td>Benign</td>\n",
+       "      <td>-0.695066</td>\n",
+       "      <td>-0.725963</td>\n",
+       "      <td>-0.678775</td>\n",
+       "      <td>-0.666627</td>\n",
+       "      <td>1.169940</td>\n",
+       "      <td>-0.221940</td>\n",
+       "      <td>-0.577646</td>\n",
+       "      <td>-0.453952</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.665579</td>\n",
+       "      <td>-0.616305</td>\n",
+       "      <td>-0.581488</td>\n",
+       "      <td>0.886862</td>\n",
+       "      <td>-0.677903</td>\n",
+       "      <td>-0.591000</td>\n",
+       "      <td>-0.250572</td>\n",
+       "      <td>-0.156530</td>\n",
+       "      <td>-0.205361</td>\n",
+       "      <td>Benign</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>332</th>\n",
+       "      <td>897132</td>\n",
+       "      <td>Benign</td>\n",
+       "      <td>-0.825712</td>\n",
+       "      <td>0.132725</td>\n",
+       "      <td>-0.825000</td>\n",
+       "      <td>-0.761051</td>\n",
+       "      <td>0.643316</td>\n",
+       "      <td>-0.692695</td>\n",
+       "      <td>-1.052023</td>\n",
+       "      <td>-1.066224</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.016737</td>\n",
+       "      <td>-0.904036</td>\n",
+       "      <td>-0.781363</td>\n",
+       "      <td>0.439736</td>\n",
+       "      <td>-1.002397</td>\n",
+       "      <td>-1.241784</td>\n",
+       "      <td>-1.437181</td>\n",
+       "      <td>0.632947</td>\n",
+       "      <td>-1.037706</td>\n",
+       "      <td>Benign</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>143 rows × 33 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           id  diagnosis  radius_mean  texture_mean  perimeter_mean  \\\n",
+       "357    901028     Benign    -0.073075     -0.716655       -0.142066   \n",
+       "361    901041     Benign    -0.234963      0.530653       -0.277170   \n",
+       "212   8810703  Malignant     3.971288     -0.190738        3.976130   \n",
+       "527  91813702     Benign    -0.507616     -1.633519       -0.536668   \n",
+       "21    8510824     Benign    -1.313080     -1.593959       -1.302806   \n",
+       "..        ...        ...          ...           ...             ...   \n",
+       "364   9010877     Benign    -0.206561     -0.544452       -0.267285   \n",
+       "434    908469     Benign     0.208100     -0.546779        0.120315   \n",
+       "299    892399     Benign    -1.027362      0.884367       -1.034658   \n",
+       "488    913512     Benign    -0.695066     -0.725963       -0.678775   \n",
+       "332    897132     Benign    -0.825712      0.132725       -0.825000   \n",
+       "\n",
+       "     area_mean  smoothness_mean  compactness_mean  concavity_mean  \\\n",
+       "357  -0.174028        -0.635527         -0.936601       -0.926297   \n",
+       "361  -0.309407        -0.750104         -0.769638       -0.695035   \n",
+       "212   5.244841         1.269571          0.895628        2.903973   \n",
+       "527  -0.530110        -0.450497         -0.782146       -0.743497   \n",
+       "21   -1.083572         0.429819         -0.747086       -0.743748   \n",
+       "..         ...              ...               ...             ...   \n",
+       "364  -0.291490        -1.209121         -0.897940       -0.841049   \n",
+       "434   0.053500        -0.506718         -0.636788       -0.694784   \n",
+       "299  -0.912073         0.365770         -0.689284       -0.801626   \n",
+       "488  -0.666627         1.169940         -0.221940       -0.577646   \n",
+       "332  -0.761051         0.643316         -0.692695       -1.052023   \n",
+       "\n",
+       "     concave points_mean  ...  texture_worst  perimeter_worst  area_worst  \\\n",
+       "357            -0.723241  ...      -0.015832        -0.313383   -0.327294   \n",
+       "361            -0.636573  ...       0.573662        -0.426569   -0.455973   \n",
+       "212             2.852321  ...      -1.173652         2.419765    2.845036   \n",
+       "527            -0.579053  ...      -1.043377        -0.596944   -0.554943   \n",
+       "21             -0.726337  ...      -1.631243        -1.254913   -0.994422   \n",
+       "..                   ...  ...            ...              ...         ...   \n",
+       "364            -0.881874  ...      -0.647666        -0.402145   -0.381613   \n",
+       "434            -0.519727  ...      -0.836565        -0.147774   -0.181211   \n",
+       "299            -0.778182  ...      -0.237300        -1.106878   -0.910393   \n",
+       "488            -0.453952  ...      -0.665579        -0.616305   -0.581488   \n",
+       "332            -1.066224  ...       0.016737        -0.904036   -0.781363   \n",
+       "\n",
+       "     smoothness_worst  compactness_worst  concavity_worst  \\\n",
+       "357         -0.748217          -0.976252        -1.052282   \n",
+       "361         -0.805204          -0.557036        -0.724371   \n",
+       "212         -0.796437          -0.653093         0.229857   \n",
+       "527         -0.138898          -0.298127        -0.446594   \n",
+       "21           0.001377          -0.887193        -0.880434   \n",
+       "..                ...                ...              ...   \n",
+       "364         -0.485202          -0.551311        -0.651448   \n",
+       "434         -0.463284          -0.631464        -0.720533   \n",
+       "299         -0.792053          -1.069510        -1.106350   \n",
+       "488          0.886862          -0.677903        -0.591000   \n",
+       "332          0.439736          -1.002397        -1.241784   \n",
+       "\n",
+       "     concave points_worst  symmetry_worst  fractal_dimension_worst  predicited  \n",
+       "357             -0.899073       -0.871589                -0.710199      Benign  \n",
+       "361             -0.890242       -0.426699                -0.962341      Benign  \n",
+       "212              0.683579       -2.026684                -1.590202   Malignant  \n",
+       "527             -0.115817        0.338511                -0.444757      Benign  \n",
+       "21              -0.796903       -0.729224                -0.344455      Benign  \n",
+       "..                    ...             ...                      ...         ...  \n",
+       "364             -0.681180       -0.258450                -0.450299      Benign  \n",
+       "434             -0.531351       -0.607890                -0.868688      Benign  \n",
+       "299             -1.269232       -1.089989                -0.896396      Benign  \n",
+       "488             -0.250572       -0.156530                -0.205361      Benign  \n",
+       "332             -1.437181        0.632947                -1.037706      Benign  \n",
+       "\n",
+       "[143 rows x 33 columns]"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# after we train, we can make predicitions on the test data\n",
+    "cancer_test[\"predicited\"] = knn.predict(cancer_test[[\"perimeter_mean\", \"concavity_mean\"]])\n",
+    "cancer_test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "87e23c6c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9230769230769231"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#evaulate how accurate our method is \n",
+    "knn.score(cancer_test[[\"perimeter_mean\", \"concavity_mean\"]], cancer_test[\"diagnosis\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "6fce1a19",
+   "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>predicited</th>\n",
+       "      <th>Benign</th>\n",
+       "      <th>Malignant</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>diagnosis</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": [
+       "predicited  Benign  Malignant\n",
+       "diagnosis                    \n",
+       "Benign          88          2\n",
+       "Malignant        9         44"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# accurcy help measure, we need to show were the errors are with a confusion matrix, view confusion matrix we use below \n",
+    "#confusion matrix\n",
+    "pd.crosstab(\n",
+    "    cancer_test[\"diagnosis\"],\n",
+    "    cancer_test[\"predicited\"]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "95d53be7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9565217391304348"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# precision measurement to anwser where our model predicted a positive how often was it corrent\n",
+    "# true posiives over false posiitves\n",
+    "# if high percision score, then 96% accurate when it labels something malignant\n",
+    "precision_score (\n",
+    "    y_true= cancer_test[\"diagnosis\"],\n",
+    "    y_pred= cancer_test[\"predicited\"],\n",
+    "    pos_label= \"Malignant\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2b4ff003",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.8301886792452831"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# recall, how  good is our model at finding a malignant tumor, if malignant tumour is here in the first place\n",
+    "# if high score, means model does not miss many malignany tumours is they're there\n",
+    "recall_score(\n",
+    "    y_true= cancer_test[\"diagnosis\"],\n",
+    "    y_pred= cancer_test[\"predicited\"],\n",
+    "    pos_label= \"Malignant\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "ba28db76",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cancer_subtrain, cancer_validation = train_test_split(cancer_train, train_size= 0.75, shuffle = True, stratify= cancer_train[\"diagnosis\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "48203433",
+   "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",
+       "\n",
+       "#sk-container-id-2.light {\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: black;\n",
+       "  --sklearn-color-background: white;\n",
+       "  --sklearn-color-border-box: black;\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2.dark {\n",
+       "  --sklearn-color-text-on-default-background: white;\n",
+       "  --sklearn-color-background: #111;\n",
+       "  --sklearn-color-border-box: white;\n",
+       "  --sklearn-color-icon: #878787;\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: center;\n",
+       "  justify-content: center;\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",
+       "  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-unfitted-level-0);\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-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-3);\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",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-3);\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",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-0);\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",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-0);\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-unfitted-level-0);\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",
+       "  background-color: var(--sklearn-color-fitted-level-0);\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 {\n",
+       "    font-family: monospace;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary::marker {\n",
+       "    font-size: 0.7rem;\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",
+       "    margin-top: 0;\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",
+       "/*\n",
+       "    `table td`is set in notebook with right text-align.\n",
+       "    We need to overwrite it.\n",
+       "*/\n",
+       ".estimator-table table td.param {\n",
+       "    text-align: left;\n",
+       "    position: relative;\n",
+       "    padding: 0;\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    background-color: transparent;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    Styles for parameter documentation links\n",
+       "    We need styling for visited so jupyter doesn't overwrite it\n",
+       "*/\n",
+       "a.param-doc-link,\n",
+       "a.param-doc-link:link,\n",
+       "a.param-doc-link:visited {\n",
+       "    text-decoration: underline dashed;\n",
+       "    text-underline-offset: .3em;\n",
+       "    color: inherit;\n",
+       "    display: block;\n",
+       "    padding: .5em;\n",
+       "}\n",
+       "\n",
+       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
+       "a.param-doc-link::before {\n",
+       "    position: absolute;\n",
+       "    content: \"\";\n",
+       "    inset: 0;\n",
+       "}\n",
+       "\n",
+       ".param-doc-description {\n",
+       "    display: none;\n",
+       "    position: absolute;\n",
+       "    z-index: 9999;\n",
+       "    left: 0;\n",
+       "    padding: .5ex;\n",
+       "    margin-left: 1.5em;\n",
+       "    color: var(--sklearn-color-text);\n",
+       "    box-shadow: .3em .3em .4em #999;\n",
+       "    width: max-content;\n",
+       "    text-align: left;\n",
+       "    max-height: 10em;\n",
+       "    overflow-y: auto;\n",
+       "\n",
+       "    /* unfitted */\n",
+       "    background: var(--sklearn-color-unfitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* Fitted state for parameter tooltips */\n",
+       ".fitted .param-doc-description {\n",
+       "    /* fitted */\n",
+       "    background: var(--sklearn-color-fitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".param-doc-link:hover .param-doc-description {\n",
+       "    display: block;\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-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-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/1.8/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",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=n_neighbors,-int%2C%20default%3D5\">\n",
+       "            n_neighbors\n",
+       "            <span class=\"param-doc-description\">n_neighbors: int, default=5<br><br>Number of neighbors to use by default for :meth:`kneighbors` queries.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">3</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\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=weights,-%7B%27uniform%27%2C%20%27distance%27%7D%2C%20callable%20or%20None%2C%20default%3D%27uniform%27\">\n",
+       "            weights\n",
+       "            <span class=\"param-doc-description\">weights: {'uniform', 'distance'}, callable or None, default='uniform'<br><br>Weight function used in prediction.  Possible values:<br><br>- 'uniform' : uniform weights.  All points in each neighborhood<br>  are weighted equally.<br>- 'distance' : weight points by the inverse of their distance.<br>  in this case, closer neighbors of a query point will have a<br>  greater influence than neighbors which are further away.<br>- [callable] : a user-defined function which accepts an<br>  array of distances, and returns an array of the same shape<br>  containing the weights.<br><br>Refer to the example entitled<br>:ref:`sphx_glr_auto_examples_neighbors_plot_classification.py`<br>showing the impact of the `weights` parameter on the decision<br>boundary.</span>\n",
+       "        </a>\n",
+       "    </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\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=algorithm,-%7B%27auto%27%2C%20%27ball_tree%27%2C%20%27kd_tree%27%2C%20%27brute%27%7D%2C%20default%3D%27auto%27\">\n",
+       "            algorithm\n",
+       "            <span class=\"param-doc-description\">algorithm: {'auto', 'ball_tree', 'kd_tree', 'brute'}, default='auto'<br><br>Algorithm used to compute the nearest neighbors:<br><br>- 'ball_tree' will use :class:`BallTree`<br>- 'kd_tree' will use :class:`KDTree`<br>- 'brute' will use a brute-force search.<br>- 'auto' will attempt to decide the most appropriate algorithm<br>  based on the values passed to :meth:`fit` method.<br><br>Note: fitting on sparse input will override the setting of<br>this parameter, using brute force.</span>\n",
+       "        </a>\n",
+       "    </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\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=leaf_size,-int%2C%20default%3D30\">\n",
+       "            leaf_size\n",
+       "            <span class=\"param-doc-description\">leaf_size: int, default=30<br><br>Leaf size passed to BallTree or KDTree.  This can affect the<br>speed of the construction and query, as well as the memory<br>required to store the tree.  The optimal value depends on the<br>nature of the problem.</span>\n",
+       "        </a>\n",
+       "    </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\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=p,-float%2C%20default%3D2\">\n",
+       "            p\n",
+       "            <span class=\"param-doc-description\">p: float, default=2<br><br>Power parameter for the Minkowski metric. When p = 1, this is equivalent<br>to using manhattan_distance (l1), and euclidean_distance (l2) for p = 2.<br>For arbitrary p, minkowski_distance (l_p) is used. This parameter is expected<br>to be positive.</span>\n",
+       "        </a>\n",
+       "    </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\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=metric,-str%20or%20callable%2C%20default%3D%27minkowski%27\">\n",
+       "            metric\n",
+       "            <span class=\"param-doc-description\">metric: str or callable, default='minkowski'<br><br>Metric to use for distance computation. Default is \"minkowski\", which<br>results in the standard Euclidean distance when p = 2. See the<br>documentation of `scipy.spatial.distance<br><https://docs.scipy.org/doc/scipy/reference/spatial.distance.html>`_ and<br>the metrics listed in<br>:class:`~sklearn.metrics.pairwise.distance_metrics` for valid metric<br>values.<br><br>If metric is \"precomputed\", X is assumed to be a distance matrix and<br>must be square during fit. X may be a :term:`sparse graph`, in which<br>case only \"nonzero\" elements may be considered neighbors.<br><br>If metric is a callable function, it takes two arrays representing 1D<br>vectors as inputs and must return one value indicating the distance<br>between those vectors. This works for Scipy's metrics, but is less<br>efficient than passing the metric name as a string.</span>\n",
+       "        </a>\n",
+       "    </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\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=metric_params,-dict%2C%20default%3DNone\">\n",
+       "            metric_params\n",
+       "            <span class=\"param-doc-description\">metric_params: dict, default=None<br><br>Additional keyword arguments for the metric function.</span>\n",
+       "        </a>\n",
+       "    </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",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
+       "            n_jobs\n",
+       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>The number of parallel jobs to run for neighbors search.<br>``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.<br>``-1`` means using all processors. See :term:`Glossary <n_jobs>`<br>for more details.<br>Doesn't affect :meth:`fit` method.</span>\n",
+       "        </a>\n",
+       "    </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}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling\n",
+       "        .textContent.trim().split(' ')[0];\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "\n",
+       "\n",
+       "/**\n",
+       " * Adapted from Skrub\n",
+       " * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
+       " * @returns \"light\" or \"dark\"\n",
+       " */\n",
+       "function detectTheme(element) {\n",
+       "    const body = document.querySelector('body');\n",
+       "\n",
+       "    // Check VSCode theme\n",
+       "    const themeKindAttr = body.getAttribute('data-vscode-theme-kind');\n",
+       "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
+       "\n",
+       "    if (themeKindAttr && themeNameAttr) {\n",
+       "        const themeKind = themeKindAttr.toLowerCase();\n",
+       "        const themeName = themeNameAttr.toLowerCase();\n",
+       "\n",
+       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
+       "            return \"dark\";\n",
+       "        }\n",
+       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
+       "            return \"light\";\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    // Check Jupyter theme\n",
+       "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
+       "        return 'dark';\n",
+       "    } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
+       "        return 'light';\n",
+       "    }\n",
+       "\n",
+       "    // Guess based on a parent element's color\n",
+       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
+       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
+       "    if (match) {\n",
+       "        const [r, g, b] = [\n",
+       "            parseFloat(match[1]),\n",
+       "            parseFloat(match[2]),\n",
+       "            parseFloat(match[3])\n",
+       "        ];\n",
+       "\n",
+       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
+       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
+       "\n",
+       "        if (luma > 180) {\n",
+       "            // If the text is very bright we have a dark theme\n",
+       "            return 'dark';\n",
+       "        }\n",
+       "        if (luma < 75) {\n",
+       "            // If the text is very dark we have a light theme\n",
+       "            return 'light';\n",
+       "        }\n",
+       "        // Otherwise fall back to the next heuristic.\n",
+       "    }\n",
+       "\n",
+       "    // Fallback to system preference\n",
+       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
+       "}\n",
+       "\n",
+       "\n",
+       "function forceTheme(elementId) {\n",
+       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
+       "    if (estimatorElement === null) {\n",
+       "        console.error(`Element with id ${elementId} not found.`);\n",
+       "    } else {\n",
+       "        const theme = detectTheme(estimatorElement);\n",
+       "        estimatorElement.classList.add(theme);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "forceTheme('sk-container-id-2');</script></body>"
+      ],
+      "text/plain": [
+       "KNeighborsClassifier(n_neighbors=3)"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# k = 3\n",
+    "# initialize our model\n",
+    "knn = KNeighborsClassifier(n_neighbors=3)\n",
+    "X = cancer_subtrain[[\"perimeter_mean\", \"concavity_mean\"]]\n",
+    "y = cancer_subtrain[\"diagnosis\"]\n",
+    "knn.fit(X, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "d1e275f1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.8785046728971962"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "knn.score(cancer_validation[[\"perimeter_mean\", \"concavity_mean\"]], cancer_validation[\"diagnosis\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "8d02358f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# perform 5-fold cross validation\n",
+    "knn = KNeighborsClassifier(n_neighbors=3)\n",
+    "X= cancer_train[[\"perimeter_mean\", \"concavity_mean\"]]\n",
+    "y = cancer_train[\"diagnosis\"]\n",
+    "\n",
+    "returned_dictionary = cross_validate(\n",
+    "    estimator = knn,\n",
+    "    cv = 5,\n",
+    "    X = X,\n",
+    "    y = y\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "d20e2425",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'fit_time': array([0.04487824, 0.00792933, 0.00299144, 0.00498796, 0.00299168]),\n",
+       " 'score_time': array([0.01894546, 0.01198339, 0.00299215, 0.00997186, 0.0089767 ]),\n",
+       " 'test_score': array([0.93023256, 0.89411765, 0.87058824, 0.95294118, 0.91764706])}"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "returned_dictionary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "c1c1719a",
+   "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>0.044878</td>\n",
+       "      <td>0.018945</td>\n",
+       "      <td>0.930233</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.007929</td>\n",
+       "      <td>0.011983</td>\n",
+       "      <td>0.894118</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.002991</td>\n",
+       "      <td>0.002992</td>\n",
+       "      <td>0.870588</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.004988</td>\n",
+       "      <td>0.009972</td>\n",
+       "      <td>0.952941</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.002992</td>\n",
+       "      <td>0.008977</td>\n",
+       "      <td>0.917647</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   fit_time  score_time  test_score\n",
+       "0  0.044878    0.018945    0.930233\n",
+       "1  0.007929    0.011983    0.894118\n",
+       "2  0.002991    0.002992    0.870588\n",
+       "3  0.004988    0.009972    0.952941\n",
+       "4  0.002992    0.008977    0.917647"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cv_5_df = pd.DataFrame(returned_dictionary)\n",
+    "cv_5_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "c1c6ba3e",
+   "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>0.012756</td>\n",
+       "      <td>0.010574</td>\n",
+       "      <td>0.913105</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sem</th>\n",
+       "      <td>0.008081</td>\n",
+       "      <td>0.002574</td>\n",
+       "      <td>0.014264</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      fit_time  score_time  test_score\n",
+       "mean  0.012756    0.010574    0.913105\n",
+       "sem   0.008081    0.002574    0.014264"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cv_5_df.agg([\"mean\", \"sem\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "9c802067",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# step1. define our parameter grid\n",
+    "parameter_grid = {\n",
+    "    \"n_neighbors\" : range(1, 100, 5)\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "120bd0d3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# step2. initialize our grid search CV object\n",
+    "\n",
+    "cancer_tune_grid = GridSearchCV(\n",
+    "    estimator = knn, \n",
+    "    param_grid= parameter_grid,\n",
+    "    cv = 10\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "6ffe7067",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-4 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #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",
+       "\n",
+       "#sk-container-id-4.light {\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: black;\n",
+       "  --sklearn-color-background: white;\n",
+       "  --sklearn-color-border-box: black;\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4.dark {\n",
+       "  --sklearn-color-text-on-default-background: white;\n",
+       "  --sklearn-color-background: #111;\n",
+       "  --sklearn-color-border-box: white;\n",
+       "  --sklearn-color-icon: #878787;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-4 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: center;\n",
+       "  justify-content: center;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 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-4 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-4 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-4 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-4 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\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-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-3);\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",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-3);\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",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-0);\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",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-0);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table {\n",
+       "    font-family: monospace;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary::marker {\n",
+       "    font-size: 0.7rem;\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",
+       "    margin-top: 0;\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",
+       "/*\n",
+       "    `table td`is set in notebook with right text-align.\n",
+       "    We need to overwrite it.\n",
+       "*/\n",
+       ".estimator-table table td.param {\n",
+       "    text-align: left;\n",
+       "    position: relative;\n",
+       "    padding: 0;\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    background-color: transparent;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    Styles for parameter documentation links\n",
+       "    We need styling for visited so jupyter doesn't overwrite it\n",
+       "*/\n",
+       "a.param-doc-link,\n",
+       "a.param-doc-link:link,\n",
+       "a.param-doc-link:visited {\n",
+       "    text-decoration: underline dashed;\n",
+       "    text-underline-offset: .3em;\n",
+       "    color: inherit;\n",
+       "    display: block;\n",
+       "    padding: .5em;\n",
+       "}\n",
+       "\n",
+       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
+       "a.param-doc-link::before {\n",
+       "    position: absolute;\n",
+       "    content: \"\";\n",
+       "    inset: 0;\n",
+       "}\n",
+       "\n",
+       ".param-doc-description {\n",
+       "    display: none;\n",
+       "    position: absolute;\n",
+       "    z-index: 9999;\n",
+       "    left: 0;\n",
+       "    padding: .5ex;\n",
+       "    margin-left: 1.5em;\n",
+       "    color: var(--sklearn-color-text);\n",
+       "    box-shadow: .3em .3em .4em #999;\n",
+       "    width: max-content;\n",
+       "    text-align: left;\n",
+       "    max-height: 10em;\n",
+       "    overflow-y: auto;\n",
+       "\n",
+       "    /* unfitted */\n",
+       "    background: var(--sklearn-color-unfitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* Fitted state for parameter tooltips */\n",
+       ".fitted .param-doc-description {\n",
+       "    /* fitted */\n",
+       "    background: var(--sklearn-color-fitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".param-doc-link:hover .param-doc-description {\n",
+       "    display: block;\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-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=10, estimator=KNeighborsClassifier(n_neighbors=3),\n",
+       "             param_grid={&#x27;n_neighbors&#x27;: range(1, 100, 5)})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" 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/1.8/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\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=estimator,-estimator%20object\">\n",
+       "            estimator\n",
+       "            <span class=\"param-doc-description\">estimator: estimator object<br><br>This is assumed to implement the scikit-learn estimator interface.<br>Either estimator needs to provide a ``score`` function,<br>or ``scoring`` must be passed.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">KNeighborsCla...n_neighbors=3)</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\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=param_grid,-dict%20or%20list%20of%20dictionaries\">\n",
+       "            param_grid\n",
+       "            <span class=\"param-doc-description\">param_grid: dict or list of dictionaries<br><br>Dictionary with parameters names (`str`) as keys and lists of<br>parameter settings to try as values, or a list of such<br>dictionaries, in which case the grids spanned by each dictionary<br>in the list are explored. This enables searching over any sequence<br>of parameter settings.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">{&#x27;n_neighbors&#x27;: range(1, 100, 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\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=scoring,-str%2C%20callable%2C%20list%2C%20tuple%20or%20dict%2C%20default%3DNone\">\n",
+       "            scoring\n",
+       "            <span class=\"param-doc-description\">scoring: str, callable, list, tuple or dict, default=None<br><br>Strategy to evaluate the performance of the cross-validated model on<br>the test set.<br><br>If `scoring` represents a single score, one can use:<br><br>- a single string (see :ref:`scoring_string_names`);<br>- a callable (see :ref:`scoring_callable`) that returns a single value;<br>- `None`, the `estimator`'s<br>  :ref:`default evaluation criterion <scoring_api_overview>` is used.<br><br>If `scoring` represents multiple scores, one can use:<br><br>- a list or tuple of unique strings;<br>- a callable returning a dictionary where the keys are the metric<br>  names and the values are the metric scores;<br>- a dictionary with metric names as keys and callables as values.<br><br>See :ref:`multimetric_grid_search` for an example.</span>\n",
+       "        </a>\n",
+       "    </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",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
+       "            n_jobs\n",
+       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>Number of jobs to run in parallel.<br>``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.<br>``-1`` means using all processors. See :term:`Glossary <n_jobs>`<br>for more details.<br><br>.. versionchanged:: v0.20<br>   `n_jobs` default changed from 1 to None</span>\n",
+       "        </a>\n",
+       "    </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\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=refit,-bool%2C%20str%2C%20or%20callable%2C%20default%3DTrue\">\n",
+       "            refit\n",
+       "            <span class=\"param-doc-description\">refit: bool, str, or callable, default=True<br><br>Refit an estimator using the best found parameters on the whole<br>dataset.<br><br>For multiple metric evaluation, this needs to be a `str` denoting the<br>scorer that would be used to find the best parameters for refitting<br>the estimator at the end.<br><br>Where there are considerations other than maximum score in<br>choosing a best estimator, ``refit`` can be set to a function which<br>returns the selected ``best_index_`` given ``cv_results_``. In that<br>case, the ``best_estimator_`` and ``best_params_`` will be set<br>according to the returned ``best_index_`` while the ``best_score_``<br>attribute will not be available.<br><br>The refitted estimator is made available at the ``best_estimator_``<br>attribute and permits using ``predict`` directly on this<br>``GridSearchCV`` instance.<br><br>Also for multiple metric evaluation, the attributes ``best_index_``,<br>``best_score_`` and ``best_params_`` will only be available if<br>``refit`` is set and all of them will be determined w.r.t this specific<br>scorer.<br><br>See ``scoring`` parameter to know more about multiple metric<br>evaluation.<br><br>See :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_digits.py`<br>to see how to design a custom selection strategy using a callable<br>via `refit`.<br><br>See :ref:`this example<br><sphx_glr_auto_examples_model_selection_plot_grid_search_refit_callable.py>`<br>for an example of how to use ``refit=callable`` to balance model<br>complexity and cross-validated score.<br><br>.. versionchanged:: 0.20<br>    Support for callable added.</span>\n",
+       "        </a>\n",
+       "    </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\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=cv,-int%2C%20cross-validation%20generator%20or%20an%20iterable%2C%20default%3DNone\">\n",
+       "            cv\n",
+       "            <span class=\"param-doc-description\">cv: int, cross-validation generator or an iterable, default=None<br><br>Determines the cross-validation splitting strategy.<br>Possible inputs for cv are:<br><br>- None, to use the default 5-fold cross validation,<br>- integer, to specify the number of folds in a `(Stratified)KFold`,<br>- :term:`CV splitter`,<br>- An iterable yielding (train, test) splits as arrays of indices.<br><br>For integer/None inputs, if the estimator is a classifier and ``y`` is<br>either binary or multiclass, :class:`StratifiedKFold` is used. In all<br>other cases, :class:`KFold` is used. These splitters are instantiated<br>with `shuffle=False` so the splits will be the same across calls.<br><br>Refer :ref:`User Guide <cross_validation>` for the various<br>cross-validation strategies that can be used here.<br><br>.. versionchanged:: 0.22<br>    ``cv`` default value if None changed from 3-fold to 5-fold.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">10</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=verbose,-int\">\n",
+       "            verbose\n",
+       "            <span class=\"param-doc-description\">verbose: int<br><br>Controls the verbosity: the higher, the more messages.<br><br>- >1 : the computation time for each fold and parameter candidate is<br>  displayed;<br>- >2 : the score is also displayed;<br>- >3 : the fold and candidate parameter indexes are also displayed<br>  together with the starting time of the computation.</span>\n",
+       "        </a>\n",
+       "    </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\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=pre_dispatch,-int%2C%20or%20str%2C%20default%3D%272%2An_jobs%27\">\n",
+       "            pre_dispatch\n",
+       "            <span class=\"param-doc-description\">pre_dispatch: int, or str, default='2*n_jobs'<br><br>Controls the number of jobs that get dispatched during parallel<br>execution. Reducing this number can be useful to avoid an<br>explosion of memory consumption when more jobs get dispatched<br>than CPUs can process. This parameter can be:<br><br>- None, in which case all the jobs are immediately created and spawned. Use<br>  this for lightweight and fast-running jobs, to avoid delays due to on-demand<br>  spawning of the jobs<br>- An int, giving the exact number of total jobs that are spawned<br>- A str, giving an expression as a function of n_jobs, as in '2*n_jobs'</span>\n",
+       "        </a>\n",
+       "    </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\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=error_score,-%27raise%27%20or%20numeric%2C%20default%3Dnp.nan\">\n",
+       "            error_score\n",
+       "            <span class=\"param-doc-description\">error_score: 'raise' or numeric, default=np.nan<br><br>Value to assign to the score if an error occurs in estimator fitting.<br>If set to 'raise', the error is raised. If a numeric value is given,<br>FitFailedWarning is raised. This parameter does not affect the refit<br>step, which will always raise the error.</span>\n",
+       "        </a>\n",
+       "    </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\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.model_selection.GridSearchCV.html#:~:text=return_train_score,-bool%2C%20default%3DFalse\">\n",
+       "            return_train_score\n",
+       "            <span class=\"param-doc-description\">return_train_score: bool, default=False<br><br>If ``False``, the ``cv_results_`` attribute will not include training<br>scores.<br>Computing training scores is used to get insights on how different<br>parameter settings impact the overfitting/underfitting trade-off.<br>However computing the scores on the training set can be computationally<br>expensive and is not strictly required to select the parameters that<br>yield the best generalization performance.<br><br>.. versionadded:: 0.19<br><br>.. versionchanged:: 0.21<br>    Default value was changed from ``True`` to ``False``</span>\n",
+       "        </a>\n",
+       "    </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-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" 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=16)</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-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" 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/1.8/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",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=n_neighbors,-int%2C%20default%3D5\">\n",
+       "            n_neighbors\n",
+       "            <span class=\"param-doc-description\">n_neighbors: int, default=5<br><br>Number of neighbors to use by default for :meth:`kneighbors` queries.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">16</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\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=weights,-%7B%27uniform%27%2C%20%27distance%27%7D%2C%20callable%20or%20None%2C%20default%3D%27uniform%27\">\n",
+       "            weights\n",
+       "            <span class=\"param-doc-description\">weights: {'uniform', 'distance'}, callable or None, default='uniform'<br><br>Weight function used in prediction.  Possible values:<br><br>- 'uniform' : uniform weights.  All points in each neighborhood<br>  are weighted equally.<br>- 'distance' : weight points by the inverse of their distance.<br>  in this case, closer neighbors of a query point will have a<br>  greater influence than neighbors which are further away.<br>- [callable] : a user-defined function which accepts an<br>  array of distances, and returns an array of the same shape<br>  containing the weights.<br><br>Refer to the example entitled<br>:ref:`sphx_glr_auto_examples_neighbors_plot_classification.py`<br>showing the impact of the `weights` parameter on the decision<br>boundary.</span>\n",
+       "        </a>\n",
+       "    </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\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=algorithm,-%7B%27auto%27%2C%20%27ball_tree%27%2C%20%27kd_tree%27%2C%20%27brute%27%7D%2C%20default%3D%27auto%27\">\n",
+       "            algorithm\n",
+       "            <span class=\"param-doc-description\">algorithm: {'auto', 'ball_tree', 'kd_tree', 'brute'}, default='auto'<br><br>Algorithm used to compute the nearest neighbors:<br><br>- 'ball_tree' will use :class:`BallTree`<br>- 'kd_tree' will use :class:`KDTree`<br>- 'brute' will use a brute-force search.<br>- 'auto' will attempt to decide the most appropriate algorithm<br>  based on the values passed to :meth:`fit` method.<br><br>Note: fitting on sparse input will override the setting of<br>this parameter, using brute force.</span>\n",
+       "        </a>\n",
+       "    </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\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=leaf_size,-int%2C%20default%3D30\">\n",
+       "            leaf_size\n",
+       "            <span class=\"param-doc-description\">leaf_size: int, default=30<br><br>Leaf size passed to BallTree or KDTree.  This can affect the<br>speed of the construction and query, as well as the memory<br>required to store the tree.  The optimal value depends on the<br>nature of the problem.</span>\n",
+       "        </a>\n",
+       "    </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\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=p,-float%2C%20default%3D2\">\n",
+       "            p\n",
+       "            <span class=\"param-doc-description\">p: float, default=2<br><br>Power parameter for the Minkowski metric. When p = 1, this is equivalent<br>to using manhattan_distance (l1), and euclidean_distance (l2) for p = 2.<br>For arbitrary p, minkowski_distance (l_p) is used. This parameter is expected<br>to be positive.</span>\n",
+       "        </a>\n",
+       "    </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\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=metric,-str%20or%20callable%2C%20default%3D%27minkowski%27\">\n",
+       "            metric\n",
+       "            <span class=\"param-doc-description\">metric: str or callable, default='minkowski'<br><br>Metric to use for distance computation. Default is \"minkowski\", which<br>results in the standard Euclidean distance when p = 2. See the<br>documentation of `scipy.spatial.distance<br><https://docs.scipy.org/doc/scipy/reference/spatial.distance.html>`_ and<br>the metrics listed in<br>:class:`~sklearn.metrics.pairwise.distance_metrics` for valid metric<br>values.<br><br>If metric is \"precomputed\", X is assumed to be a distance matrix and<br>must be square during fit. X may be a :term:`sparse graph`, in which<br>case only \"nonzero\" elements may be considered neighbors.<br><br>If metric is a callable function, it takes two arrays representing 1D<br>vectors as inputs and must return one value indicating the distance<br>between those vectors. This works for Scipy's metrics, but is less<br>efficient than passing the metric name as a string.</span>\n",
+       "        </a>\n",
+       "    </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\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=metric_params,-dict%2C%20default%3DNone\">\n",
+       "            metric_params\n",
+       "            <span class=\"param-doc-description\">metric_params: dict, default=None<br><br>Additional keyword arguments for the metric function.</span>\n",
+       "        </a>\n",
+       "    </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",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
+       "            n_jobs\n",
+       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>The number of parallel jobs to run for neighbors search.<br>``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.<br>``-1`` means using all processors. See :term:`Glossary <n_jobs>`<br>for more details.<br>Doesn't affect :meth:`fit` method.</span>\n",
+       "        </a>\n",
+       "    </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}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling\n",
+       "        .textContent.trim().split(' ')[0];\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "\n",
+       "\n",
+       "/**\n",
+       " * Adapted from Skrub\n",
+       " * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
+       " * @returns \"light\" or \"dark\"\n",
+       " */\n",
+       "function detectTheme(element) {\n",
+       "    const body = document.querySelector('body');\n",
+       "\n",
+       "    // Check VSCode theme\n",
+       "    const themeKindAttr = body.getAttribute('data-vscode-theme-kind');\n",
+       "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
+       "\n",
+       "    if (themeKindAttr && themeNameAttr) {\n",
+       "        const themeKind = themeKindAttr.toLowerCase();\n",
+       "        const themeName = themeNameAttr.toLowerCase();\n",
+       "\n",
+       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
+       "            return \"dark\";\n",
+       "        }\n",
+       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
+       "            return \"light\";\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    // Check Jupyter theme\n",
+       "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
+       "        return 'dark';\n",
+       "    } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
+       "        return 'light';\n",
+       "    }\n",
+       "\n",
+       "    // Guess based on a parent element's color\n",
+       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
+       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
+       "    if (match) {\n",
+       "        const [r, g, b] = [\n",
+       "            parseFloat(match[1]),\n",
+       "            parseFloat(match[2]),\n",
+       "            parseFloat(match[3])\n",
+       "        ];\n",
+       "\n",
+       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
+       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
+       "\n",
+       "        if (luma > 180) {\n",
+       "            // If the text is very bright we have a dark theme\n",
+       "            return 'dark';\n",
+       "        }\n",
+       "        if (luma < 75) {\n",
+       "            // If the text is very dark we have a light theme\n",
+       "            return 'light';\n",
+       "        }\n",
+       "        // Otherwise fall back to the next heuristic.\n",
+       "    }\n",
+       "\n",
+       "    // Fallback to system preference\n",
+       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
+       "}\n",
+       "\n",
+       "\n",
+       "function forceTheme(elementId) {\n",
+       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
+       "    if (estimatorElement === null) {\n",
+       "        console.error(`Element with id ${elementId} not found.`);\n",
+       "    } else {\n",
+       "        const theme = detectTheme(estimatorElement);\n",
+       "        estimatorElement.classList.add(theme);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "forceTheme('sk-container-id-4');</script></body>"
+      ],
+      "text/plain": [
+       "GridSearchCV(cv=10, estimator=KNeighborsClassifier(n_neighbors=3),\n",
+       "             param_grid={'n_neighbors': range(1, 100, 5)})"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# step 3. fit our grid search object to our training data\n",
+    "cancer_tune_grid.fit(cancer_train[[\"perimeter_mean\", \"concavity_mean\"]], cancer_train[\"diagnosis\"])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "7db382fc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mean_fit_time</th>\n",
+       "      <th>std_fit_time</th>\n",
+       "      <th>mean_score_time</th>\n",
+       "      <th>std_score_time</th>\n",
+       "      <th>param_n_neighbors</th>\n",
+       "      <th>params</th>\n",
+       "      <th>split0_test_score</th>\n",
+       "      <th>split1_test_score</th>\n",
+       "      <th>split2_test_score</th>\n",
+       "      <th>split3_test_score</th>\n",
+       "      <th>split4_test_score</th>\n",
+       "      <th>split5_test_score</th>\n",
+       "      <th>split6_test_score</th>\n",
+       "      <th>split7_test_score</th>\n",
+       "      <th>split8_test_score</th>\n",
+       "      <th>split9_test_score</th>\n",
+       "      <th>mean_test_score</th>\n",
+       "      <th>std_test_score</th>\n",
+       "      <th>rank_test_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.005496</td>\n",
+       "      <td>0.010541</td>\n",
+       "      <td>0.006945</td>\n",
+       "      <td>0.012057</td>\n",
+       "      <td>1</td>\n",
+       "      <td>{'n_neighbors': 1}</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.860465</td>\n",
+       "      <td>0.813953</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.880952</td>\n",
+       "      <td>0.904762</td>\n",
+       "      <td>0.880952</td>\n",
+       "      <td>0.880952</td>\n",
+       "      <td>0.875692</td>\n",
+       "      <td>0.038684</td>\n",
+       "      <td>20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.001695</td>\n",
+       "      <td>0.000638</td>\n",
+       "      <td>0.002745</td>\n",
+       "      <td>0.001018</td>\n",
+       "      <td>6</td>\n",
+       "      <td>{'n_neighbors': 6}</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.928571</td>\n",
+       "      <td>0.928571</td>\n",
+       "      <td>0.880952</td>\n",
+       "      <td>0.976190</td>\n",
+       "      <td>0.908638</td>\n",
+       "      <td>0.043373</td>\n",
+       "      <td>18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.001593</td>\n",
+       "      <td>0.000486</td>\n",
+       "      <td>0.002589</td>\n",
+       "      <td>0.000806</td>\n",
+       "      <td>11</td>\n",
+       "      <td>{'n_neighbors': 11}</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.883721</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.904762</td>\n",
+       "      <td>0.928571</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.904042</td>\n",
+       "      <td>0.039147</td>\n",
+       "      <td>19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.001694</td>\n",
+       "      <td>0.000457</td>\n",
+       "      <td>0.003261</td>\n",
+       "      <td>0.001526</td>\n",
+       "      <td>16</td>\n",
+       "      <td>{'n_neighbors': 16}</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.928571</td>\n",
+       "      <td>0.976190</td>\n",
+       "      <td>0.920487</td>\n",
+       "      <td>0.045315</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.001696</td>\n",
+       "      <td>0.000457</td>\n",
+       "      <td>0.002706</td>\n",
+       "      <td>0.000880</td>\n",
+       "      <td>21</td>\n",
+       "      <td>{'n_neighbors': 21}</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.880952</td>\n",
+       "      <td>0.976190</td>\n",
+       "      <td>0.913400</td>\n",
+       "      <td>0.046495</td>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0.001597</td>\n",
+       "      <td>0.000489</td>\n",
+       "      <td>0.003067</td>\n",
+       "      <td>0.000833</td>\n",
+       "      <td>26</td>\n",
+       "      <td>{'n_neighbors': 26}</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.928571</td>\n",
+       "      <td>0.904762</td>\n",
+       "      <td>0.976190</td>\n",
+       "      <td>0.913400</td>\n",
+       "      <td>0.043989</td>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>0.001890</td>\n",
+       "      <td>0.000542</td>\n",
+       "      <td>0.002578</td>\n",
+       "      <td>0.000666</td>\n",
+       "      <td>31</td>\n",
+       "      <td>{'n_neighbors': 31}</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.928571</td>\n",
+       "      <td>0.904762</td>\n",
+       "      <td>0.976190</td>\n",
+       "      <td>0.913400</td>\n",
+       "      <td>0.043989</td>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>0.001795</td>\n",
+       "      <td>0.000598</td>\n",
+       "      <td>0.002590</td>\n",
+       "      <td>0.000728</td>\n",
+       "      <td>36</td>\n",
+       "      <td>{'n_neighbors': 36}</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.883721</td>\n",
+       "      <td>0.928571</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.904762</td>\n",
+       "      <td>0.976190</td>\n",
+       "      <td>0.911074</td>\n",
+       "      <td>0.044873</td>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>0.001379</td>\n",
+       "      <td>0.000471</td>\n",
+       "      <td>0.002977</td>\n",
+       "      <td>0.000660</td>\n",
+       "      <td>41</td>\n",
+       "      <td>{'n_neighbors': 41}</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.883721</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.928571</td>\n",
+       "      <td>0.904762</td>\n",
+       "      <td>0.976190</td>\n",
+       "      <td>0.911074</td>\n",
+       "      <td>0.044873</td>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>0.001795</td>\n",
+       "      <td>0.000399</td>\n",
+       "      <td>0.002588</td>\n",
+       "      <td>0.000457</td>\n",
+       "      <td>46</td>\n",
+       "      <td>{'n_neighbors': 46}</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.904762</td>\n",
+       "      <td>0.976190</td>\n",
+       "      <td>0.915781</td>\n",
+       "      <td>0.045368</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>0.001895</td>\n",
+       "      <td>0.000298</td>\n",
+       "      <td>0.002550</td>\n",
+       "      <td>0.000483</td>\n",
+       "      <td>51</td>\n",
+       "      <td>{'n_neighbors': 51}</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.883721</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.904762</td>\n",
+       "      <td>0.976190</td>\n",
+       "      <td>0.913455</td>\n",
+       "      <td>0.046345</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>0.001502</td>\n",
+       "      <td>0.000504</td>\n",
+       "      <td>0.002877</td>\n",
+       "      <td>0.000692</td>\n",
+       "      <td>56</td>\n",
+       "      <td>{'n_neighbors': 56}</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.976744</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.860465</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.904762</td>\n",
+       "      <td>0.976190</td>\n",
+       "      <td>0.920432</td>\n",
+       "      <td>0.044212</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>0.001795</td>\n",
+       "      <td>0.000399</td>\n",
+       "      <td>0.002499</td>\n",
+       "      <td>0.000629</td>\n",
+       "      <td>61</td>\n",
+       "      <td>{'n_neighbors': 61}</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.860465</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.904762</td>\n",
+       "      <td>0.976190</td>\n",
+       "      <td>0.918106</td>\n",
+       "      <td>0.041731</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>0.001546</td>\n",
+       "      <td>0.000470</td>\n",
+       "      <td>0.003039</td>\n",
+       "      <td>0.000552</td>\n",
+       "      <td>66</td>\n",
+       "      <td>{'n_neighbors': 66}</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.860465</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.904762</td>\n",
+       "      <td>0.976190</td>\n",
+       "      <td>0.920432</td>\n",
+       "      <td>0.041694</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>0.001793</td>\n",
+       "      <td>0.000398</td>\n",
+       "      <td>0.002860</td>\n",
+       "      <td>0.000653</td>\n",
+       "      <td>71</td>\n",
+       "      <td>{'n_neighbors': 71}</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.860465</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.904762</td>\n",
+       "      <td>0.976190</td>\n",
+       "      <td>0.920432</td>\n",
+       "      <td>0.041694</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>0.001395</td>\n",
+       "      <td>0.000489</td>\n",
+       "      <td>0.002968</td>\n",
+       "      <td>0.000345</td>\n",
+       "      <td>76</td>\n",
+       "      <td>{'n_neighbors': 76}</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.976744</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.860465</td>\n",
+       "      <td>0.813953</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.904762</td>\n",
+       "      <td>0.976190</td>\n",
+       "      <td>0.920432</td>\n",
+       "      <td>0.048861</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>0.001496</td>\n",
+       "      <td>0.000499</td>\n",
+       "      <td>0.003191</td>\n",
+       "      <td>0.000869</td>\n",
+       "      <td>81</td>\n",
+       "      <td>{'n_neighbors': 81}</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.976744</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.860465</td>\n",
+       "      <td>0.813953</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.904762</td>\n",
+       "      <td>0.976190</td>\n",
+       "      <td>0.920432</td>\n",
+       "      <td>0.048861</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>0.001695</td>\n",
+       "      <td>0.000457</td>\n",
+       "      <td>0.002762</td>\n",
+       "      <td>0.000595</td>\n",
+       "      <td>86</td>\n",
+       "      <td>{'n_neighbors': 86}</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.976744</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.860465</td>\n",
+       "      <td>0.813953</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.928571</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.904762</td>\n",
+       "      <td>0.976190</td>\n",
+       "      <td>0.915725</td>\n",
+       "      <td>0.047731</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>0.001600</td>\n",
+       "      <td>0.000492</td>\n",
+       "      <td>0.002949</td>\n",
+       "      <td>0.000380</td>\n",
+       "      <td>91</td>\n",
+       "      <td>{'n_neighbors': 91}</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.976744</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.860465</td>\n",
+       "      <td>0.813953</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.904762</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.904762</td>\n",
+       "      <td>0.976190</td>\n",
+       "      <td>0.913344</td>\n",
+       "      <td>0.047625</td>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>0.001795</td>\n",
+       "      <td>0.000399</td>\n",
+       "      <td>0.002622</td>\n",
+       "      <td>0.000519</td>\n",
+       "      <td>96</td>\n",
+       "      <td>{'n_neighbors': 96}</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.976744</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.860465</td>\n",
+       "      <td>0.813953</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.904762</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.904762</td>\n",
+       "      <td>0.976190</td>\n",
+       "      <td>0.913344</td>\n",
+       "      <td>0.047625</td>\n",
+       "      <td>14</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        0.005496      0.010541         0.006945        0.012057   \n",
+       "1        0.001695      0.000638         0.002745        0.001018   \n",
+       "2        0.001593      0.000486         0.002589        0.000806   \n",
+       "3        0.001694      0.000457         0.003261        0.001526   \n",
+       "4        0.001696      0.000457         0.002706        0.000880   \n",
+       "5        0.001597      0.000489         0.003067        0.000833   \n",
+       "6        0.001890      0.000542         0.002578        0.000666   \n",
+       "7        0.001795      0.000598         0.002590        0.000728   \n",
+       "8        0.001379      0.000471         0.002977        0.000660   \n",
+       "9        0.001795      0.000399         0.002588        0.000457   \n",
+       "10       0.001895      0.000298         0.002550        0.000483   \n",
+       "11       0.001502      0.000504         0.002877        0.000692   \n",
+       "12       0.001795      0.000399         0.002499        0.000629   \n",
+       "13       0.001546      0.000470         0.003039        0.000552   \n",
+       "14       0.001793      0.000398         0.002860        0.000653   \n",
+       "15       0.001395      0.000489         0.002968        0.000345   \n",
+       "16       0.001496      0.000499         0.003191        0.000869   \n",
+       "17       0.001695      0.000457         0.002762        0.000595   \n",
+       "18       0.001600      0.000492         0.002949        0.000380   \n",
+       "19       0.001795      0.000399         0.002622        0.000519   \n",
+       "\n",
+       "    param_n_neighbors               params  split0_test_score  \\\n",
+       "0                   1   {'n_neighbors': 1}           0.953488   \n",
+       "1                   6   {'n_neighbors': 6}           0.930233   \n",
+       "2                  11  {'n_neighbors': 11}           0.906977   \n",
+       "3                  16  {'n_neighbors': 16}           0.906977   \n",
+       "4                  21  {'n_neighbors': 21}           0.906977   \n",
+       "5                  26  {'n_neighbors': 26}           0.906977   \n",
+       "6                  31  {'n_neighbors': 31}           0.906977   \n",
+       "7                  36  {'n_neighbors': 36}           0.906977   \n",
+       "8                  41  {'n_neighbors': 41}           0.906977   \n",
+       "9                  46  {'n_neighbors': 46}           0.906977   \n",
+       "10                 51  {'n_neighbors': 51}           0.906977   \n",
+       "11                 56  {'n_neighbors': 56}           0.906977   \n",
+       "12                 61  {'n_neighbors': 61}           0.906977   \n",
+       "13                 66  {'n_neighbors': 66}           0.930233   \n",
+       "14                 71  {'n_neighbors': 71}           0.930233   \n",
+       "15                 76  {'n_neighbors': 76}           0.930233   \n",
+       "16                 81  {'n_neighbors': 81}           0.930233   \n",
+       "17                 86  {'n_neighbors': 86}           0.906977   \n",
+       "18                 91  {'n_neighbors': 91}           0.906977   \n",
+       "19                 96  {'n_neighbors': 96}           0.906977   \n",
+       "\n",
+       "    split1_test_score  split2_test_score  split3_test_score  \\\n",
+       "0            0.837209           0.906977           0.860465   \n",
+       "1            0.953488           0.906977           0.837209   \n",
+       "2            0.930233           0.906977           0.837209   \n",
+       "3            0.953488           0.930233           0.837209   \n",
+       "4            0.953488           0.930233           0.837209   \n",
+       "5            0.953488           0.930233           0.837209   \n",
+       "6            0.953488           0.930233           0.837209   \n",
+       "7            0.953488           0.930233           0.837209   \n",
+       "8            0.953488           0.930233           0.837209   \n",
+       "9            0.953488           0.930233           0.837209   \n",
+       "10           0.953488           0.930233           0.837209   \n",
+       "11           0.976744           0.930233           0.860465   \n",
+       "12           0.953488           0.930233           0.860465   \n",
+       "13           0.953488           0.930233           0.860465   \n",
+       "14           0.953488           0.930233           0.860465   \n",
+       "15           0.976744           0.930233           0.860465   \n",
+       "16           0.976744           0.930233           0.860465   \n",
+       "17           0.976744           0.930233           0.860465   \n",
+       "18           0.976744           0.930233           0.860465   \n",
+       "19           0.976744           0.930233           0.860465   \n",
+       "\n",
+       "    split4_test_score  split5_test_score  split6_test_score  \\\n",
+       "0            0.813953           0.837209           0.880952   \n",
+       "1            0.837209           0.906977           0.928571   \n",
+       "2            0.837209           0.883721           0.952381   \n",
+       "3            0.837209           0.930233           0.952381   \n",
+       "4            0.837209           0.906977           0.952381   \n",
+       "5            0.837209           0.906977           0.952381   \n",
+       "6            0.837209           0.906977           0.952381   \n",
+       "7            0.837209           0.883721           0.928571   \n",
+       "8            0.837209           0.883721           0.952381   \n",
+       "9            0.837209           0.906977           0.952381   \n",
+       "10           0.837209           0.883721           0.952381   \n",
+       "11           0.837209           0.906977           0.952381   \n",
+       "12           0.837209           0.906977           0.952381   \n",
+       "13           0.837209           0.906977           0.952381   \n",
+       "14           0.837209           0.906977           0.952381   \n",
+       "15           0.813953           0.906977           0.952381   \n",
+       "16           0.813953           0.906977           0.952381   \n",
+       "17           0.813953           0.906977           0.928571   \n",
+       "18           0.813953           0.906977           0.904762   \n",
+       "19           0.813953           0.906977           0.904762   \n",
+       "\n",
+       "    split7_test_score  split8_test_score  split9_test_score  mean_test_score  \\\n",
+       "0            0.904762           0.880952           0.880952         0.875692   \n",
+       "1            0.928571           0.880952           0.976190         0.908638   \n",
+       "2            0.904762           0.928571           0.952381         0.904042   \n",
+       "3            0.952381           0.928571           0.976190         0.920487   \n",
+       "4            0.952381           0.880952           0.976190         0.913400   \n",
+       "5            0.928571           0.904762           0.976190         0.913400   \n",
+       "6            0.928571           0.904762           0.976190         0.913400   \n",
+       "7            0.952381           0.904762           0.976190         0.911074   \n",
+       "8            0.928571           0.904762           0.976190         0.911074   \n",
+       "9            0.952381           0.904762           0.976190         0.915781   \n",
+       "10           0.952381           0.904762           0.976190         0.913455   \n",
+       "11           0.952381           0.904762           0.976190         0.920432   \n",
+       "12           0.952381           0.904762           0.976190         0.918106   \n",
+       "13           0.952381           0.904762           0.976190         0.920432   \n",
+       "14           0.952381           0.904762           0.976190         0.920432   \n",
+       "15           0.952381           0.904762           0.976190         0.920432   \n",
+       "16           0.952381           0.904762           0.976190         0.920432   \n",
+       "17           0.952381           0.904762           0.976190         0.915725   \n",
+       "18           0.952381           0.904762           0.976190         0.913344   \n",
+       "19           0.952381           0.904762           0.976190         0.913344   \n",
+       "\n",
+       "    std_test_score  rank_test_score  \n",
+       "0         0.038684               20  \n",
+       "1         0.043373               18  \n",
+       "2         0.039147               19  \n",
+       "3         0.045315                1  \n",
+       "4         0.046495               11  \n",
+       "5         0.043989               11  \n",
+       "6         0.043989               11  \n",
+       "7         0.044873               16  \n",
+       "8         0.044873               16  \n",
+       "9         0.045368                8  \n",
+       "10        0.046345               10  \n",
+       "11        0.044212                2  \n",
+       "12        0.041731                7  \n",
+       "13        0.041694                2  \n",
+       "14        0.041694                2  \n",
+       "15        0.048861                2  \n",
+       "16        0.048861                2  \n",
+       "17        0.047731                9  \n",
+       "18        0.047625               14  \n",
+       "19        0.047625               14  "
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "accuracy_grid = pd.DataFrame(cancer_tune_grid.cv_results_)\n",
+    "accuracy_grid"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "76fb7afd",
+   "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": 36,
+   "id": "584c58ab",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'n_neighbors': 16}"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cancer_tune_grid.best_params_ #keeps the best value of k"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7a7db64a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "lcr-env",
+   "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": "3.11.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/01_materials/notebooks/classwork_dec `12.ipynb b/01_materials/notebooks/classwork_dec `12.ipynb
new file mode 100644
index 000000000..c32c50ee0
--- /dev/null
+++ b/01_materials/notebooks/classwork_dec `12.ipynb	
@@ -0,0 +1,552 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "9a3096e5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#load in libraries\n",
+    "\n",
+    "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": "5b7da488",
+   "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": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# read in our data sets\n",
+    "\n",
+    "cancer = pd.read_csv(\"dataset/wdbc.csv\")\n",
+    "cancer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "6627331f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "standardized_cancer = cancer.copy() # make a copy of our data\n",
+    "columns_to_exclude = ['id','diagnosis'] #columns we dont want to scale\n",
+    "columns_to_scale = standardized_cancer.columns.difference(columns_to_exclude)\n",
+    "                      "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6e5e6718",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scaler = StandardScaler() #inituiL\n",
+    "standardized_cancer[columns_to_scale] = scaler.fit_transform"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "ea09cb11",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Malignant', 'Benign'], dtype=object)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cancer[\"diagnosis\"] = cancer[\"diagnosis\"].replace({\n",
+    "    \"M\" : \"Malignant\",\n",
+    "    \"B\" : \"Benign\"\n",
+    "})\n",
+    "\n",
+    "cancer[\"diagnosis\"].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9ce59b4f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a copy of the original 'cancer' dataframe to ensure we're not modifying the original data\n",
+    "standardized_cancer = cancer.copy()\n",
+    "\n",
+    "# Specify the columns that we do NOT want to scale (ID and diagnosis columns)\n",
+    "columns_to_exclude = ['id', 'diagnosis']\n",
+    "\n",
+    "# Select the columns that we want to scale by excluding the 'id' and 'diagnosis' columns\n",
+    "# This will return a list of the numeric columns we need to scale\n",
+    "columns_to_scale = standardized_cancer.columns.difference(columns_to_exclude)\n",
+    "\n",
+    "# Initialize the StandardScaler to standardize the selected numeric columns\n",
+    "scaler = StandardScaler()\n",
+    "\n",
+    "# Apply the scaler to the selected columns. This transforms the data so that each feature\n",
+    "# has a mean of 0 and a standard deviation of 1, which is essential to prevent larger\n",
+    "# scale features from dominating the analysis, especially for distance-based algorithms like KNN.\n",
+    "standardized_cancer[columns_to_scale] = scaler.fit_transform(cancer[columns_to_scale])\n",
+    "\n",
+    "# Output the standardized dataframe with the scaled numeric columns\n",
+    "standardized_cancer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "493edfa6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# set the seed\n",
+    "np.random.seed(1)\n",
+    "\n",
+    "#split the data\n",
+    "cancer_train, cancer_test = train_test_split(\n",
+    "    standardized_cancer, train_size=0.75, stratify=standardized_cancer[\"diagnosis\"]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "607d3cbc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#k = 5\n",
+    "cancer.nsmallest(5,\"di\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "lcr-env",
+   "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": "3.11.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/02_activities/assignments/assignment_1.ipynb b/02_activities/assignments/assignment_1.ipynb
index 28d4df017..9b9d0d1a8 100644
--- a/02_activities/assignments/assignment_1.ipynb
+++ b/02_activities/assignments/assignment_1.ipynb
@@ -34,7 +34,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "4a3485d6-ba58-4660-a983-5680821c5719",
    "metadata": {},
    "outputs": [],
@@ -56,10 +56,288 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "a431d282-f9ca-4d5d-8912-71ffc9d8ea19",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>alcohol</th>\n",
+       "      <th>malic_acid</th>\n",
+       "      <th>ash</th>\n",
+       "      <th>alcalinity_of_ash</th>\n",
+       "      <th>magnesium</th>\n",
+       "      <th>total_phenols</th>\n",
+       "      <th>flavanoids</th>\n",
+       "      <th>nonflavanoid_phenols</th>\n",
+       "      <th>proanthocyanins</th>\n",
+       "      <th>color_intensity</th>\n",
+       "      <th>hue</th>\n",
+       "      <th>od280/od315_of_diluted_wines</th>\n",
+       "      <th>proline</th>\n",
+       "      <th>class</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>14.23</td>\n",
+       "      <td>1.71</td>\n",
+       "      <td>2.43</td>\n",
+       "      <td>15.6</td>\n",
+       "      <td>127.0</td>\n",
+       "      <td>2.80</td>\n",
+       "      <td>3.06</td>\n",
+       "      <td>0.28</td>\n",
+       "      <td>2.29</td>\n",
+       "      <td>5.64</td>\n",
+       "      <td>1.04</td>\n",
+       "      <td>3.92</td>\n",
+       "      <td>1065.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>13.20</td>\n",
+       "      <td>1.78</td>\n",
+       "      <td>2.14</td>\n",
+       "      <td>11.2</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>2.65</td>\n",
+       "      <td>2.76</td>\n",
+       "      <td>0.26</td>\n",
+       "      <td>1.28</td>\n",
+       "      <td>4.38</td>\n",
+       "      <td>1.05</td>\n",
+       "      <td>3.40</td>\n",
+       "      <td>1050.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>13.16</td>\n",
+       "      <td>2.36</td>\n",
+       "      <td>2.67</td>\n",
+       "      <td>18.6</td>\n",
+       "      <td>101.0</td>\n",
+       "      <td>2.80</td>\n",
+       "      <td>3.24</td>\n",
+       "      <td>0.30</td>\n",
+       "      <td>2.81</td>\n",
+       "      <td>5.68</td>\n",
+       "      <td>1.03</td>\n",
+       "      <td>3.17</td>\n",
+       "      <td>1185.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>14.37</td>\n",
+       "      <td>1.95</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>16.8</td>\n",
+       "      <td>113.0</td>\n",
+       "      <td>3.85</td>\n",
+       "      <td>3.49</td>\n",
+       "      <td>0.24</td>\n",
+       "      <td>2.18</td>\n",
+       "      <td>7.80</td>\n",
+       "      <td>0.86</td>\n",
+       "      <td>3.45</td>\n",
+       "      <td>1480.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>13.24</td>\n",
+       "      <td>2.59</td>\n",
+       "      <td>2.87</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>118.0</td>\n",
+       "      <td>2.80</td>\n",
+       "      <td>2.69</td>\n",
+       "      <td>0.39</td>\n",
+       "      <td>1.82</td>\n",
+       "      <td>4.32</td>\n",
+       "      <td>1.04</td>\n",
+       "      <td>2.93</td>\n",
+       "      <td>735.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>173</th>\n",
+       "      <td>13.71</td>\n",
+       "      <td>5.65</td>\n",
+       "      <td>2.45</td>\n",
+       "      <td>20.5</td>\n",
+       "      <td>95.0</td>\n",
+       "      <td>1.68</td>\n",
+       "      <td>0.61</td>\n",
+       "      <td>0.52</td>\n",
+       "      <td>1.06</td>\n",
+       "      <td>7.70</td>\n",
+       "      <td>0.64</td>\n",
+       "      <td>1.74</td>\n",
+       "      <td>740.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>174</th>\n",
+       "      <td>13.40</td>\n",
+       "      <td>3.91</td>\n",
+       "      <td>2.48</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>102.0</td>\n",
+       "      <td>1.80</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>0.43</td>\n",
+       "      <td>1.41</td>\n",
+       "      <td>7.30</td>\n",
+       "      <td>0.70</td>\n",
+       "      <td>1.56</td>\n",
+       "      <td>750.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>175</th>\n",
+       "      <td>13.27</td>\n",
+       "      <td>4.28</td>\n",
+       "      <td>2.26</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>120.0</td>\n",
+       "      <td>1.59</td>\n",
+       "      <td>0.69</td>\n",
+       "      <td>0.43</td>\n",
+       "      <td>1.35</td>\n",
+       "      <td>10.20</td>\n",
+       "      <td>0.59</td>\n",
+       "      <td>1.56</td>\n",
+       "      <td>835.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>176</th>\n",
+       "      <td>13.17</td>\n",
+       "      <td>2.59</td>\n",
+       "      <td>2.37</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>120.0</td>\n",
+       "      <td>1.65</td>\n",
+       "      <td>0.68</td>\n",
+       "      <td>0.53</td>\n",
+       "      <td>1.46</td>\n",
+       "      <td>9.30</td>\n",
+       "      <td>0.60</td>\n",
+       "      <td>1.62</td>\n",
+       "      <td>840.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>177</th>\n",
+       "      <td>14.13</td>\n",
+       "      <td>4.10</td>\n",
+       "      <td>2.74</td>\n",
+       "      <td>24.5</td>\n",
+       "      <td>96.0</td>\n",
+       "      <td>2.05</td>\n",
+       "      <td>0.76</td>\n",
+       "      <td>0.56</td>\n",
+       "      <td>1.35</td>\n",
+       "      <td>9.20</td>\n",
+       "      <td>0.61</td>\n",
+       "      <td>1.60</td>\n",
+       "      <td>560.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>178 rows × 14 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     alcohol  malic_acid   ash  alcalinity_of_ash  magnesium  total_phenols  \\\n",
+       "0      14.23        1.71  2.43               15.6      127.0           2.80   \n",
+       "1      13.20        1.78  2.14               11.2      100.0           2.65   \n",
+       "2      13.16        2.36  2.67               18.6      101.0           2.80   \n",
+       "3      14.37        1.95  2.50               16.8      113.0           3.85   \n",
+       "4      13.24        2.59  2.87               21.0      118.0           2.80   \n",
+       "..       ...         ...   ...                ...        ...            ...   \n",
+       "173    13.71        5.65  2.45               20.5       95.0           1.68   \n",
+       "174    13.40        3.91  2.48               23.0      102.0           1.80   \n",
+       "175    13.27        4.28  2.26               20.0      120.0           1.59   \n",
+       "176    13.17        2.59  2.37               20.0      120.0           1.65   \n",
+       "177    14.13        4.10  2.74               24.5       96.0           2.05   \n",
+       "\n",
+       "     flavanoids  nonflavanoid_phenols  proanthocyanins  color_intensity   hue  \\\n",
+       "0          3.06                  0.28             2.29             5.64  1.04   \n",
+       "1          2.76                  0.26             1.28             4.38  1.05   \n",
+       "2          3.24                  0.30             2.81             5.68  1.03   \n",
+       "3          3.49                  0.24             2.18             7.80  0.86   \n",
+       "4          2.69                  0.39             1.82             4.32  1.04   \n",
+       "..          ...                   ...              ...              ...   ...   \n",
+       "173        0.61                  0.52             1.06             7.70  0.64   \n",
+       "174        0.75                  0.43             1.41             7.30  0.70   \n",
+       "175        0.69                  0.43             1.35            10.20  0.59   \n",
+       "176        0.68                  0.53             1.46             9.30  0.60   \n",
+       "177        0.76                  0.56             1.35             9.20  0.61   \n",
+       "\n",
+       "     od280/od315_of_diluted_wines  proline  class  \n",
+       "0                            3.92   1065.0      0  \n",
+       "1                            3.40   1050.0      0  \n",
+       "2                            3.17   1185.0      0  \n",
+       "3                            3.45   1480.0      0  \n",
+       "4                            2.93    735.0      0  \n",
+       "..                            ...      ...    ...  \n",
+       "173                          1.74    740.0      2  \n",
+       "174                          1.56    750.0      2  \n",
+       "175                          1.56    835.0      2  \n",
+       "176                          1.62    840.0      2  \n",
+       "177                          1.60    560.0      2  \n",
+       "\n",
+       "[178 rows x 14 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "from sklearn.datasets import load_wine\n",
     "\n",
@@ -91,12 +369,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "56916892",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The dataset contains 178 observations (rows).\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your answer here"
+    "# 178 - based on info above\n",
+    "# get the # of rows\n",
+    "\n",
+    "num_rows = wine_df.shape[0] # shape [0] # of rows\n",
+    "\n",
+    "print(f\"The dataset contains {num_rows} observations (rows).\")"
    ]
   },
   {
@@ -109,12 +400,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "df0ef103",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The dataset contains 14 variables (columns).\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your answer here"
+    "# 14 - based on info above\n",
+    "# get # of columns\n",
+    "num_columns = wine_df.shape[1] # shape [1] # of columsn\n",
+    "print(f\"The dataset contains {num_columns} variables (columns).\")"
    ]
   },
   {
@@ -127,12 +429,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "47989426",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[0, 1, 2]\n",
+       "Categories (3, int64): [0, 1, 2]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your answer here"
+    "# check the variable type of the response variable\n",
+    "#wine_df[\"class\"].dtype - first attempt\n",
+    "wine_df[\"class\"] = wine_df[\"class\"].astype(\"category\") # 2nd attempt for clarity \n",
+    " \n",
+    "# check levels (unique value)\n",
+    "wine_df[\"class\"].unique() "
    ]
   },
   {
@@ -146,12 +465,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "bd7b0910",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The dataset contains 13 predictor variables.\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your answer here"
+    "# count all predictor variables, excluding class \n",
+    "num_predictors = wine_df.drop(columns=[\"class\"]).shape[1]\n",
+    "\n",
+    "print(f\"The dataset contains {num_predictors} predictor variables.\")"
    ]
   },
   {
@@ -175,10 +505,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "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 +561,7 @@
    "id": "403ef0bb",
    "metadata": {},
    "source": [
-    "> Your answer here..."
+    "Standardization is important because KNN is a distance-based algorithm. Without standardization, predictors with larger scales would disproportionately influence distance calculations. Standardizing ensures all predictors contribute equally to the model. "
    ]
   },
   {
@@ -220,7 +577,7 @@
    "id": "fdee5a15",
    "metadata": {},
    "source": [
-    "> Your answer here..."
+    ">The response variable class is categorical, not continuous. Standardizing it would alter the class labels and remove their meaning. Standardization is only applied to predictor variables, not the target variable in classification tasks."
    ]
   },
   {
@@ -236,7 +593,7 @@
    "id": "f0676c21",
    "metadata": {},
    "source": [
-    "> Your answer here..."
+    "> Setting a random seed ensures reproducibility by fixing the randomness in model training and evaluation. The specific seed value does not matter, as long as it is used consistently."
    ]
   },
   {
@@ -251,7 +608,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "72c101f2",
    "metadata": {},
    "outputs": [],
@@ -259,9 +616,12 @@
     "# 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",
+    "# seperate predictors and responses\n",
+    "X = wine_df.drop(columns=[\"class\"])\n",
+    "y = wine_df[\"class\"]\n",
     "\n",
-    "# Your code here ..."
+    "# split the data\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size= 0.75, shuffle= True, stratify= y)"
    ]
   },
   {
@@ -284,12 +644,90 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
+   "id": "f0b56247",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set seed for reproducibility\n",
+    "np.random.seed(123)\n",
+    "\n",
+    "# Response variable\n",
+    "y = wine_df[\"class\"]\n",
+    "\n",
+    "# Split the SCALED predictors (as requested by TA) and y\n",
+    "X_train_scaled, X_test_scaled, y_train, y_test = train_test_split(\n",
+    "    predictors_standardized,   # <--- scaled predictors\n",
+    "    y,\n",
+    "    train_size=0.75,\n",
+    "    shuffle=True,\n",
+    "    stratify=y,\n",
+    "    random_state=123           # makes the split reproducible \n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
    "id": "08818c64",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your code here..."
+    "# Step 1. Initalize th KNN classiffer k = 5, 5 nearest neighbours\n",
+    "knn = KNeighborsClassifier()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "eff9108a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# step 2. define our parameter grid\n",
+    "parameter_grid = {\n",
+    "    \"n_neighbors\" : range(1, 51)\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "30ca4abf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#step 3. implement GridSearchCV with 10 fold\n",
+    "wine_tune_grid = GridSearchCV(\n",
+    "    estimator = knn, \n",
+    "    param_grid= parameter_grid,\n",
+    "    cv = 10\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "ed25d434",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Best n_neighbors: 7\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Step 4: Fit on the training data (use scaled predictors)\n",
+    "wine_tune_grid.fit(X_train_scaled, y_train)\n",
+    "\n",
+    "# Best hyperparameter value\n",
+    "best_k = wine_tune_grid.best_params_[\"n_neighbors\"]\n",
+    "print(\"Best n_neighbors:\", best_k)\n",
+    "# was 20 before, now 7!"
    ]
   },
   {
@@ -308,9 +746,31 @@
    "execution_count": null,
    "id": "ffefa9f2",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test set accuracy: 0.9333333333333333\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your code here..."
+    "# Initialize KNN using the best value of n_neighbors from grid search\n",
+    "best_knn = KNeighborsClassifier(n_neighbors=best_k)\n",
+    "\n",
+    "# Fit the model on the training data # based on feedback changing this to X_train_scaled\n",
+    "best_knn.fit(X_train_scaled, y_train)\n",
+    "\n",
+    "# Make predictions on the test set\n",
+    "y_pred = best_knn.predict(X_test_scaled)\n",
+    "\n",
+    "# Evaluate model performance using accuracy\n",
+    "test_accuracy = accuracy_score(y_test, y_pred)\n",
+    "\n",
+    "print(\"Test set accuracy:\", test_accuracy)\n",
+    "\n",
+    "# was this anwser previously: 0.7333, when I used x_test and ran this with the above chnages I got 0.71, now with x_test_scaled I got 0.4 - progress #update 3, now using the X_train_scaled set i am at 0.9333 - that seems a lot better!"
    ]
   },
   {
@@ -365,7 +825,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.10.4",
+   "display_name": "lcr-env",
    "language": "python",
    "name": "python3"
   },
@@ -379,12 +839,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.19"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "497a84dc8fec8cf8d24e7e87b6d954c9a18a327edc66feb9b9ea7e9e72cc5c7e"
-   }
+   "version": "3.11.14"
   }
  },
  "nbformat": 4,