From fc97fbe84ae3220f5a73f0161aba54ff8c55cc3f Mon Sep 17 00:00:00 2001
From: juliagallucci <78810440+juliagallucci@users.noreply.github.com>
Date: Tue, 13 May 2025 20:48:21 -0400
Subject: [PATCH 1/3] Add files via upload

---
 .../live_code/live_code_05_13_2025.ipynb      | 1272 +++++++++++++++++
 1 file changed, 1272 insertions(+)
 create mode 100644 04_this_cohort/live_code/live_code_05_13_2025.ipynb

diff --git a/04_this_cohort/live_code/live_code_05_13_2025.ipynb b/04_this_cohort/live_code/live_code_05_13_2025.ipynb
new file mode 100644
index 000000000..2721e155c
--- /dev/null
+++ b/04_this_cohort/live_code/live_code_05_13_2025.ipynb
@@ -0,0 +1,1272 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Import libraries\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.colors as mcolors\n",
+    "from mpl_toolkits import mplot3d"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>diagnosis</th>\n",
+       "      <th>radius_mean</th>\n",
+       "      <th>texture_mean</th>\n",
+       "      <th>perimeter_mean</th>\n",
+       "      <th>area_mean</th>\n",
+       "      <th>smoothness_mean</th>\n",
+       "      <th>compactness_mean</th>\n",
+       "      <th>concavity_mean</th>\n",
+       "      <th>concave points_mean</th>\n",
+       "      <th>...</th>\n",
+       "      <th>radius_worst</th>\n",
+       "      <th>texture_worst</th>\n",
+       "      <th>perimeter_worst</th>\n",
+       "      <th>area_worst</th>\n",
+       "      <th>smoothness_worst</th>\n",
+       "      <th>compactness_worst</th>\n",
+       "      <th>concavity_worst</th>\n",
+       "      <th>concave points_worst</th>\n",
+       "      <th>symmetry_worst</th>\n",
+       "      <th>fractal_dimension_worst</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>842302</td>\n",
+       "      <td>M</td>\n",
+       "      <td>17.99</td>\n",
+       "      <td>10.38</td>\n",
+       "      <td>122.80</td>\n",
+       "      <td>1001.0</td>\n",
+       "      <td>0.11840</td>\n",
+       "      <td>0.27760</td>\n",
+       "      <td>0.30010</td>\n",
+       "      <td>0.14710</td>\n",
+       "      <td>...</td>\n",
+       "      <td>25.380</td>\n",
+       "      <td>17.33</td>\n",
+       "      <td>184.60</td>\n",
+       "      <td>2019.0</td>\n",
+       "      <td>0.16220</td>\n",
+       "      <td>0.66560</td>\n",
+       "      <td>0.7119</td>\n",
+       "      <td>0.2654</td>\n",
+       "      <td>0.4601</td>\n",
+       "      <td>0.11890</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>842517</td>\n",
+       "      <td>M</td>\n",
+       "      <td>20.57</td>\n",
+       "      <td>17.77</td>\n",
+       "      <td>132.90</td>\n",
+       "      <td>1326.0</td>\n",
+       "      <td>0.08474</td>\n",
+       "      <td>0.07864</td>\n",
+       "      <td>0.08690</td>\n",
+       "      <td>0.07017</td>\n",
+       "      <td>...</td>\n",
+       "      <td>24.990</td>\n",
+       "      <td>23.41</td>\n",
+       "      <td>158.80</td>\n",
+       "      <td>1956.0</td>\n",
+       "      <td>0.12380</td>\n",
+       "      <td>0.18660</td>\n",
+       "      <td>0.2416</td>\n",
+       "      <td>0.1860</td>\n",
+       "      <td>0.2750</td>\n",
+       "      <td>0.08902</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>84300903</td>\n",
+       "      <td>M</td>\n",
+       "      <td>19.69</td>\n",
+       "      <td>21.25</td>\n",
+       "      <td>130.00</td>\n",
+       "      <td>1203.0</td>\n",
+       "      <td>0.10960</td>\n",
+       "      <td>0.15990</td>\n",
+       "      <td>0.19740</td>\n",
+       "      <td>0.12790</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23.570</td>\n",
+       "      <td>25.53</td>\n",
+       "      <td>152.50</td>\n",
+       "      <td>1709.0</td>\n",
+       "      <td>0.14440</td>\n",
+       "      <td>0.42450</td>\n",
+       "      <td>0.4504</td>\n",
+       "      <td>0.2430</td>\n",
+       "      <td>0.3613</td>\n",
+       "      <td>0.08758</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>84348301</td>\n",
+       "      <td>M</td>\n",
+       "      <td>11.42</td>\n",
+       "      <td>20.38</td>\n",
+       "      <td>77.58</td>\n",
+       "      <td>386.1</td>\n",
+       "      <td>0.14250</td>\n",
+       "      <td>0.28390</td>\n",
+       "      <td>0.24140</td>\n",
+       "      <td>0.10520</td>\n",
+       "      <td>...</td>\n",
+       "      <td>14.910</td>\n",
+       "      <td>26.50</td>\n",
+       "      <td>98.87</td>\n",
+       "      <td>567.7</td>\n",
+       "      <td>0.20980</td>\n",
+       "      <td>0.86630</td>\n",
+       "      <td>0.6869</td>\n",
+       "      <td>0.2575</td>\n",
+       "      <td>0.6638</td>\n",
+       "      <td>0.17300</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>84358402</td>\n",
+       "      <td>M</td>\n",
+       "      <td>20.29</td>\n",
+       "      <td>14.34</td>\n",
+       "      <td>135.10</td>\n",
+       "      <td>1297.0</td>\n",
+       "      <td>0.10030</td>\n",
+       "      <td>0.13280</td>\n",
+       "      <td>0.19800</td>\n",
+       "      <td>0.10430</td>\n",
+       "      <td>...</td>\n",
+       "      <td>22.540</td>\n",
+       "      <td>16.67</td>\n",
+       "      <td>152.20</td>\n",
+       "      <td>1575.0</td>\n",
+       "      <td>0.13740</td>\n",
+       "      <td>0.20500</td>\n",
+       "      <td>0.4000</td>\n",
+       "      <td>0.1625</td>\n",
+       "      <td>0.2364</td>\n",
+       "      <td>0.07678</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>564</th>\n",
+       "      <td>926424</td>\n",
+       "      <td>M</td>\n",
+       "      <td>21.56</td>\n",
+       "      <td>22.39</td>\n",
+       "      <td>142.00</td>\n",
+       "      <td>1479.0</td>\n",
+       "      <td>0.11100</td>\n",
+       "      <td>0.11590</td>\n",
+       "      <td>0.24390</td>\n",
+       "      <td>0.13890</td>\n",
+       "      <td>...</td>\n",
+       "      <td>25.450</td>\n",
+       "      <td>26.40</td>\n",
+       "      <td>166.10</td>\n",
+       "      <td>2027.0</td>\n",
+       "      <td>0.14100</td>\n",
+       "      <td>0.21130</td>\n",
+       "      <td>0.4107</td>\n",
+       "      <td>0.2216</td>\n",
+       "      <td>0.2060</td>\n",
+       "      <td>0.07115</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>565</th>\n",
+       "      <td>926682</td>\n",
+       "      <td>M</td>\n",
+       "      <td>20.13</td>\n",
+       "      <td>28.25</td>\n",
+       "      <td>131.20</td>\n",
+       "      <td>1261.0</td>\n",
+       "      <td>0.09780</td>\n",
+       "      <td>0.10340</td>\n",
+       "      <td>0.14400</td>\n",
+       "      <td>0.09791</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23.690</td>\n",
+       "      <td>38.25</td>\n",
+       "      <td>155.00</td>\n",
+       "      <td>1731.0</td>\n",
+       "      <td>0.11660</td>\n",
+       "      <td>0.19220</td>\n",
+       "      <td>0.3215</td>\n",
+       "      <td>0.1628</td>\n",
+       "      <td>0.2572</td>\n",
+       "      <td>0.06637</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>566</th>\n",
+       "      <td>926954</td>\n",
+       "      <td>M</td>\n",
+       "      <td>16.60</td>\n",
+       "      <td>28.08</td>\n",
+       "      <td>108.30</td>\n",
+       "      <td>858.1</td>\n",
+       "      <td>0.08455</td>\n",
+       "      <td>0.10230</td>\n",
+       "      <td>0.09251</td>\n",
+       "      <td>0.05302</td>\n",
+       "      <td>...</td>\n",
+       "      <td>18.980</td>\n",
+       "      <td>34.12</td>\n",
+       "      <td>126.70</td>\n",
+       "      <td>1124.0</td>\n",
+       "      <td>0.11390</td>\n",
+       "      <td>0.30940</td>\n",
+       "      <td>0.3403</td>\n",
+       "      <td>0.1418</td>\n",
+       "      <td>0.2218</td>\n",
+       "      <td>0.07820</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>567</th>\n",
+       "      <td>927241</td>\n",
+       "      <td>M</td>\n",
+       "      <td>20.60</td>\n",
+       "      <td>29.33</td>\n",
+       "      <td>140.10</td>\n",
+       "      <td>1265.0</td>\n",
+       "      <td>0.11780</td>\n",
+       "      <td>0.27700</td>\n",
+       "      <td>0.35140</td>\n",
+       "      <td>0.15200</td>\n",
+       "      <td>...</td>\n",
+       "      <td>25.740</td>\n",
+       "      <td>39.42</td>\n",
+       "      <td>184.60</td>\n",
+       "      <td>1821.0</td>\n",
+       "      <td>0.16500</td>\n",
+       "      <td>0.86810</td>\n",
+       "      <td>0.9387</td>\n",
+       "      <td>0.2650</td>\n",
+       "      <td>0.4087</td>\n",
+       "      <td>0.12400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>568</th>\n",
+       "      <td>92751</td>\n",
+       "      <td>B</td>\n",
+       "      <td>7.76</td>\n",
+       "      <td>24.54</td>\n",
+       "      <td>47.92</td>\n",
+       "      <td>181.0</td>\n",
+       "      <td>0.05263</td>\n",
+       "      <td>0.04362</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>9.456</td>\n",
+       "      <td>30.37</td>\n",
+       "      <td>59.16</td>\n",
+       "      <td>268.6</td>\n",
+       "      <td>0.08996</td>\n",
+       "      <td>0.06444</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.2871</td>\n",
+       "      <td>0.07039</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>569 rows × 32 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
+       "0      842302         M        17.99         10.38          122.80     1001.0   \n",
+       "1      842517         M        20.57         17.77          132.90     1326.0   \n",
+       "2    84300903         M        19.69         21.25          130.00     1203.0   \n",
+       "3    84348301         M        11.42         20.38           77.58      386.1   \n",
+       "4    84358402         M        20.29         14.34          135.10     1297.0   \n",
+       "..        ...       ...          ...           ...             ...        ...   \n",
+       "564    926424         M        21.56         22.39          142.00     1479.0   \n",
+       "565    926682         M        20.13         28.25          131.20     1261.0   \n",
+       "566    926954         M        16.60         28.08          108.30      858.1   \n",
+       "567    927241         M        20.60         29.33          140.10     1265.0   \n",
+       "568     92751         B         7.76         24.54           47.92      181.0   \n",
+       "\n",
+       "     smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
+       "0            0.11840           0.27760         0.30010              0.14710   \n",
+       "1            0.08474           0.07864         0.08690              0.07017   \n",
+       "2            0.10960           0.15990         0.19740              0.12790   \n",
+       "3            0.14250           0.28390         0.24140              0.10520   \n",
+       "4            0.10030           0.13280         0.19800              0.10430   \n",
+       "..               ...               ...             ...                  ...   \n",
+       "564          0.11100           0.11590         0.24390              0.13890   \n",
+       "565          0.09780           0.10340         0.14400              0.09791   \n",
+       "566          0.08455           0.10230         0.09251              0.05302   \n",
+       "567          0.11780           0.27700         0.35140              0.15200   \n",
+       "568          0.05263           0.04362         0.00000              0.00000   \n",
+       "\n",
+       "     ...  radius_worst  texture_worst  perimeter_worst  area_worst  \\\n",
+       "0    ...        25.380          17.33           184.60      2019.0   \n",
+       "1    ...        24.990          23.41           158.80      1956.0   \n",
+       "2    ...        23.570          25.53           152.50      1709.0   \n",
+       "3    ...        14.910          26.50            98.87       567.7   \n",
+       "4    ...        22.540          16.67           152.20      1575.0   \n",
+       "..   ...           ...            ...              ...         ...   \n",
+       "564  ...        25.450          26.40           166.10      2027.0   \n",
+       "565  ...        23.690          38.25           155.00      1731.0   \n",
+       "566  ...        18.980          34.12           126.70      1124.0   \n",
+       "567  ...        25.740          39.42           184.60      1821.0   \n",
+       "568  ...         9.456          30.37            59.16       268.6   \n",
+       "\n",
+       "     smoothness_worst  compactness_worst  concavity_worst  \\\n",
+       "0             0.16220            0.66560           0.7119   \n",
+       "1             0.12380            0.18660           0.2416   \n",
+       "2             0.14440            0.42450           0.4504   \n",
+       "3             0.20980            0.86630           0.6869   \n",
+       "4             0.13740            0.20500           0.4000   \n",
+       "..                ...                ...              ...   \n",
+       "564           0.14100            0.21130           0.4107   \n",
+       "565           0.11660            0.19220           0.3215   \n",
+       "566           0.11390            0.30940           0.3403   \n",
+       "567           0.16500            0.86810           0.9387   \n",
+       "568           0.08996            0.06444           0.0000   \n",
+       "\n",
+       "     concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
+       "0                  0.2654          0.4601                  0.11890  \n",
+       "1                  0.1860          0.2750                  0.08902  \n",
+       "2                  0.2430          0.3613                  0.08758  \n",
+       "3                  0.2575          0.6638                  0.17300  \n",
+       "4                  0.1625          0.2364                  0.07678  \n",
+       "..                    ...             ...                      ...  \n",
+       "564                0.2216          0.2060                  0.07115  \n",
+       "565                0.1628          0.2572                  0.06637  \n",
+       "566                0.1418          0.2218                  0.07820  \n",
+       "567                0.2650          0.4087                  0.12400  \n",
+       "568                0.0000          0.2871                  0.07039  \n",
+       "\n",
+       "[569 rows x 32 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cancer = pd.read_csv('dataset/wdbc.csv')\n",
+    "cancer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "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()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['M', 'B'], dtype=object)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cancer['diagnosis'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#clean up diagnosis column\n",
+    "\n",
+    "cancer['diagnosis'] = cancer['diagnosis'].replace({\n",
+    "    \"M\": \"Malignant\",\n",
+    "    \"B\" : \"Benign\"\n",
+    "})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Malignant', 'Benign'], dtype=object)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cancer['diagnosis'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Benign       0.627417\n",
+       "Malignant    0.372583\n",
+       "Name: diagnosis, dtype: float64"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cancer['diagnosis'].value_counts(normalize = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "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()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "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": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#new observation\n",
+    "new_obs_Perimeter = 97\n",
+    "new_obs_Concavity = 0.2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cancer['dist_from_new'] = (\n",
+    "(cancer['perimeter_mean'] - new_obs_Perimeter)**2 +\n",
+    "(cancer['concavity_mean'] - new_obs_Concavity)**2\n",
+    ")**(1/2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#k = 5\n",
+    "\n",
+    "nearest_5 = cancer.nsmallest(5, \"dist_from_new\")[[\n",
+    "    \"perimeter_mean\",\n",
+    "    \"concavity_mean\",\n",
+    "    \"diagnosis\",\n",
+    "    \"dist_from_new\"\n",
+    "]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>perimeter_mean</th>\n",
+       "      <th>concavity_mean</th>\n",
+       "      <th>diagnosis</th>\n",
+       "      <th>dist_from_new</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>291</th>\n",
+       "      <td>97.03</td>\n",
+       "      <td>0.05940</td>\n",
+       "      <td>Benign</td>\n",
+       "      <td>0.143765</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>138</th>\n",
+       "      <td>96.85</td>\n",
+       "      <td>0.15390</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>0.156924</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>96.73</td>\n",
+       "      <td>0.16390</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>0.272403</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>514</th>\n",
+       "      <td>97.26</td>\n",
+       "      <td>0.07486</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>0.288548</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54</th>\n",
+       "      <td>97.26</td>\n",
+       "      <td>0.05253</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>0.298910</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     perimeter_mean  concavity_mean  diagnosis  dist_from_new\n",
+       "291           97.03         0.05940     Benign       0.143765\n",
+       "138           96.85         0.15390  Malignant       0.156924\n",
+       "15            96.73         0.16390  Malignant       0.272403\n",
+       "514           97.26         0.07486  Malignant       0.288548\n",
+       "54            97.26         0.05253  Malignant       0.298910"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nearest_5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<module 'matplotlib.pyplot' from '/Users/juliagallucci/miniconda3/lib/python3.10/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 hand\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,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#new observation\n",
+    "new_obs_Perimeter = 97\n",
+    "new_obs_Concavity = 0.2\n",
+    "new_obs_Symmetry = 0.22"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cancer['dist_from_new'] = (\n",
+    "    (cancer['perimeter_mean'] - new_obs_Perimeter)**2 +\n",
+    "(cancer['concavity_mean'] - new_obs_Concavity)**2 +\n",
+    "(cancer['symmetry_mean'] - new_obs_Symmetry)**2\n",
+    ")**(1/2)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nearest_5 = cancer.nsmallest(5, 'dist_from_new')[[\n",
+    "    \"perimeter_mean\",\n",
+    "    \"concavity_mean\",\n",
+    "    \"symmetry_mean\",\n",
+    "    \"diagnosis\",\n",
+    "    \"dist_from_new\"\n",
+    "]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>perimeter_mean</th>\n",
+       "      <th>concavity_mean</th>\n",
+       "      <th>symmetry_mean</th>\n",
+       "      <th>diagnosis</th>\n",
+       "      <th>dist_from_new</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>291</th>\n",
+       "      <td>97.03</td>\n",
+       "      <td>0.05940</td>\n",
+       "      <td>0.1879</td>\n",
+       "      <td>Benign</td>\n",
+       "      <td>0.147305</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>138</th>\n",
+       "      <td>96.85</td>\n",
+       "      <td>0.15390</td>\n",
+       "      <td>0.1957</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>0.158795</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>96.73</td>\n",
+       "      <td>0.16390</td>\n",
+       "      <td>0.2303</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>0.272597</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>514</th>\n",
+       "      <td>97.26</td>\n",
+       "      <td>0.07486</td>\n",
+       "      <td>0.1561</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>0.295539</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54</th>\n",
+       "      <td>97.26</td>\n",
+       "      <td>0.05253</td>\n",
+       "      <td>0.1616</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>0.304562</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     perimeter_mean  concavity_mean  symmetry_mean  diagnosis  dist_from_new\n",
+       "291           97.03         0.05940         0.1879     Benign       0.147305\n",
+       "138           96.85         0.15390         0.1957  Malignant       0.158795\n",
+       "15            96.73         0.16390         0.2303  Malignant       0.272597\n",
+       "514           97.26         0.07486         0.1561  Malignant       0.295539\n",
+       "54            97.26         0.05253         0.1616  Malignant       0.304562"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nearest_5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn import set_config\n",
+    "set_config(transform_output = \"pandas\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.neighbors import KNeighborsClassifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "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": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cancer_train = cancer[[\"diagnosis\",\"perimeter_mean\",\"concavity_mean\"]]\n",
+    "cancer_train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "KNeighborsClassifier()"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "knn = KNeighborsClassifier(n_neighbors = 5)\n",
+    "knn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# predictor - x, response - y\n",
+    "X = cancer_train[[\"perimeter_mean\",\"concavity_mean\"]]\n",
+    "y = cancer_train[\"diagnosis\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "KNeighborsClassifier()"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#fit knn model to data\n",
+    "\n",
+    "knn.fit(X,y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "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",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>100</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   perimeter_mean  concavity_mean\n",
+       "0              97             0.2\n",
+       "1             100             1.0"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "new_obs = pd.DataFrame({\"perimeter_mean\":[97,100],\"concavity_mean\":[0.20,1]})\n",
+    "new_obs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Malignant', 'Malignant'], dtype=object)"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "knn.predict(new_obs)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

From 1df54b03f545bef6cb6d9144b490f9b416fc50fa Mon Sep 17 00:00:00 2001
From: juliagallucci <78810440+juliagallucci@users.noreply.github.com>
Date: Thu, 15 May 2025 20:39:23 -0400
Subject: [PATCH 2/3] Add files via upload

---
 .../live_code/live_code_05_15_2025.ipynb      | 2677 +++++++++++++++++
 1 file changed, 2677 insertions(+)
 create mode 100644 04_this_cohort/live_code/live_code_05_15_2025.ipynb

diff --git a/04_this_cohort/live_code/live_code_05_15_2025.ipynb b/04_this_cohort/live_code/live_code_05_15_2025.ipynb
new file mode 100644
index 000000000..f3b85ff17
--- /dev/null
+++ b/04_this_cohort/live_code/live_code_05_15_2025.ipynb
@@ -0,0 +1,2677 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.metrics import recall_score, precision_score\n",
+    "from sklearn.model_selection import cross_validate\n",
+    "from sklearn.model_selection import GridSearchCV"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>diagnosis</th>\n",
+       "      <th>radius_mean</th>\n",
+       "      <th>texture_mean</th>\n",
+       "      <th>perimeter_mean</th>\n",
+       "      <th>area_mean</th>\n",
+       "      <th>smoothness_mean</th>\n",
+       "      <th>compactness_mean</th>\n",
+       "      <th>concavity_mean</th>\n",
+       "      <th>concave points_mean</th>\n",
+       "      <th>...</th>\n",
+       "      <th>radius_worst</th>\n",
+       "      <th>texture_worst</th>\n",
+       "      <th>perimeter_worst</th>\n",
+       "      <th>area_worst</th>\n",
+       "      <th>smoothness_worst</th>\n",
+       "      <th>compactness_worst</th>\n",
+       "      <th>concavity_worst</th>\n",
+       "      <th>concave points_worst</th>\n",
+       "      <th>symmetry_worst</th>\n",
+       "      <th>fractal_dimension_worst</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>842302</td>\n",
+       "      <td>M</td>\n",
+       "      <td>17.99</td>\n",
+       "      <td>10.38</td>\n",
+       "      <td>122.80</td>\n",
+       "      <td>1001.0</td>\n",
+       "      <td>0.11840</td>\n",
+       "      <td>0.27760</td>\n",
+       "      <td>0.30010</td>\n",
+       "      <td>0.14710</td>\n",
+       "      <td>...</td>\n",
+       "      <td>25.380</td>\n",
+       "      <td>17.33</td>\n",
+       "      <td>184.60</td>\n",
+       "      <td>2019.0</td>\n",
+       "      <td>0.16220</td>\n",
+       "      <td>0.66560</td>\n",
+       "      <td>0.7119</td>\n",
+       "      <td>0.2654</td>\n",
+       "      <td>0.4601</td>\n",
+       "      <td>0.11890</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>842517</td>\n",
+       "      <td>M</td>\n",
+       "      <td>20.57</td>\n",
+       "      <td>17.77</td>\n",
+       "      <td>132.90</td>\n",
+       "      <td>1326.0</td>\n",
+       "      <td>0.08474</td>\n",
+       "      <td>0.07864</td>\n",
+       "      <td>0.08690</td>\n",
+       "      <td>0.07017</td>\n",
+       "      <td>...</td>\n",
+       "      <td>24.990</td>\n",
+       "      <td>23.41</td>\n",
+       "      <td>158.80</td>\n",
+       "      <td>1956.0</td>\n",
+       "      <td>0.12380</td>\n",
+       "      <td>0.18660</td>\n",
+       "      <td>0.2416</td>\n",
+       "      <td>0.1860</td>\n",
+       "      <td>0.2750</td>\n",
+       "      <td>0.08902</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>84300903</td>\n",
+       "      <td>M</td>\n",
+       "      <td>19.69</td>\n",
+       "      <td>21.25</td>\n",
+       "      <td>130.00</td>\n",
+       "      <td>1203.0</td>\n",
+       "      <td>0.10960</td>\n",
+       "      <td>0.15990</td>\n",
+       "      <td>0.19740</td>\n",
+       "      <td>0.12790</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23.570</td>\n",
+       "      <td>25.53</td>\n",
+       "      <td>152.50</td>\n",
+       "      <td>1709.0</td>\n",
+       "      <td>0.14440</td>\n",
+       "      <td>0.42450</td>\n",
+       "      <td>0.4504</td>\n",
+       "      <td>0.2430</td>\n",
+       "      <td>0.3613</td>\n",
+       "      <td>0.08758</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>84348301</td>\n",
+       "      <td>M</td>\n",
+       "      <td>11.42</td>\n",
+       "      <td>20.38</td>\n",
+       "      <td>77.58</td>\n",
+       "      <td>386.1</td>\n",
+       "      <td>0.14250</td>\n",
+       "      <td>0.28390</td>\n",
+       "      <td>0.24140</td>\n",
+       "      <td>0.10520</td>\n",
+       "      <td>...</td>\n",
+       "      <td>14.910</td>\n",
+       "      <td>26.50</td>\n",
+       "      <td>98.87</td>\n",
+       "      <td>567.7</td>\n",
+       "      <td>0.20980</td>\n",
+       "      <td>0.86630</td>\n",
+       "      <td>0.6869</td>\n",
+       "      <td>0.2575</td>\n",
+       "      <td>0.6638</td>\n",
+       "      <td>0.17300</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>84358402</td>\n",
+       "      <td>M</td>\n",
+       "      <td>20.29</td>\n",
+       "      <td>14.34</td>\n",
+       "      <td>135.10</td>\n",
+       "      <td>1297.0</td>\n",
+       "      <td>0.10030</td>\n",
+       "      <td>0.13280</td>\n",
+       "      <td>0.19800</td>\n",
+       "      <td>0.10430</td>\n",
+       "      <td>...</td>\n",
+       "      <td>22.540</td>\n",
+       "      <td>16.67</td>\n",
+       "      <td>152.20</td>\n",
+       "      <td>1575.0</td>\n",
+       "      <td>0.13740</td>\n",
+       "      <td>0.20500</td>\n",
+       "      <td>0.4000</td>\n",
+       "      <td>0.1625</td>\n",
+       "      <td>0.2364</td>\n",
+       "      <td>0.07678</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>564</th>\n",
+       "      <td>926424</td>\n",
+       "      <td>M</td>\n",
+       "      <td>21.56</td>\n",
+       "      <td>22.39</td>\n",
+       "      <td>142.00</td>\n",
+       "      <td>1479.0</td>\n",
+       "      <td>0.11100</td>\n",
+       "      <td>0.11590</td>\n",
+       "      <td>0.24390</td>\n",
+       "      <td>0.13890</td>\n",
+       "      <td>...</td>\n",
+       "      <td>25.450</td>\n",
+       "      <td>26.40</td>\n",
+       "      <td>166.10</td>\n",
+       "      <td>2027.0</td>\n",
+       "      <td>0.14100</td>\n",
+       "      <td>0.21130</td>\n",
+       "      <td>0.4107</td>\n",
+       "      <td>0.2216</td>\n",
+       "      <td>0.2060</td>\n",
+       "      <td>0.07115</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>565</th>\n",
+       "      <td>926682</td>\n",
+       "      <td>M</td>\n",
+       "      <td>20.13</td>\n",
+       "      <td>28.25</td>\n",
+       "      <td>131.20</td>\n",
+       "      <td>1261.0</td>\n",
+       "      <td>0.09780</td>\n",
+       "      <td>0.10340</td>\n",
+       "      <td>0.14400</td>\n",
+       "      <td>0.09791</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23.690</td>\n",
+       "      <td>38.25</td>\n",
+       "      <td>155.00</td>\n",
+       "      <td>1731.0</td>\n",
+       "      <td>0.11660</td>\n",
+       "      <td>0.19220</td>\n",
+       "      <td>0.3215</td>\n",
+       "      <td>0.1628</td>\n",
+       "      <td>0.2572</td>\n",
+       "      <td>0.06637</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>566</th>\n",
+       "      <td>926954</td>\n",
+       "      <td>M</td>\n",
+       "      <td>16.60</td>\n",
+       "      <td>28.08</td>\n",
+       "      <td>108.30</td>\n",
+       "      <td>858.1</td>\n",
+       "      <td>0.08455</td>\n",
+       "      <td>0.10230</td>\n",
+       "      <td>0.09251</td>\n",
+       "      <td>0.05302</td>\n",
+       "      <td>...</td>\n",
+       "      <td>18.980</td>\n",
+       "      <td>34.12</td>\n",
+       "      <td>126.70</td>\n",
+       "      <td>1124.0</td>\n",
+       "      <td>0.11390</td>\n",
+       "      <td>0.30940</td>\n",
+       "      <td>0.3403</td>\n",
+       "      <td>0.1418</td>\n",
+       "      <td>0.2218</td>\n",
+       "      <td>0.07820</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>567</th>\n",
+       "      <td>927241</td>\n",
+       "      <td>M</td>\n",
+       "      <td>20.60</td>\n",
+       "      <td>29.33</td>\n",
+       "      <td>140.10</td>\n",
+       "      <td>1265.0</td>\n",
+       "      <td>0.11780</td>\n",
+       "      <td>0.27700</td>\n",
+       "      <td>0.35140</td>\n",
+       "      <td>0.15200</td>\n",
+       "      <td>...</td>\n",
+       "      <td>25.740</td>\n",
+       "      <td>39.42</td>\n",
+       "      <td>184.60</td>\n",
+       "      <td>1821.0</td>\n",
+       "      <td>0.16500</td>\n",
+       "      <td>0.86810</td>\n",
+       "      <td>0.9387</td>\n",
+       "      <td>0.2650</td>\n",
+       "      <td>0.4087</td>\n",
+       "      <td>0.12400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>568</th>\n",
+       "      <td>92751</td>\n",
+       "      <td>B</td>\n",
+       "      <td>7.76</td>\n",
+       "      <td>24.54</td>\n",
+       "      <td>47.92</td>\n",
+       "      <td>181.0</td>\n",
+       "      <td>0.05263</td>\n",
+       "      <td>0.04362</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>9.456</td>\n",
+       "      <td>30.37</td>\n",
+       "      <td>59.16</td>\n",
+       "      <td>268.6</td>\n",
+       "      <td>0.08996</td>\n",
+       "      <td>0.06444</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.2871</td>\n",
+       "      <td>0.07039</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>569 rows × 32 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
+       "0      842302         M        17.99         10.38          122.80     1001.0   \n",
+       "1      842517         M        20.57         17.77          132.90     1326.0   \n",
+       "2    84300903         M        19.69         21.25          130.00     1203.0   \n",
+       "3    84348301         M        11.42         20.38           77.58      386.1   \n",
+       "4    84358402         M        20.29         14.34          135.10     1297.0   \n",
+       "..        ...       ...          ...           ...             ...        ...   \n",
+       "564    926424         M        21.56         22.39          142.00     1479.0   \n",
+       "565    926682         M        20.13         28.25          131.20     1261.0   \n",
+       "566    926954         M        16.60         28.08          108.30      858.1   \n",
+       "567    927241         M        20.60         29.33          140.10     1265.0   \n",
+       "568     92751         B         7.76         24.54           47.92      181.0   \n",
+       "\n",
+       "     smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
+       "0            0.11840           0.27760         0.30010              0.14710   \n",
+       "1            0.08474           0.07864         0.08690              0.07017   \n",
+       "2            0.10960           0.15990         0.19740              0.12790   \n",
+       "3            0.14250           0.28390         0.24140              0.10520   \n",
+       "4            0.10030           0.13280         0.19800              0.10430   \n",
+       "..               ...               ...             ...                  ...   \n",
+       "564          0.11100           0.11590         0.24390              0.13890   \n",
+       "565          0.09780           0.10340         0.14400              0.09791   \n",
+       "566          0.08455           0.10230         0.09251              0.05302   \n",
+       "567          0.11780           0.27700         0.35140              0.15200   \n",
+       "568          0.05263           0.04362         0.00000              0.00000   \n",
+       "\n",
+       "     ...  radius_worst  texture_worst  perimeter_worst  area_worst  \\\n",
+       "0    ...        25.380          17.33           184.60      2019.0   \n",
+       "1    ...        24.990          23.41           158.80      1956.0   \n",
+       "2    ...        23.570          25.53           152.50      1709.0   \n",
+       "3    ...        14.910          26.50            98.87       567.7   \n",
+       "4    ...        22.540          16.67           152.20      1575.0   \n",
+       "..   ...           ...            ...              ...         ...   \n",
+       "564  ...        25.450          26.40           166.10      2027.0   \n",
+       "565  ...        23.690          38.25           155.00      1731.0   \n",
+       "566  ...        18.980          34.12           126.70      1124.0   \n",
+       "567  ...        25.740          39.42           184.60      1821.0   \n",
+       "568  ...         9.456          30.37            59.16       268.6   \n",
+       "\n",
+       "     smoothness_worst  compactness_worst  concavity_worst  \\\n",
+       "0             0.16220            0.66560           0.7119   \n",
+       "1             0.12380            0.18660           0.2416   \n",
+       "2             0.14440            0.42450           0.4504   \n",
+       "3             0.20980            0.86630           0.6869   \n",
+       "4             0.13740            0.20500           0.4000   \n",
+       "..                ...                ...              ...   \n",
+       "564           0.14100            0.21130           0.4107   \n",
+       "565           0.11660            0.19220           0.3215   \n",
+       "566           0.11390            0.30940           0.3403   \n",
+       "567           0.16500            0.86810           0.9387   \n",
+       "568           0.08996            0.06444           0.0000   \n",
+       "\n",
+       "     concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
+       "0                  0.2654          0.4601                  0.11890  \n",
+       "1                  0.1860          0.2750                  0.08902  \n",
+       "2                  0.2430          0.3613                  0.08758  \n",
+       "3                  0.2575          0.6638                  0.17300  \n",
+       "4                  0.1625          0.2364                  0.07678  \n",
+       "..                    ...             ...                      ...  \n",
+       "564                0.2216          0.2060                  0.07115  \n",
+       "565                0.1628          0.2572                  0.06637  \n",
+       "566                0.1418          0.2218                  0.07820  \n",
+       "567                0.2650          0.4087                  0.12400  \n",
+       "568                0.0000          0.2871                  0.07039  \n",
+       "\n",
+       "[569 rows x 32 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cancer = pd.read_csv('dataset/wdbc.csv')\n",
+    "cancer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "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": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cancer['diagnosis'] = cancer['diagnosis'].replace({\n",
+    "    \"M\" : \"Malignant\",\n",
+    "    \"B\": \"Benign\"\n",
+    "})\n",
+    "\n",
+    "cancer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "standardized_cancer = cancer.copy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "columns_to_exclude = ['id', 'diagnosis']\n",
+    "columns_to_scale = standardized_cancer.columns.difference(columns_to_exclude)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scaler = StandardScaler()\n",
+    "standardized_cancer[columns_to_scale] = scaler.fit_transform(standardized_cancer[columns_to_scale])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
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+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>diagnosis</th>\n",
+       "      <th>radius_mean</th>\n",
+       "      <th>texture_mean</th>\n",
+       "      <th>perimeter_mean</th>\n",
+       "      <th>area_mean</th>\n",
+       "      <th>smoothness_mean</th>\n",
+       "      <th>compactness_mean</th>\n",
+       "      <th>concavity_mean</th>\n",
+       "      <th>concave points_mean</th>\n",
+       "      <th>...</th>\n",
+       "      <th>radius_worst</th>\n",
+       "      <th>texture_worst</th>\n",
+       "      <th>perimeter_worst</th>\n",
+       "      <th>area_worst</th>\n",
+       "      <th>smoothness_worst</th>\n",
+       "      <th>compactness_worst</th>\n",
+       "      <th>concavity_worst</th>\n",
+       "      <th>concave points_worst</th>\n",
+       "      <th>symmetry_worst</th>\n",
+       "      <th>fractal_dimension_worst</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>842302</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>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": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "standardized_cancer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#set seed\n",
+    "np.random.seed(1)\n",
+    "\n",
+    "#split the data\n",
+    "\n",
+    "cancer_train, cancer_test = train_test_split(\n",
+    "    standardized_cancer, train_size = 0.75, shuffle = True, \n",
+    "    stratify = standardized_cancer['diagnosis']\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Int64Index: 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": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Int64Index: 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": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "KNeighborsClassifier()"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#k = 5\n",
+    "knn = KNeighborsClassifier(n_neighbors=5)\n",
+    "knn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#define our variables ; X and y\n",
+    "X = cancer_train[[\"perimeter_mean\",\"concavity_mean\"]]\n",
+    "y = cancer_train['diagnosis']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "KNeighborsClassifier()"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#fit our model\n",
+    "knn.fit(X,y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "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>predicted</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>357</th>\n",
+       "      <td>Benign</td>\n",
+       "      <td>Benign</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>361</th>\n",
+       "      <td>Benign</td>\n",
+       "      <td>Benign</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>212</th>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>Malignant</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>527</th>\n",
+       "      <td>Benign</td>\n",
+       "      <td>Benign</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>Benign</td>\n",
+       "      <td>Benign</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>364</th>\n",
+       "      <td>Benign</td>\n",
+       "      <td>Benign</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>434</th>\n",
+       "      <td>Benign</td>\n",
+       "      <td>Benign</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>299</th>\n",
+       "      <td>Benign</td>\n",
+       "      <td>Benign</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>488</th>\n",
+       "      <td>Benign</td>\n",
+       "      <td>Benign</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>332</th>\n",
+       "      <td>Benign</td>\n",
+       "      <td>Benign</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>143 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     diagnosis  predicted\n",
+       "357     Benign     Benign\n",
+       "361     Benign     Benign\n",
+       "212  Malignant  Malignant\n",
+       "527     Benign     Benign\n",
+       "21      Benign     Benign\n",
+       "..         ...        ...\n",
+       "364     Benign     Benign\n",
+       "434     Benign     Benign\n",
+       "299     Benign     Benign\n",
+       "488     Benign     Benign\n",
+       "332     Benign     Benign\n",
+       "\n",
+       "[143 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cancer_test['predicted'] = knn.predict(cancer_test[[\"perimeter_mean\",\"concavity_mean\"]])\n",
+    "\n",
+    "cancer_test[[\"diagnosis\",\"predicted\"]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9230769230769231"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#accuracy for test data\n",
+    "knn.score(cancer_test[[\"perimeter_mean\",\"concavity_mean\"]],cancer_test[\"diagnosis\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>Predicted</th>\n",
+       "      <th>Benign</th>\n",
+       "      <th>Malignant</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Actual</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Benign</th>\n",
+       "      <td>88</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Malignant</th>\n",
+       "      <td>9</td>\n",
+       "      <td>44</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Predicted  Benign  Malignant\n",
+       "Actual                      \n",
+       "Benign         88          2\n",
+       "Malignant       9         44"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#confusion matrix\n",
+    "\n",
+    "pd.crosstab(\n",
+    "    cancer_test[\"diagnosis\"],\n",
+    "    cancer_test[\"predicted\"],\n",
+    "    rownames = [\"Actual\"],\n",
+    "    colnames = [\"Predicted\"]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9565217391304348"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "precision_score(\n",
+    "    y_true = cancer_test[\"diagnosis\"],\n",
+    "    y_pred = cancer_test['predicted'],\n",
+    "    pos_label = \"Malignant\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.8301886792452831"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "recall_score(\n",
+    "   y_true = cancer_test['diagnosis'],\n",
+    "   y_pred = cancer_test['predicted'],\n",
+    "   pos_label = \"Malignant\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cancer_subtrain, cancer_validation = train_test_split(cancer_train, train_size= 0.75,\n",
+    "                                                     stratify = cancer_train[\"diagnosis\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" 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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=3)</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "KNeighborsClassifier(n_neighbors=3)"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "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": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.897196261682243"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#compute score on validation\n",
+    "acc = knn.score(\n",
+    "    cancer_validation[[\"perimeter_mean\",\"concavity_mean\"]],\n",
+    "    cancer_validation[\"diagnosis\"]\n",
+    ")\n",
+    "acc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>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>0.010476</td>\n",
+       "      <td>0.019072</td>\n",
+       "      <td>0.930233</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.003695</td>\n",
+       "      <td>0.004912</td>\n",
+       "      <td>0.894118</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.002568</td>\n",
+       "      <td>0.006499</td>\n",
+       "      <td>0.870588</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.014486</td>\n",
+       "      <td>0.028369</td>\n",
+       "      <td>0.952941</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.013098</td>\n",
+       "      <td>0.010131</td>\n",
+       "      <td>0.917647</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   fit_time  score_time  test_score\n",
+       "0  0.010476    0.019072    0.930233\n",
+       "1  0.003695    0.004912    0.894118\n",
+       "2  0.002568    0.006499    0.870588\n",
+       "3  0.014486    0.028369    0.952941\n",
+       "4  0.013098    0.010131    0.917647"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "knn = KNeighborsClassifier(n_neighbors=3)\n",
+    "X = cancer_train[[\"perimeter_mean\",\"concavity_mean\"]]\n",
+    "y = cancer_train['diagnosis']\n",
+    "\n",
+    "returned_dictionary = cross_validate(\n",
+    "    estimator = knn,\n",
+    "    cv = 5,\n",
+    "    X = X,\n",
+    "    y = y\n",
+    ")\n",
+    "\n",
+    "cv_5_df = pd.DataFrame(returned_dictionary)\n",
+    "cv_5_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "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>0.008865</td>\n",
+       "      <td>0.013797</td>\n",
+       "      <td>0.913105</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sem</th>\n",
+       "      <td>0.002434</td>\n",
+       "      <td>0.004392</td>\n",
+       "      <td>0.014264</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      fit_time  score_time  test_score\n",
+       "mean  0.008865    0.013797    0.913105\n",
+       "sem   0.002434    0.004392    0.014264"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cv_5_metrics = cv_5_df.agg([\"mean\",\"sem\"])\n",
+    "cv_5_metrics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "parameter_grid = {\n",
+    "    \"n_neighbors\" : range(1,100,5)\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cancer_tune_grid = GridSearchCV(\n",
+    "    estimator = knn,\n",
+    "    param_grid= parameter_grid,\n",
+    "    cv = 10\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-10 {color: black;background-color: white;}#sk-container-id-10 pre{padding: 0;}#sk-container-id-10 div.sk-toggleable {background-color: white;}#sk-container-id-10 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-10 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-10 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-10 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-10 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-10 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-10 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-10 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-10 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-10 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-10 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-10 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-10 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-10 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-10 div.sk-item {position: relative;z-index: 1;}#sk-container-id-10 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-10 div.sk-item::before, #sk-container-id-10 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-10 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-10 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-10 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-10 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-10 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-10 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-10 div.sk-label-container {text-align: center;}#sk-container-id-10 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-10 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-10\" 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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=10, estimator=KNeighborsClassifier(n_neighbors=3),\n",
+       "             param_grid={&#x27;n_neighbors&#x27;: range(1, 100, 5)})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=3)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=3)</pre></div></div></div></div></div></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "GridSearchCV(cv=10, estimator=KNeighborsClassifier(n_neighbors=3),\n",
+       "             param_grid={'n_neighbors': range(1, 100, 5)})"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cancer_tune_grid.fit(\n",
+    "    cancer_train[[\"perimeter_mean\", \"concavity_mean\"]],\n",
+    "    cancer_train['diagnosis']\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>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>0.004927</td>\n",
+       "      <td>0.002544</td>\n",
+       "      <td>0.007713</td>\n",
+       "      <td>0.002053</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>0.006205</td>\n",
+       "      <td>0.002044</td>\n",
+       "      <td>0.009256</td>\n",
+       "      <td>0.003887</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>0.003928</td>\n",
+       "      <td>0.000814</td>\n",
+       "      <td>0.006543</td>\n",
+       "      <td>0.001193</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>0.004453</td>\n",
+       "      <td>0.000908</td>\n",
+       "      <td>0.006600</td>\n",
+       "      <td>0.001631</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>0.005512</td>\n",
+       "      <td>0.002091</td>\n",
+       "      <td>0.006711</td>\n",
+       "      <td>0.000964</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>0.003768</td>\n",
+       "      <td>0.000858</td>\n",
+       "      <td>0.006578</td>\n",
+       "      <td>0.001180</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>0.004372</td>\n",
+       "      <td>0.001001</td>\n",
+       "      <td>0.006401</td>\n",
+       "      <td>0.001315</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>0.003675</td>\n",
+       "      <td>0.000891</td>\n",
+       "      <td>0.006684</td>\n",
+       "      <td>0.001395</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>0.004309</td>\n",
+       "      <td>0.001205</td>\n",
+       "      <td>0.005873</td>\n",
+       "      <td>0.001126</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>0.003698</td>\n",
+       "      <td>0.001003</td>\n",
+       "      <td>0.006654</td>\n",
+       "      <td>0.001187</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>0.004186</td>\n",
+       "      <td>0.000726</td>\n",
+       "      <td>0.006290</td>\n",
+       "      <td>0.001209</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>0.006861</td>\n",
+       "      <td>0.004795</td>\n",
+       "      <td>0.010006</td>\n",
+       "      <td>0.003187</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>0.005258</td>\n",
+       "      <td>0.002923</td>\n",
+       "      <td>0.009385</td>\n",
+       "      <td>0.003200</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>0.004179</td>\n",
+       "      <td>0.001172</td>\n",
+       "      <td>0.006498</td>\n",
+       "      <td>0.001036</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>0.003909</td>\n",
+       "      <td>0.000620</td>\n",
+       "      <td>0.006399</td>\n",
+       "      <td>0.001265</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>0.004091</td>\n",
+       "      <td>0.001142</td>\n",
+       "      <td>0.006418</td>\n",
+       "      <td>0.001327</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>0.004044</td>\n",
+       "      <td>0.000870</td>\n",
+       "      <td>0.007306</td>\n",
+       "      <td>0.001676</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>0.003659</td>\n",
+       "      <td>0.000462</td>\n",
+       "      <td>0.006809</td>\n",
+       "      <td>0.001188</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>0.004307</td>\n",
+       "      <td>0.001345</td>\n",
+       "      <td>0.007271</td>\n",
+       "      <td>0.002289</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>0.003831</td>\n",
+       "      <td>0.001237</td>\n",
+       "      <td>0.006685</td>\n",
+       "      <td>0.001340</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        0.004927      0.002544         0.007713        0.002053   \n",
+       "1        0.006205      0.002044         0.009256        0.003887   \n",
+       "2        0.003928      0.000814         0.006543        0.001193   \n",
+       "3        0.004453      0.000908         0.006600        0.001631   \n",
+       "4        0.005512      0.002091         0.006711        0.000964   \n",
+       "5        0.003768      0.000858         0.006578        0.001180   \n",
+       "6        0.004372      0.001001         0.006401        0.001315   \n",
+       "7        0.003675      0.000891         0.006684        0.001395   \n",
+       "8        0.004309      0.001205         0.005873        0.001126   \n",
+       "9        0.003698      0.001003         0.006654        0.001187   \n",
+       "10       0.004186      0.000726         0.006290        0.001209   \n",
+       "11       0.006861      0.004795         0.010006        0.003187   \n",
+       "12       0.005258      0.002923         0.009385        0.003200   \n",
+       "13       0.004179      0.001172         0.006498        0.001036   \n",
+       "14       0.003909      0.000620         0.006399        0.001265   \n",
+       "15       0.004091      0.001142         0.006418        0.001327   \n",
+       "16       0.004044      0.000870         0.007306        0.001676   \n",
+       "17       0.003659      0.000462         0.006809        0.001188   \n",
+       "18       0.004307      0.001345         0.007271        0.002289   \n",
+       "19       0.003831      0.001237         0.006685        0.001340   \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": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "accuracy_grid = pd.DataFrame(cancer_tune_grid.cv_results_)\n",
+    "accuracy_grid"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "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()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'n_neighbors': 16}"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cancer_tune_grid.best_params_"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

From 09fd0ca1c811296c936403711b379cd5a416669a Mon Sep 17 00:00:00 2001
From: Khoren <avetisyankhoren@yahoo.com>
Date: Sat, 17 May 2025 11:39:47 -0400
Subject: [PATCH 3/3] Complete assignment 1

---
 02_activities/assignments/assignment_1.ipynb | 479 +++++++++++++++++--
 1 file changed, 442 insertions(+), 37 deletions(-)

diff --git a/02_activities/assignments/assignment_1.ipynb b/02_activities/assignments/assignment_1.ipynb
index 828092657..7913b24f1 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": 3,
    "id": "4a3485d6-ba58-4660-a983-5680821c5719",
    "metadata": {},
    "outputs": [],
@@ -56,10 +56,288 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "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": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "from sklearn.datasets import load_wine\n",
     "\n",
@@ -91,12 +369,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "id": "56916892",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "178"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your answer here"
+    "# number of rows\n",
+    "wine_df.shape[0]"
    ]
   },
   {
@@ -109,12 +399,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "id": "df0ef103",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "14"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your answer here"
+    "# the number of variables\n",
+    "wine_df.shape[1]"
    ]
   },
   {
@@ -127,12 +429,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "id": "47989426",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(dtype('int32'), array([0, 1, 2]))"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your answer here"
+    "# used one-liner code\n",
+    "wine_df['class'].dtype, wine_df['class'].unique()"
    ]
   },
   {
@@ -146,12 +460,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "id": "bd7b0910",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "13"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your answer here"
+    "# In total 13 predictor variables\n",
+    "wine_df.shape[1] - 1"
    ]
   },
   {
@@ -175,10 +501,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "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 +557,8 @@
    "id": "403ef0bb",
    "metadata": {},
    "source": [
-    "> Your answer here..."
+    " # Standardizing the predictors is important because the algorithm relies on distance calculations. If the variables have different scales, those with larger values could outweigh the smaller ones and affect the results. By scaling all predictors to the same range, we make sure that each feature contributes equally to the distance measurement.\n",
+    " "
    ]
   },
   {
@@ -220,7 +574,7 @@
    "id": "fdee5a15",
    "metadata": {},
    "source": [
-    "> Your answer here..."
+    "# We didn’t standardize the response variable Class, because it’s a categorical label, not a continuous value. Standardizing it would change its meaning and make it unusable for classification. \n"
    ]
   },
   {
@@ -232,11 +586,15 @@
    ]
   },
   {
-   "cell_type": "markdown",
-   "id": "f0676c21",
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "7f660856",
    "metadata": {},
+   "outputs": [],
    "source": [
-    "> Your answer here..."
+    "# Setting a random seed is important to make sure the results are reproducible. The actual number (111) isn’t important — it just needs to be fixed and used consistently.\n",
+    "random.seed(111)\n",
+    "np.random.seed(111)\n"
    ]
   },
   {
@@ -251,7 +609,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "id": "72c101f2",
    "metadata": {},
    "outputs": [],
@@ -261,7 +619,10 @@
     "\n",
     "# split the data into a training and testing set. hint: use train_test_split !\n",
     "\n",
-    "# Your code here ..."
+    "# Your code here ...\n",
+    "X_train, X_test, y_train, y_test = train_test_split(\n",
+    "    predictors_standardized, wine_df['class'], test_size=0.25, random_state=111\n",
+    ")\n"
    ]
   },
   {
@@ -284,12 +645,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "id": "08818c64",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "28"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code here..."
+    "# base KNN model\n",
+    "knn = KNeighborsClassifier()\n",
+    "\n",
+    "# test different values, define the grid\n",
+    "param_grid = {'n_neighbors': np.arange(1, 51)}\n",
+    "\n",
+    "# evaluate using 10-fold CV\n",
+    "grid = GridSearchCV(knn, param_grid, cv=10)\n",
+    "\n",
+    "# fit the model in training data\n",
+    "grid.fit(X_train, y_train)\n",
+    "\n",
+    "# return the best number of neighbors\n",
+    "grid.best_params_['n_neighbors']\n",
+    "\n"
    ]
   },
   {
@@ -305,12 +691,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "id": "ffefa9f2",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9555555555555556"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code here..."
+    "from sklearn.metrics import accuracy_score\n",
+    "\n",
+    "# Create final model with best n_neighbors\n",
+    "final_knn = KNeighborsClassifier(n_neighbors=28)\n",
+    "\n",
+    "# Fit the model\n",
+    "final_knn.fit(X_train, y_train)\n",
+    "\n",
+    "# Predict on the test set\n",
+    "y_pred = final_knn.predict(X_test)\n",
+    "\n",
+    "# Check accuracy\n",
+    "accuracy_score(y_test, y_pred)\n",
+    "\n"
    ]
   },
   {
@@ -365,7 +775,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.10.4",
+   "display_name": "dsi_participant",
    "language": "python",
    "name": "python3"
   },
@@ -379,12 +789,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.19"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "497a84dc8fec8cf8d24e7e87b6d954c9a18a327edc66feb9b9ea7e9e72cc5c7e"
-   }
+   "version": "3.9.15"
   }
  },
  "nbformat": 4,