Breast Cancer Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0f01bd78",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading Libraries\n",
    "import pandas as pd #Data Manipulation\n",
    "\n",
    "import matplotlib.pyplot as plt #Plotting\n",
    "import seaborn as sns #Plotting\n",
    "\n",
    "from sklearn.model_selection import train_test_split #Train/Test Split\n",
    "from sklearn.linear_model import LogisticRegression #Model\n",
    "\n",
    "from sklearn.metrics import classification_report #Metrics\n",
    "from sklearn.metrics import accuracy_score #Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6d13f970",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reading Dataset from repository\n",
    "data = pd.read_csv('data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "951df4cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA UNDERSTANDING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "869f1093",
   "metadata": {},
   "outputs": [
    {
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       "  <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>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>...</td>\n",
       "      <td>25.38</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
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       "    <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>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>...</td>\n",
       "      <td>24.99</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
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       "    <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>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>...</td>\n",
       "      <td>23.57</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</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>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>...</td>\n",
       "      <td>14.91</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.8663</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>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>...</td>\n",
       "      <td>22.54</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
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       "<p>5 rows × 32 columns</p>\n",
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      "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",
       "   smoothness_mean  compactness_mean  concavity_mean  concave_points_mean  \\\n",
       "0          0.11840           0.27760          0.3001              0.14710   \n",
       "1          0.08474           0.07864          0.0869              0.07017   \n",
       "2          0.10960           0.15990          0.1974              0.12790   \n",
       "3          0.14250           0.28390          0.2414              0.10520   \n",
       "4          0.10030           0.13280          0.1980              0.10430   \n",
       "\n",
       "   ...  radius_worst  texture_worst  perimeter_worst  area_worst  \\\n",
       "0  ...         25.38          17.33           184.60      2019.0   \n",
       "1  ...         24.99          23.41           158.80      1956.0   \n",
       "2  ...         23.57          25.53           152.50      1709.0   \n",
       "3  ...         14.91          26.50            98.87       567.7   \n",
       "4  ...         22.54          16.67           152.20      1575.0   \n",
       "\n",
       "   smoothness_worst  compactness_worst  concavity_worst  concave_points_worst  \\\n",
       "0            0.1622             0.6656           0.7119                0.2654   \n",
       "1            0.1238             0.1866           0.2416                0.1860   \n",
       "2            0.1444             0.4245           0.4504                0.2430   \n",
       "3            0.2098             0.8663           0.6869                0.2575   \n",
       "4            0.1374             0.2050           0.4000                0.1625   \n",
       "\n",
       "   symmetry_worst  fractal_dimension_worst  \n",
       "0          0.4601                  0.11890  \n",
       "1          0.2750                  0.08902  \n",
       "2          0.3613                  0.08758  \n",
       "3          0.6638                  0.17300  \n",
       "4          0.2364                  0.07678  \n",
       "\n",
       "[5 rows x 32 columns]"
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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    "data.head()"
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  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "02134b39",
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>diagnosis</th>\n",
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       "      <th>smoothness_mean</th>\n",
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       "      <th>concavity_mean</th>\n",
       "      <th>concave_points_mean</th>\n",
       "      <th>...</th>\n",
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       "      <th>perimeter_worst</th>\n",
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       "      <th>smoothness_worst</th>\n",
       "      <th>compactness_worst</th>\n",
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       "      <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",
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       "      <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",
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       "      <td>0.2060</td>\n",
       "      <td>0.07115</td>\n",
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       "    <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",
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       "    <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",
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       "    <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",
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       "      <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",
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       "      <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",
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      "text/plain": [
       "         id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\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",
       "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",
       "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",
       "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",
       "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",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()"
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  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e4ffda2f",
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   "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": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c839a94f",
   "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>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>symmetry_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>count</th>\n",
       "      <td>5.690000e+02</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.037183e+07</td>\n",
       "      <td>14.127292</td>\n",
       "      <td>19.289649</td>\n",
       "      <td>91.969033</td>\n",
       "      <td>654.889104</td>\n",
       "      <td>0.096360</td>\n",
       "      <td>0.104341</td>\n",
       "      <td>0.088799</td>\n",
       "      <td>0.048919</td>\n",
       "      <td>0.181162</td>\n",
       "      <td>...</td>\n",
       "      <td>16.269190</td>\n",
       "      <td>25.677223</td>\n",
       "      <td>107.261213</td>\n",
       "      <td>880.583128</td>\n",
       "      <td>0.132369</td>\n",
       "      <td>0.254265</td>\n",
       "      <td>0.272188</td>\n",
       "      <td>0.114606</td>\n",
       "      <td>0.290076</td>\n",
       "      <td>0.083946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.250206e+08</td>\n",
       "      <td>3.524049</td>\n",
       "      <td>4.301036</td>\n",
       "      <td>24.298981</td>\n",
       "      <td>351.914129</td>\n",
       "      <td>0.014064</td>\n",
       "      <td>0.052813</td>\n",
       "      <td>0.079720</td>\n",
       "      <td>0.038803</td>\n",
       "      <td>0.027414</td>\n",
       "      <td>...</td>\n",
       "      <td>4.833242</td>\n",
       "      <td>6.146258</td>\n",
       "      <td>33.602542</td>\n",
       "      <td>569.356993</td>\n",
       "      <td>0.022832</td>\n",
       "      <td>0.157336</td>\n",
       "      <td>0.208624</td>\n",
       "      <td>0.065732</td>\n",
       "      <td>0.061867</td>\n",
       "      <td>0.018061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>8.670000e+03</td>\n",
       "      <td>6.981000</td>\n",
       "      <td>9.710000</td>\n",
       "      <td>43.790000</td>\n",
       "      <td>143.500000</td>\n",
       "      <td>0.052630</td>\n",
       "      <td>0.019380</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.106000</td>\n",
       "      <td>...</td>\n",
       "      <td>7.930000</td>\n",
       "      <td>12.020000</td>\n",
       "      <td>50.410000</td>\n",
       "      <td>185.200000</td>\n",
       "      <td>0.071170</td>\n",
       "      <td>0.027290</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.156500</td>\n",
       "      <td>0.055040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8.692180e+05</td>\n",
       "      <td>11.700000</td>\n",
       "      <td>16.170000</td>\n",
       "      <td>75.170000</td>\n",
       "      <td>420.300000</td>\n",
       "      <td>0.086370</td>\n",
       "      <td>0.064920</td>\n",
       "      <td>0.029560</td>\n",
       "      <td>0.020310</td>\n",
       "      <td>0.161900</td>\n",
       "      <td>...</td>\n",
       "      <td>13.010000</td>\n",
       "      <td>21.080000</td>\n",
       "      <td>84.110000</td>\n",
       "      <td>515.300000</td>\n",
       "      <td>0.116600</td>\n",
       "      <td>0.147200</td>\n",
       "      <td>0.114500</td>\n",
       "      <td>0.064930</td>\n",
       "      <td>0.250400</td>\n",
       "      <td>0.071460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>9.060240e+05</td>\n",
       "      <td>13.370000</td>\n",
       "      <td>18.840000</td>\n",
       "      <td>86.240000</td>\n",
       "      <td>551.100000</td>\n",
       "      <td>0.095870</td>\n",
       "      <td>0.092630</td>\n",
       "      <td>0.061540</td>\n",
       "      <td>0.033500</td>\n",
       "      <td>0.179200</td>\n",
       "      <td>...</td>\n",
       "      <td>14.970000</td>\n",
       "      <td>25.410000</td>\n",
       "      <td>97.660000</td>\n",
       "      <td>686.500000</td>\n",
       "      <td>0.131300</td>\n",
       "      <td>0.211900</td>\n",
       "      <td>0.226700</td>\n",
       "      <td>0.099930</td>\n",
       "      <td>0.282200</td>\n",
       "      <td>0.080040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.813129e+06</td>\n",
       "      <td>15.780000</td>\n",
       "      <td>21.800000</td>\n",
       "      <td>104.100000</td>\n",
       "      <td>782.700000</td>\n",
       "      <td>0.105300</td>\n",
       "      <td>0.130400</td>\n",
       "      <td>0.130700</td>\n",
       "      <td>0.074000</td>\n",
       "      <td>0.195700</td>\n",
       "      <td>...</td>\n",
       "      <td>18.790000</td>\n",
       "      <td>29.720000</td>\n",
       "      <td>125.400000</td>\n",
       "      <td>1084.000000</td>\n",
       "      <td>0.146000</td>\n",
       "      <td>0.339100</td>\n",
       "      <td>0.382900</td>\n",
       "      <td>0.161400</td>\n",
       "      <td>0.317900</td>\n",
       "      <td>0.092080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.113205e+08</td>\n",
       "      <td>28.110000</td>\n",
       "      <td>39.280000</td>\n",
       "      <td>188.500000</td>\n",
       "      <td>2501.000000</td>\n",
       "      <td>0.163400</td>\n",
       "      <td>0.345400</td>\n",
       "      <td>0.426800</td>\n",
       "      <td>0.201200</td>\n",
       "      <td>0.304000</td>\n",
       "      <td>...</td>\n",
       "      <td>36.040000</td>\n",
       "      <td>49.540000</td>\n",
       "      <td>251.200000</td>\n",
       "      <td>4254.000000</td>\n",
       "      <td>0.222600</td>\n",
       "      <td>1.058000</td>\n",
       "      <td>1.252000</td>\n",
       "      <td>0.291000</td>\n",
       "      <td>0.663800</td>\n",
       "      <td>0.207500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id  radius_mean  texture_mean  perimeter_mean    area_mean  \\\n",
       "count  5.690000e+02   569.000000    569.000000      569.000000   569.000000   \n",
       "mean   3.037183e+07    14.127292     19.289649       91.969033   654.889104   \n",
       "std    1.250206e+08     3.524049      4.301036       24.298981   351.914129   \n",
       "min    8.670000e+03     6.981000      9.710000       43.790000   143.500000   \n",
       "25%    8.692180e+05    11.700000     16.170000       75.170000   420.300000   \n",
       "50%    9.060240e+05    13.370000     18.840000       86.240000   551.100000   \n",
       "75%    8.813129e+06    15.780000     21.800000      104.100000   782.700000   \n",
       "max    9.113205e+08    28.110000     39.280000      188.500000  2501.000000   \n",
       "\n",
       "       smoothness_mean  compactness_mean  concavity_mean  concave_points_mean  \\\n",
       "count       569.000000        569.000000      569.000000           569.000000   \n",
       "mean          0.096360          0.104341        0.088799             0.048919   \n",
       "std           0.014064          0.052813        0.079720             0.038803   \n",
       "min           0.052630          0.019380        0.000000             0.000000   \n",
       "25%           0.086370          0.064920        0.029560             0.020310   \n",
       "50%           0.095870          0.092630        0.061540             0.033500   \n",
       "75%           0.105300          0.130400        0.130700             0.074000   \n",
       "max           0.163400          0.345400        0.426800             0.201200   \n",
       "\n",
       "       symmetry_mean  ...  radius_worst  texture_worst  perimeter_worst  \\\n",
       "count     569.000000  ...    569.000000     569.000000       569.000000   \n",
       "mean        0.181162  ...     16.269190      25.677223       107.261213   \n",
       "std         0.027414  ...      4.833242       6.146258        33.602542   \n",
       "min         0.106000  ...      7.930000      12.020000        50.410000   \n",
       "25%         0.161900  ...     13.010000      21.080000        84.110000   \n",
       "50%         0.179200  ...     14.970000      25.410000        97.660000   \n",
       "75%         0.195700  ...     18.790000      29.720000       125.400000   \n",
       "max         0.304000  ...     36.040000      49.540000       251.200000   \n",
       "\n",
       "        area_worst  smoothness_worst  compactness_worst  concavity_worst  \\\n",
       "count   569.000000        569.000000         569.000000       569.000000   \n",
       "mean    880.583128          0.132369           0.254265         0.272188   \n",
       "std     569.356993          0.022832           0.157336         0.208624   \n",
       "min     185.200000          0.071170           0.027290         0.000000   \n",
       "25%     515.300000          0.116600           0.147200         0.114500   \n",
       "50%     686.500000          0.131300           0.211900         0.226700   \n",
       "75%    1084.000000          0.146000           0.339100         0.382900   \n",
       "max    4254.000000          0.222600           1.058000         1.252000   \n",
       "\n",
       "       concave_points_worst  symmetry_worst  fractal_dimension_worst  \n",
       "count            569.000000      569.000000               569.000000  \n",
       "mean               0.114606        0.290076                 0.083946  \n",
       "std                0.065732        0.061867                 0.018061  \n",
       "min                0.000000        0.156500                 0.055040  \n",
       "25%                0.064930        0.250400                 0.071460  \n",
       "50%                0.099930        0.282200                 0.080040  \n",
       "75%                0.161400        0.317900                 0.092080  \n",
       "max                0.291000        0.663800                 0.207500  \n",
       "\n",
       "[8 rows x 31 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "39d5518d",
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA CLEANING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f6650f3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                         0\n",
       "diagnosis                  0\n",
       "radius_mean                0\n",
       "texture_mean               0\n",
       "perimeter_mean             0\n",
       "area_mean                  0\n",
       "smoothness_mean            0\n",
       "compactness_mean           0\n",
       "concavity_mean             0\n",
       "concave_points_mean        0\n",
       "symmetry_mean              0\n",
       "fractal_dimension_mean     0\n",
       "radius_se                  0\n",
       "texture_se                 0\n",
       "perimeter_se               0\n",
       "area_se                    0\n",
       "smoothness_se              0\n",
       "compactness_se             0\n",
       "concavity_se               0\n",
       "concave_points_se          0\n",
       "symmetry_se                0\n",
       "fractal_dimension_se       0\n",
       "radius_worst               0\n",
       "texture_worst              0\n",
       "perimeter_worst            0\n",
       "area_worst                 0\n",
       "smoothness_worst           0\n",
       "compactness_worst          0\n",
       "concavity_worst            0\n",
       "concave_points_worst       0\n",
       "symmetry_worst             0\n",
       "fractal_dimension_worst    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for null values\n",
    "\n",
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d0c6a01c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a1e3901f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Selecting relevant columns\n",
    "breast_data = data.iloc[:,1:12]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "75823afb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removal of Duplicate Rows\n",
    "breast_data.drop_duplicates(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ff76d818",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Checking outliers using Boxplot\n",
    "\n",
    "# Define the selected features again\n",
    "selected_features = ['radius_mean', 'texture_mean', 'smoothness_mean', 'compactness_mean', 'area_mean', 'perimeter_mean']\n",
    "\n",
    "# Plotting boxplots for the selected features\n",
    "plt.figure(figsize=(15, 10))\n",
    "sns.boxplot(data=breast_data[selected_features], palette=\"pastel\", orient=\"h\")\n",
    "plt.title('Boxplots for Selected Features')\n",
    "plt.xlabel('Value')\n",
    "plt.ylabel('Feature')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3d3ecf18",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = breast_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "42968b11",
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA VISUALISATION "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0915fbd7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1. Distribution of the diagnosis target variable\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.countplot(x='diagnosis', data=df)\n",
    "plt.title('Distribution of Diagnosis (M = Malignant, B = Benign)')\n",
    "plt.xlabel('Diagnosis')\n",
    "plt.ylabel('Count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9ea120ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2. Histograms for a selection of features\n",
    "features = ['radius_mean', 'texture_mean', 'smoothness_mean', 'compactness_mean']\n",
    "plt.figure(figsize=(20, 15))\n",
    "df[features].hist(ax=plt.gca(), color='lightblue', edgecolor='black')\n",
    "plt.suptitle('Histograms for Selected Features')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# 3. Correlation heatmap to understand relationships between features\n",
    "correlation_matrix = df.corr()  \n",
    "plt.figure(figsize=(20, 15))\n",
    "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', linewidths=0.5)\n",
    "plt.title('Correlation Heatmap for Selected Features')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4853ffe7",
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA PREPROCESSING FOR MACHINE LEARNING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "79c4151d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((426, 10), (143, 10), (426,), (143,))"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
    "\n",
    "# 1. Convert the 'diagnosis' column to binary labels\n",
    "le = LabelEncoder()\n",
    "breast_data['diagnosis'] = le.fit_transform(breast_data['diagnosis'])\n",
    "\n",
    "# Splitting the data into features and target variable\n",
    "X = breast_data.drop(columns=['diagnosis'])\n",
    "y = breast_data['diagnosis']\n",
    "\n",
    "# 2. Scale the features\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)\n",
    "\n",
    "# 3. Split the data into training and testing sets\n",
    "X_scaled_train, X_scaled_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.25, random_state=42)\n",
    "\n",
    "X_scaled_train.shape, X_scaled_test.shape, y_train.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1cea826e",
   "metadata": {},
   "outputs": [],
   "source": [
    "MODEL BUILDING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "166c209e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3c861d31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Support Vector Machine', 0.972027972027972),\n",
       " ('Logistic Regression', 0.9440559440559441)]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Defining the models\n",
    "models = {\n",
    "    \"Logistic Regression\": LogisticRegression(max_iter=10000, random_state=42),\n",
    "    \"Support Vector Machine\": SVC(random_state=42),\n",
    "}\n",
    "\n",
    "# Training the models and evaluating their performance\n",
    "model_accuracies = {}\n",
    "\n",
    "for name, model in models.items():\n",
    "    # Train the model\n",
    "    model.fit(X_scaled_train, y_train)\n",
    "    \n",
    "    # Predict on the test set\n",
    "    y_pred = model.predict(X_scaled_test)\n",
    "    \n",
    "    # Calculate the accuracy\n",
    "    accuracy = accuracy_score(y_test, y_pred)\n",
    "    model_accuracies[name] = accuracy\n",
    "\n",
    "# Sorting the models based on accuracy\n",
    "sorted_models = sorted(model_accuracies.items(), key=lambda x: x[1], reverse=True)\n",
    "\n",
    "sorted_models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0ce25708",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.97      0.98        91\n",
      "           1       0.94      0.98      0.96        52\n",
      "\n",
      "    accuracy                           0.97       143\n",
      "   macro avg       0.97      0.97      0.97       143\n",
      "weighted avg       0.97      0.97      0.97       143\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_pred, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "506e095b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating a bar plot of the model accuracy scores\n",
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "sns.barplot(x=list(model_accuracies.keys()), y=list(model_accuracies.values()), palette=\"viridis\")\n",
    "plt.ylim(0.9, 1.0)\n",
    "plt.title('Model Accuracy Score Comparisons')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('Machine Learning Models')\n",
    "plt.xticks(rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "db10df4a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}