heart disease.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "79da695b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing libraries \n",
    "import pandas as pd \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "f4c06aa3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(r\"D:\\Data\\Heart_Disease_Prediction.csv\" , encoding = 'utf-8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "5a3127fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of framework : (270, 14)\n"
     ]
    },
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Chest pain type</th>\n",
       "      <th>BP</th>\n",
       "      <th>Cholesterol</th>\n",
       "      <th>FBS over 120</th>\n",
       "      <th>EKG results</th>\n",
       "      <th>Max HR</th>\n",
       "      <th>Exercise angina</th>\n",
       "      <th>ST depression</th>\n",
       "      <th>Slope of ST</th>\n",
       "      <th>Number of vessels fluro</th>\n",
       "      <th>Thallium</th>\n",
       "      <th>Heart Disease</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>130</td>\n",
       "      <td>322</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>109</td>\n",
       "      <td>0</td>\n",
       "      <td>2.4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Presence</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>67</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>115</td>\n",
       "      <td>564</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>160</td>\n",
       "      <td>0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>Absence</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>124</td>\n",
       "      <td>261</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>Presence</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>64</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>128</td>\n",
       "      <td>263</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>105</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>Absence</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>120</td>\n",
       "      <td>269</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>121</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Absence</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>120</td>\n",
       "      <td>177</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>Absence</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>130</td>\n",
       "      <td>256</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>142</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>Presence</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>59</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>110</td>\n",
       "      <td>239</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>142</td>\n",
       "      <td>1</td>\n",
       "      <td>1.2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>Presence</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>140</td>\n",
       "      <td>293</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>170</td>\n",
       "      <td>0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>Presence</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>150</td>\n",
       "      <td>407</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>154</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>Presence</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Sex  Chest pain type   BP  Cholesterol  FBS over 120  EKG results  \\\n",
       "0   70    1                4  130          322             0            2   \n",
       "1   67    0                3  115          564             0            2   \n",
       "2   57    1                2  124          261             0            0   \n",
       "3   64    1                4  128          263             0            0   \n",
       "4   74    0                2  120          269             0            2   \n",
       "5   65    1                4  120          177             0            0   \n",
       "6   56    1                3  130          256             1            2   \n",
       "7   59    1                4  110          239             0            2   \n",
       "8   60    1                4  140          293             0            2   \n",
       "9   63    0                4  150          407             0            2   \n",
       "\n",
       "   Max HR  Exercise angina  ST depression  Slope of ST  \\\n",
       "0     109                0            2.4            2   \n",
       "1     160                0            1.6            2   \n",
       "2     141                0            0.3            1   \n",
       "3     105                1            0.2            2   \n",
       "4     121                1            0.2            1   \n",
       "5     140                0            0.4            1   \n",
       "6     142                1            0.6            2   \n",
       "7     142                1            1.2            2   \n",
       "8     170                0            1.2            2   \n",
       "9     154                0            4.0            2   \n",
       "\n",
       "   Number of vessels fluro  Thallium Heart Disease  \n",
       "0                        3         3      Presence  \n",
       "1                        0         7       Absence  \n",
       "2                        0         7      Presence  \n",
       "3                        1         7       Absence  \n",
       "4                        1         3       Absence  \n",
       "5                        0         7       Absence  \n",
       "6                        1         6      Presence  \n",
       "7                        1         7      Presence  \n",
       "8                        2         7      Presence  \n",
       "9                        3         7      Presence  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('shape of framework : {}' .format(df.shape))\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "358605c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 270 entries, 0 to 269\n",
      "Data columns (total 14 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   Age                      270 non-null    int64  \n",
      " 1   Sex                      270 non-null    int64  \n",
      " 2   Chest pain type          270 non-null    int64  \n",
      " 3   BP                       270 non-null    int64  \n",
      " 4   Cholesterol              270 non-null    int64  \n",
      " 5   FBS over 120             270 non-null    int64  \n",
      " 6   EKG results              270 non-null    int64  \n",
      " 7   Max HR                   270 non-null    int64  \n",
      " 8   Exercise angina          270 non-null    int64  \n",
      " 9   ST depression            270 non-null    float64\n",
      " 10  Slope of ST              270 non-null    int64  \n",
      " 11  Number of vessels fluro  270 non-null    int64  \n",
      " 12  Thallium                 270 non-null    int64  \n",
      " 13  Heart Disease            270 non-null    object \n",
      "dtypes: float64(1), int64(12), object(1)\n",
      "memory usage: 29.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5dd3e6db",
   "metadata": {},
   "source": [
    "## there are 0 missing values in this data and heart disease column is the only object column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "cb6fefe1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Chest pain type</th>\n",
       "      <th>BP</th>\n",
       "      <th>Cholesterol</th>\n",
       "      <th>FBS over 120</th>\n",
       "      <th>EKG results</th>\n",
       "      <th>Max HR</th>\n",
       "      <th>Exercise angina</th>\n",
       "      <th>ST depression</th>\n",
       "      <th>Slope of ST</th>\n",
       "      <th>Number of vessels fluro</th>\n",
       "      <th>Thallium</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>270.000000</td>\n",
       "      <td>270.000000</td>\n",
       "      <td>270.000000</td>\n",
       "      <td>270.000000</td>\n",
       "      <td>270.000000</td>\n",
       "      <td>270.000000</td>\n",
       "      <td>270.000000</td>\n",
       "      <td>270.000000</td>\n",
       "      <td>270.000000</td>\n",
       "      <td>270.00000</td>\n",
       "      <td>270.000000</td>\n",
       "      <td>270.000000</td>\n",
       "      <td>270.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>54.433333</td>\n",
       "      <td>0.677778</td>\n",
       "      <td>3.174074</td>\n",
       "      <td>131.344444</td>\n",
       "      <td>249.659259</td>\n",
       "      <td>0.148148</td>\n",
       "      <td>1.022222</td>\n",
       "      <td>149.677778</td>\n",
       "      <td>0.329630</td>\n",
       "      <td>1.05000</td>\n",
       "      <td>1.585185</td>\n",
       "      <td>0.670370</td>\n",
       "      <td>4.696296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.109067</td>\n",
       "      <td>0.468195</td>\n",
       "      <td>0.950090</td>\n",
       "      <td>17.861608</td>\n",
       "      <td>51.686237</td>\n",
       "      <td>0.355906</td>\n",
       "      <td>0.997891</td>\n",
       "      <td>23.165717</td>\n",
       "      <td>0.470952</td>\n",
       "      <td>1.14521</td>\n",
       "      <td>0.614390</td>\n",
       "      <td>0.943896</td>\n",
       "      <td>1.940659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>48.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>213.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>133.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>55.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>245.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>153.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.80000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>61.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>280.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.60000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>77.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>564.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.20000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age         Sex  Chest pain type          BP  Cholesterol  \\\n",
       "count  270.000000  270.000000       270.000000  270.000000   270.000000   \n",
       "mean    54.433333    0.677778         3.174074  131.344444   249.659259   \n",
       "std      9.109067    0.468195         0.950090   17.861608    51.686237   \n",
       "min     29.000000    0.000000         1.000000   94.000000   126.000000   \n",
       "25%     48.000000    0.000000         3.000000  120.000000   213.000000   \n",
       "50%     55.000000    1.000000         3.000000  130.000000   245.000000   \n",
       "75%     61.000000    1.000000         4.000000  140.000000   280.000000   \n",
       "max     77.000000    1.000000         4.000000  200.000000   564.000000   \n",
       "\n",
       "       FBS over 120  EKG results      Max HR  Exercise angina  ST depression  \\\n",
       "count    270.000000   270.000000  270.000000       270.000000      270.00000   \n",
       "mean       0.148148     1.022222  149.677778         0.329630        1.05000   \n",
       "std        0.355906     0.997891   23.165717         0.470952        1.14521   \n",
       "min        0.000000     0.000000   71.000000         0.000000        0.00000   \n",
       "25%        0.000000     0.000000  133.000000         0.000000        0.00000   \n",
       "50%        0.000000     2.000000  153.500000         0.000000        0.80000   \n",
       "75%        0.000000     2.000000  166.000000         1.000000        1.60000   \n",
       "max        1.000000     2.000000  202.000000         1.000000        6.20000   \n",
       "\n",
       "       Slope of ST  Number of vessels fluro    Thallium  \n",
       "count   270.000000               270.000000  270.000000  \n",
       "mean      1.585185                 0.670370    4.696296  \n",
       "std       0.614390                 0.943896    1.940659  \n",
       "min       1.000000                 0.000000    3.000000  \n",
       "25%       1.000000                 0.000000    3.000000  \n",
       "50%       2.000000                 0.000000    3.000000  \n",
       "75%       2.000000                 1.000000    7.000000  \n",
       "max       3.000000                 3.000000    7.000000  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "10b4c1f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4    129\n",
       "3     79\n",
       "2     42\n",
       "1     20\n",
       "Name: Chest pain type, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Chest pain type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "ef6d3b62",
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert heart disease to numeric\n",
    "df[\"target\"] = df[\"Heart Disease\"].apply(lambda x: 1 if x == 'Presence' else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "aa60f7a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\AppData\\Local\\Temp\\ipykernel_13020\\4134588451.py:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  corr= df.corr()\n"
     ]
    },
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       "      <td id=\"T_cb5ed_row0_col8\" class=\"data row0 col8\" >0.098297</td>\n",
       "      <td id=\"T_cb5ed_row0_col9\" class=\"data row0 col9\" >0.194234</td>\n",
       "      <td id=\"T_cb5ed_row0_col10\" class=\"data row0 col10\" >0.159774</td>\n",
       "      <td id=\"T_cb5ed_row0_col11\" class=\"data row0 col11\" >0.356081</td>\n",
       "      <td id=\"T_cb5ed_row0_col12\" class=\"data row0 col12\" >0.106100</td>\n",
       "      <td id=\"T_cb5ed_row0_col13\" class=\"data row0 col13\" >0.212322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cb5ed_level0_row1\" class=\"row_heading level0 row1\" >Sex</th>\n",
       "      <td id=\"T_cb5ed_row1_col0\" class=\"data row1 col0\" >-0.094401</td>\n",
       "      <td id=\"T_cb5ed_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
       "      <td id=\"T_cb5ed_row1_col2\" class=\"data row1 col2\" >0.034636</td>\n",
       "      <td id=\"T_cb5ed_row1_col3\" class=\"data row1 col3\" >-0.062693</td>\n",
       "      <td id=\"T_cb5ed_row1_col4\" class=\"data row1 col4\" >-0.201647</td>\n",
       "      <td id=\"T_cb5ed_row1_col5\" class=\"data row1 col5\" >0.042140</td>\n",
       "      <td id=\"T_cb5ed_row1_col6\" class=\"data row1 col6\" >0.039253</td>\n",
       "      <td id=\"T_cb5ed_row1_col7\" class=\"data row1 col7\" >-0.076101</td>\n",
       "      <td id=\"T_cb5ed_row1_col8\" class=\"data row1 col8\" >0.180022</td>\n",
       "      <td id=\"T_cb5ed_row1_col9\" class=\"data row1 col9\" >0.097412</td>\n",
       "      <td id=\"T_cb5ed_row1_col10\" class=\"data row1 col10\" >0.050545</td>\n",
       "      <td id=\"T_cb5ed_row1_col11\" class=\"data row1 col11\" >0.086830</td>\n",
       "      <td id=\"T_cb5ed_row1_col12\" class=\"data row1 col12\" >0.391046</td>\n",
       "      <td id=\"T_cb5ed_row1_col13\" class=\"data row1 col13\" >0.297721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cb5ed_level0_row2\" class=\"row_heading level0 row2\" >Chest pain type</th>\n",
       "      <td id=\"T_cb5ed_row2_col0\" class=\"data row2 col0\" >0.096920</td>\n",
       "      <td id=\"T_cb5ed_row2_col1\" class=\"data row2 col1\" >0.034636</td>\n",
       "      <td id=\"T_cb5ed_row2_col2\" class=\"data row2 col2\" >1.000000</td>\n",
       "      <td id=\"T_cb5ed_row2_col3\" class=\"data row2 col3\" >-0.043196</td>\n",
       "      <td id=\"T_cb5ed_row2_col4\" class=\"data row2 col4\" >0.090465</td>\n",
       "      <td id=\"T_cb5ed_row2_col5\" class=\"data row2 col5\" >-0.098537</td>\n",
       "      <td id=\"T_cb5ed_row2_col6\" class=\"data row2 col6\" >0.074325</td>\n",
       "      <td id=\"T_cb5ed_row2_col7\" class=\"data row2 col7\" >-0.317682</td>\n",
       "      <td id=\"T_cb5ed_row2_col8\" class=\"data row2 col8\" >0.353160</td>\n",
       "      <td id=\"T_cb5ed_row2_col9\" class=\"data row2 col9\" >0.167244</td>\n",
       "      <td id=\"T_cb5ed_row2_col10\" class=\"data row2 col10\" >0.136900</td>\n",
       "      <td id=\"T_cb5ed_row2_col11\" class=\"data row2 col11\" >0.225890</td>\n",
       "      <td id=\"T_cb5ed_row2_col12\" class=\"data row2 col12\" >0.262659</td>\n",
       "      <td id=\"T_cb5ed_row2_col13\" class=\"data row2 col13\" >0.417436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cb5ed_level0_row3\" class=\"row_heading level0 row3\" >BP</th>\n",
       "      <td id=\"T_cb5ed_row3_col0\" class=\"data row3 col0\" >0.273053</td>\n",
       "      <td id=\"T_cb5ed_row3_col1\" class=\"data row3 col1\" >-0.062693</td>\n",
       "      <td id=\"T_cb5ed_row3_col2\" class=\"data row3 col2\" >-0.043196</td>\n",
       "      <td id=\"T_cb5ed_row3_col3\" class=\"data row3 col3\" >1.000000</td>\n",
       "      <td id=\"T_cb5ed_row3_col4\" class=\"data row3 col4\" >0.173019</td>\n",
       "      <td id=\"T_cb5ed_row3_col5\" class=\"data row3 col5\" >0.155681</td>\n",
       "      <td id=\"T_cb5ed_row3_col6\" class=\"data row3 col6\" >0.116157</td>\n",
       "      <td id=\"T_cb5ed_row3_col7\" class=\"data row3 col7\" >-0.039136</td>\n",
       "      <td id=\"T_cb5ed_row3_col8\" class=\"data row3 col8\" >0.082793</td>\n",
       "      <td id=\"T_cb5ed_row3_col9\" class=\"data row3 col9\" >0.222800</td>\n",
       "      <td id=\"T_cb5ed_row3_col10\" class=\"data row3 col10\" >0.142472</td>\n",
       "      <td id=\"T_cb5ed_row3_col11\" class=\"data row3 col11\" >0.085697</td>\n",
       "      <td id=\"T_cb5ed_row3_col12\" class=\"data row3 col12\" >0.132045</td>\n",
       "      <td id=\"T_cb5ed_row3_col13\" class=\"data row3 col13\" >0.155383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cb5ed_level0_row4\" class=\"row_heading level0 row4\" >Cholesterol</th>\n",
       "      <td id=\"T_cb5ed_row4_col0\" class=\"data row4 col0\" >0.220056</td>\n",
       "      <td id=\"T_cb5ed_row4_col1\" class=\"data row4 col1\" >-0.201647</td>\n",
       "      <td id=\"T_cb5ed_row4_col2\" class=\"data row4 col2\" >0.090465</td>\n",
       "      <td id=\"T_cb5ed_row4_col3\" class=\"data row4 col3\" >0.173019</td>\n",
       "      <td id=\"T_cb5ed_row4_col4\" class=\"data row4 col4\" >1.000000</td>\n",
       "      <td id=\"T_cb5ed_row4_col5\" class=\"data row4 col5\" >0.025186</td>\n",
       "      <td id=\"T_cb5ed_row4_col6\" class=\"data row4 col6\" >0.167652</td>\n",
       "      <td id=\"T_cb5ed_row4_col7\" class=\"data row4 col7\" >-0.018739</td>\n",
       "      <td id=\"T_cb5ed_row4_col8\" class=\"data row4 col8\" >0.078243</td>\n",
       "      <td id=\"T_cb5ed_row4_col9\" class=\"data row4 col9\" >0.027709</td>\n",
       "      <td id=\"T_cb5ed_row4_col10\" class=\"data row4 col10\" >-0.005755</td>\n",
       "      <td id=\"T_cb5ed_row4_col11\" class=\"data row4 col11\" >0.126541</td>\n",
       "      <td id=\"T_cb5ed_row4_col12\" class=\"data row4 col12\" >0.028836</td>\n",
       "      <td id=\"T_cb5ed_row4_col13\" class=\"data row4 col13\" >0.118021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cb5ed_level0_row5\" class=\"row_heading level0 row5\" >FBS over 120</th>\n",
       "      <td id=\"T_cb5ed_row5_col0\" class=\"data row5 col0\" >0.123458</td>\n",
       "      <td id=\"T_cb5ed_row5_col1\" class=\"data row5 col1\" >0.042140</td>\n",
       "      <td id=\"T_cb5ed_row5_col2\" class=\"data row5 col2\" >-0.098537</td>\n",
       "      <td id=\"T_cb5ed_row5_col3\" class=\"data row5 col3\" >0.155681</td>\n",
       "      <td id=\"T_cb5ed_row5_col4\" class=\"data row5 col4\" >0.025186</td>\n",
       "      <td id=\"T_cb5ed_row5_col5\" class=\"data row5 col5\" >1.000000</td>\n",
       "      <td id=\"T_cb5ed_row5_col6\" class=\"data row5 col6\" >0.053499</td>\n",
       "      <td id=\"T_cb5ed_row5_col7\" class=\"data row5 col7\" >0.022494</td>\n",
       "      <td id=\"T_cb5ed_row5_col8\" class=\"data row5 col8\" >-0.004107</td>\n",
       "      <td id=\"T_cb5ed_row5_col9\" class=\"data row5 col9\" >-0.025538</td>\n",
       "      <td id=\"T_cb5ed_row5_col10\" class=\"data row5 col10\" >0.044076</td>\n",
       "      <td id=\"T_cb5ed_row5_col11\" class=\"data row5 col11\" >0.123774</td>\n",
       "      <td id=\"T_cb5ed_row5_col12\" class=\"data row5 col12\" >0.049237</td>\n",
       "      <td id=\"T_cb5ed_row5_col13\" class=\"data row5 col13\" >-0.016319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cb5ed_level0_row6\" class=\"row_heading level0 row6\" >EKG results</th>\n",
       "      <td id=\"T_cb5ed_row6_col0\" class=\"data row6 col0\" >0.128171</td>\n",
       "      <td id=\"T_cb5ed_row6_col1\" class=\"data row6 col1\" >0.039253</td>\n",
       "      <td id=\"T_cb5ed_row6_col2\" class=\"data row6 col2\" >0.074325</td>\n",
       "      <td id=\"T_cb5ed_row6_col3\" class=\"data row6 col3\" >0.116157</td>\n",
       "      <td id=\"T_cb5ed_row6_col4\" class=\"data row6 col4\" >0.167652</td>\n",
       "      <td id=\"T_cb5ed_row6_col5\" class=\"data row6 col5\" >0.053499</td>\n",
       "      <td id=\"T_cb5ed_row6_col6\" class=\"data row6 col6\" >1.000000</td>\n",
       "      <td id=\"T_cb5ed_row6_col7\" class=\"data row6 col7\" >-0.074628</td>\n",
       "      <td id=\"T_cb5ed_row6_col8\" class=\"data row6 col8\" >0.095098</td>\n",
       "      <td id=\"T_cb5ed_row6_col9\" class=\"data row6 col9\" >0.120034</td>\n",
       "      <td id=\"T_cb5ed_row6_col10\" class=\"data row6 col10\" >0.160614</td>\n",
       "      <td id=\"T_cb5ed_row6_col11\" class=\"data row6 col11\" >0.114368</td>\n",
       "      <td id=\"T_cb5ed_row6_col12\" class=\"data row6 col12\" >0.007337</td>\n",
       "      <td id=\"T_cb5ed_row6_col13\" class=\"data row6 col13\" >0.182091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cb5ed_level0_row7\" class=\"row_heading level0 row7\" >Max HR</th>\n",
       "      <td id=\"T_cb5ed_row7_col0\" class=\"data row7 col0\" >-0.402215</td>\n",
       "      <td id=\"T_cb5ed_row7_col1\" class=\"data row7 col1\" >-0.076101</td>\n",
       "      <td id=\"T_cb5ed_row7_col2\" class=\"data row7 col2\" >-0.317682</td>\n",
       "      <td id=\"T_cb5ed_row7_col3\" class=\"data row7 col3\" >-0.039136</td>\n",
       "      <td id=\"T_cb5ed_row7_col4\" class=\"data row7 col4\" >-0.018739</td>\n",
       "      <td id=\"T_cb5ed_row7_col5\" class=\"data row7 col5\" >0.022494</td>\n",
       "      <td id=\"T_cb5ed_row7_col6\" class=\"data row7 col6\" >-0.074628</td>\n",
       "      <td id=\"T_cb5ed_row7_col7\" class=\"data row7 col7\" >1.000000</td>\n",
       "      <td id=\"T_cb5ed_row7_col8\" class=\"data row7 col8\" >-0.380719</td>\n",
       "      <td id=\"T_cb5ed_row7_col9\" class=\"data row7 col9\" >-0.349045</td>\n",
       "      <td id=\"T_cb5ed_row7_col10\" class=\"data row7 col10\" >-0.386847</td>\n",
       "      <td id=\"T_cb5ed_row7_col11\" class=\"data row7 col11\" >-0.265333</td>\n",
       "      <td id=\"T_cb5ed_row7_col12\" class=\"data row7 col12\" >-0.253397</td>\n",
       "      <td id=\"T_cb5ed_row7_col13\" class=\"data row7 col13\" >-0.418514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cb5ed_level0_row8\" class=\"row_heading level0 row8\" >Exercise angina</th>\n",
       "      <td id=\"T_cb5ed_row8_col0\" class=\"data row8 col0\" >0.098297</td>\n",
       "      <td id=\"T_cb5ed_row8_col1\" class=\"data row8 col1\" >0.180022</td>\n",
       "      <td id=\"T_cb5ed_row8_col2\" class=\"data row8 col2\" >0.353160</td>\n",
       "      <td id=\"T_cb5ed_row8_col3\" class=\"data row8 col3\" >0.082793</td>\n",
       "      <td id=\"T_cb5ed_row8_col4\" class=\"data row8 col4\" >0.078243</td>\n",
       "      <td id=\"T_cb5ed_row8_col5\" class=\"data row8 col5\" >-0.004107</td>\n",
       "      <td id=\"T_cb5ed_row8_col6\" class=\"data row8 col6\" >0.095098</td>\n",
       "      <td id=\"T_cb5ed_row8_col7\" class=\"data row8 col7\" >-0.380719</td>\n",
       "      <td id=\"T_cb5ed_row8_col8\" class=\"data row8 col8\" >1.000000</td>\n",
       "      <td id=\"T_cb5ed_row8_col9\" class=\"data row8 col9\" >0.274672</td>\n",
       "      <td id=\"T_cb5ed_row8_col10\" class=\"data row8 col10\" >0.255908</td>\n",
       "      <td id=\"T_cb5ed_row8_col11\" class=\"data row8 col11\" >0.153347</td>\n",
       "      <td id=\"T_cb5ed_row8_col12\" class=\"data row8 col12\" >0.321449</td>\n",
       "      <td id=\"T_cb5ed_row8_col13\" class=\"data row8 col13\" >0.419303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cb5ed_level0_row9\" class=\"row_heading level0 row9\" >ST depression</th>\n",
       "      <td id=\"T_cb5ed_row9_col0\" class=\"data row9 col0\" >0.194234</td>\n",
       "      <td id=\"T_cb5ed_row9_col1\" class=\"data row9 col1\" >0.097412</td>\n",
       "      <td id=\"T_cb5ed_row9_col2\" class=\"data row9 col2\" >0.167244</td>\n",
       "      <td id=\"T_cb5ed_row9_col3\" class=\"data row9 col3\" >0.222800</td>\n",
       "      <td id=\"T_cb5ed_row9_col4\" class=\"data row9 col4\" >0.027709</td>\n",
       "      <td id=\"T_cb5ed_row9_col5\" class=\"data row9 col5\" >-0.025538</td>\n",
       "      <td id=\"T_cb5ed_row9_col6\" class=\"data row9 col6\" >0.120034</td>\n",
       "      <td id=\"T_cb5ed_row9_col7\" class=\"data row9 col7\" >-0.349045</td>\n",
       "      <td id=\"T_cb5ed_row9_col8\" class=\"data row9 col8\" >0.274672</td>\n",
       "      <td id=\"T_cb5ed_row9_col9\" class=\"data row9 col9\" >1.000000</td>\n",
       "      <td id=\"T_cb5ed_row9_col10\" class=\"data row9 col10\" >0.609712</td>\n",
       "      <td id=\"T_cb5ed_row9_col11\" class=\"data row9 col11\" >0.255005</td>\n",
       "      <td id=\"T_cb5ed_row9_col12\" class=\"data row9 col12\" >0.324333</td>\n",
       "      <td id=\"T_cb5ed_row9_col13\" class=\"data row9 col13\" >0.417967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cb5ed_level0_row10\" class=\"row_heading level0 row10\" >Slope of ST</th>\n",
       "      <td id=\"T_cb5ed_row10_col0\" class=\"data row10 col0\" >0.159774</td>\n",
       "      <td id=\"T_cb5ed_row10_col1\" class=\"data row10 col1\" >0.050545</td>\n",
       "      <td id=\"T_cb5ed_row10_col2\" class=\"data row10 col2\" >0.136900</td>\n",
       "      <td id=\"T_cb5ed_row10_col3\" class=\"data row10 col3\" >0.142472</td>\n",
       "      <td id=\"T_cb5ed_row10_col4\" class=\"data row10 col4\" >-0.005755</td>\n",
       "      <td id=\"T_cb5ed_row10_col5\" class=\"data row10 col5\" >0.044076</td>\n",
       "      <td id=\"T_cb5ed_row10_col6\" class=\"data row10 col6\" >0.160614</td>\n",
       "      <td id=\"T_cb5ed_row10_col7\" class=\"data row10 col7\" >-0.386847</td>\n",
       "      <td id=\"T_cb5ed_row10_col8\" class=\"data row10 col8\" >0.255908</td>\n",
       "      <td id=\"T_cb5ed_row10_col9\" class=\"data row10 col9\" >0.609712</td>\n",
       "      <td id=\"T_cb5ed_row10_col10\" class=\"data row10 col10\" >1.000000</td>\n",
       "      <td id=\"T_cb5ed_row10_col11\" class=\"data row10 col11\" >0.109498</td>\n",
       "      <td id=\"T_cb5ed_row10_col12\" class=\"data row10 col12\" >0.283678</td>\n",
       "      <td id=\"T_cb5ed_row10_col13\" class=\"data row10 col13\" >0.337616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cb5ed_level0_row11\" class=\"row_heading level0 row11\" >Number of vessels fluro</th>\n",
       "      <td id=\"T_cb5ed_row11_col0\" class=\"data row11 col0\" >0.356081</td>\n",
       "      <td id=\"T_cb5ed_row11_col1\" class=\"data row11 col1\" >0.086830</td>\n",
       "      <td id=\"T_cb5ed_row11_col2\" class=\"data row11 col2\" >0.225890</td>\n",
       "      <td id=\"T_cb5ed_row11_col3\" class=\"data row11 col3\" >0.085697</td>\n",
       "      <td id=\"T_cb5ed_row11_col4\" class=\"data row11 col4\" >0.126541</td>\n",
       "      <td id=\"T_cb5ed_row11_col5\" class=\"data row11 col5\" >0.123774</td>\n",
       "      <td id=\"T_cb5ed_row11_col6\" class=\"data row11 col6\" >0.114368</td>\n",
       "      <td id=\"T_cb5ed_row11_col7\" class=\"data row11 col7\" >-0.265333</td>\n",
       "      <td id=\"T_cb5ed_row11_col8\" class=\"data row11 col8\" >0.153347</td>\n",
       "      <td id=\"T_cb5ed_row11_col9\" class=\"data row11 col9\" >0.255005</td>\n",
       "      <td id=\"T_cb5ed_row11_col10\" class=\"data row11 col10\" >0.109498</td>\n",
       "      <td id=\"T_cb5ed_row11_col11\" class=\"data row11 col11\" >1.000000</td>\n",
       "      <td id=\"T_cb5ed_row11_col12\" class=\"data row11 col12\" >0.255648</td>\n",
       "      <td id=\"T_cb5ed_row11_col13\" class=\"data row11 col13\" >0.455336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cb5ed_level0_row12\" class=\"row_heading level0 row12\" >Thallium</th>\n",
       "      <td id=\"T_cb5ed_row12_col0\" class=\"data row12 col0\" >0.106100</td>\n",
       "      <td id=\"T_cb5ed_row12_col1\" class=\"data row12 col1\" >0.391046</td>\n",
       "      <td id=\"T_cb5ed_row12_col2\" class=\"data row12 col2\" >0.262659</td>\n",
       "      <td id=\"T_cb5ed_row12_col3\" class=\"data row12 col3\" >0.132045</td>\n",
       "      <td id=\"T_cb5ed_row12_col4\" class=\"data row12 col4\" >0.028836</td>\n",
       "      <td id=\"T_cb5ed_row12_col5\" class=\"data row12 col5\" >0.049237</td>\n",
       "      <td id=\"T_cb5ed_row12_col6\" class=\"data row12 col6\" >0.007337</td>\n",
       "      <td id=\"T_cb5ed_row12_col7\" class=\"data row12 col7\" >-0.253397</td>\n",
       "      <td id=\"T_cb5ed_row12_col8\" class=\"data row12 col8\" >0.321449</td>\n",
       "      <td id=\"T_cb5ed_row12_col9\" class=\"data row12 col9\" >0.324333</td>\n",
       "      <td id=\"T_cb5ed_row12_col10\" class=\"data row12 col10\" >0.283678</td>\n",
       "      <td id=\"T_cb5ed_row12_col11\" class=\"data row12 col11\" >0.255648</td>\n",
       "      <td id=\"T_cb5ed_row12_col12\" class=\"data row12 col12\" >1.000000</td>\n",
       "      <td id=\"T_cb5ed_row12_col13\" class=\"data row12 col13\" >0.525020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cb5ed_level0_row13\" class=\"row_heading level0 row13\" >target</th>\n",
       "      <td id=\"T_cb5ed_row13_col0\" class=\"data row13 col0\" >0.212322</td>\n",
       "      <td id=\"T_cb5ed_row13_col1\" class=\"data row13 col1\" >0.297721</td>\n",
       "      <td id=\"T_cb5ed_row13_col2\" class=\"data row13 col2\" >0.417436</td>\n",
       "      <td id=\"T_cb5ed_row13_col3\" class=\"data row13 col3\" >0.155383</td>\n",
       "      <td id=\"T_cb5ed_row13_col4\" class=\"data row13 col4\" >0.118021</td>\n",
       "      <td id=\"T_cb5ed_row13_col5\" class=\"data row13 col5\" >-0.016319</td>\n",
       "      <td id=\"T_cb5ed_row13_col6\" class=\"data row13 col6\" >0.182091</td>\n",
       "      <td id=\"T_cb5ed_row13_col7\" class=\"data row13 col7\" >-0.418514</td>\n",
       "      <td id=\"T_cb5ed_row13_col8\" class=\"data row13 col8\" >0.419303</td>\n",
       "      <td id=\"T_cb5ed_row13_col9\" class=\"data row13 col9\" >0.417967</td>\n",
       "      <td id=\"T_cb5ed_row13_col10\" class=\"data row13 col10\" >0.337616</td>\n",
       "      <td id=\"T_cb5ed_row13_col11\" class=\"data row13 col11\" >0.455336</td>\n",
       "      <td id=\"T_cb5ed_row13_col12\" class=\"data row13 col12\" >0.525020</td>\n",
       "      <td id=\"T_cb5ed_row13_col13\" class=\"data row13 col13\" >1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1ee79a38d60>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr= df.corr()\n",
    "corr.style.background_gradient(cmap='coolwarm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "22772c35",
   "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>target</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chest pain type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>62</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>38</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "target            0   1\n",
       "Chest pain type        \n",
       "1                15   5\n",
       "2                35   7\n",
       "3                62  17\n",
       "4                38  91"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['Chest pain type', 'target'])['target'].count().unstack()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "c4348ecc",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1500x1000 with 14 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Select numeric columns\n",
    "numeric_columns = df.select_dtypes(include=['number'])\n",
    "\n",
    "# Plotting boxplots for numeric columns\n",
    "plt.figure(figsize=(15, 10))\n",
    "for i, column in enumerate(numeric_columns, 1):\n",
    "    plt.subplot(3, 5, i)\n",
    "    sns.boxplot(y=numeric_columns[column])\n",
    "    plt.xlabel(column)\n",
    "    \n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "7d8b2643",
   "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>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Chest pain type</th>\n",
       "      <th>BP</th>\n",
       "      <th>Cholesterol</th>\n",
       "      <th>FBS over 120</th>\n",
       "      <th>EKG results</th>\n",
       "      <th>Max HR</th>\n",
       "      <th>Exercise angina</th>\n",
       "      <th>ST depression</th>\n",
       "      <th>Slope of ST</th>\n",
       "      <th>Number of vessels fluro</th>\n",
       "      <th>Thallium</th>\n",
       "      <th>Heart Disease</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>67</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>115</td>\n",
       "      <td>564</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>160</td>\n",
       "      <td>0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>Absence</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>65</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>140</td>\n",
       "      <td>417</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>157</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Absence</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>134</td>\n",
       "      <td>409</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>Presence</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>150</td>\n",
       "      <td>407</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>154</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>Presence</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>140</td>\n",
       "      <td>394</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>157</td>\n",
       "      <td>0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Absence</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>65</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>160</td>\n",
       "      <td>360</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>151</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Absence</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Absence</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>132</td>\n",
       "      <td>353</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>132</td>\n",
       "      <td>1</td>\n",
       "      <td>1.2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>Presence</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age  Sex  Chest pain type   BP  Cholesterol  FBS over 120  EKG results  \\\n",
       "1     67    0                3  115          564             0            2   \n",
       "52    65    0                3  140          417             1            2   \n",
       "181   56    0                4  134          409             0            2   \n",
       "9     63    0                4  150          407             0            2   \n",
       "188   62    0                4  140          394             0            2   \n",
       "123   65    0                3  160          360             0            2   \n",
       "71    57    0                4  120          354             0            0   \n",
       "257   55    1                4  132          353             0            0   \n",
       "\n",
       "     Max HR  Exercise angina  ST depression  Slope of ST  \\\n",
       "1       160                0            1.6            2   \n",
       "52      157                0            0.8            1   \n",
       "181     150                1            1.9            2   \n",
       "9       154                0            4.0            2   \n",
       "188     157                0            1.2            2   \n",
       "123     151                0            0.8            1   \n",
       "71      163                1            0.6            1   \n",
       "257     132                1            1.2            2   \n",
       "\n",
       "     Number of vessels fluro  Thallium Heart Disease  target  \n",
       "1                          0         7       Absence       0  \n",
       "52                         1         3       Absence       0  \n",
       "181                        2         7      Presence       1  \n",
       "9                          3         7      Presence       1  \n",
       "188                        0         3       Absence       0  \n",
       "123                        0         3       Absence       0  \n",
       "71                         0         3       Absence       0  \n",
       "257                        1         7      Presence       1  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['Cholesterol'] > 350 , :].sort_values(by='Cholesterol', ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "77c088e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x1200 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(figsize = (12, 12));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "f68507c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Group by age and target to get the frequency\n",
    "age_frequency = df.groupby([\"Age\", \"Heart Disease\"]).size().unstack()\n",
    "\n",
    "# Plot\n",
    "plt.figure(figsize=(12, 6))\n",
    "age_frequency.plot(kind=\"bar\", stacked=False)\n",
    "plt.xlabel(\"Age\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.title(\"Frequency of Heart Disease for Each Age\")\n",
    "plt.legend([\"No Heart Disease\", \"Heart Disease\"])\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51b091c0",
   "metadata": {},
   "source": [
    "## see if there is people at age of 70+ and don't have heart disease "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "9012cbad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter by aged 70 and above\n",
    "age_70plus = df[df[\"Age\"] >= 70]\n",
    "\n",
    "# Calculate the proportion of individuals without heart disease in this age group\n",
    "no_heart_disease = age_70plus[age_70plus[\"target\"] == 0].shape[0] / age_70plus.shape[0]\n",
    "no_heart_disease"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "305a1b00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                          int64\n",
       "Sex                          int64\n",
       "Chest pain type              int64\n",
       "BP                           int64\n",
       "Cholesterol                  int64\n",
       "FBS over 120                 int64\n",
       "EKG results                  int64\n",
       "Max HR                       int64\n",
       "Exercise angina              int64\n",
       "ST depression              float64\n",
       "Slope of ST                  int64\n",
       "Number of vessels fluro      int64\n",
       "Thallium                     int64\n",
       "Heart Disease               object\n",
       "target                       int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "a8d64f10",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns = 'Heart Disease' , inplace =True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "2e464069",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "numeric = df.select_dtypes(include='number')\n",
    "plt.figure(figsize=(10,10))\n",
    "sns.heatmap(numeric.corr(),annot=True,fmt='.1f')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "d203d127",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_ac1f0_row0_col0 {\n",
       "  background-color: #cdd9ec;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_ac1f0_row1_col0 {\n",
       "  background-color: #dedcdb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_ac1f0_row2_col0, #T_ac1f0_row9_col0 {\n",
       "  background-color: #f0cdbb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_ac1f0_row3_col0 {\n",
       "  background-color: #c1d4f4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_ac1f0_row4_col0 {\n",
       "  background-color: #b9d0f9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_ac1f0_row5_col0 {\n",
       "  background-color: #98b9ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_ac1f0_row6_col0 {\n",
       "  background-color: #c7d7f0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_ac1f0_row7_col0 {\n",
       "  background-color: #3b4cc0;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_ac1f0_row8_col0 {\n",
       "  background-color: #f1cdba;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_ac1f0_row10_col0 {\n",
       "  background-color: #e5d8d1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_ac1f0_row11_col0 {\n",
       "  background-color: #f3c7b1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_ac1f0_row12_col0 {\n",
       "  background-color: #f7b89c;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_ac1f0_row13_col0 {\n",
       "  background-color: #b40426;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_ac1f0\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_ac1f0_level0_col0\" class=\"col_heading level0 col0\" >target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_ac1f0_level0_row0\" class=\"row_heading level0 row0\" >Age</th>\n",
       "      <td id=\"T_ac1f0_row0_col0\" class=\"data row0 col0\" >0.212322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ac1f0_level0_row1\" class=\"row_heading level0 row1\" >Sex</th>\n",
       "      <td id=\"T_ac1f0_row1_col0\" class=\"data row1 col0\" >0.297721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ac1f0_level0_row2\" class=\"row_heading level0 row2\" >Chest pain type</th>\n",
       "      <td id=\"T_ac1f0_row2_col0\" class=\"data row2 col0\" >0.417436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ac1f0_level0_row3\" class=\"row_heading level0 row3\" >BP</th>\n",
       "      <td id=\"T_ac1f0_row3_col0\" class=\"data row3 col0\" >0.155383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ac1f0_level0_row4\" class=\"row_heading level0 row4\" >Cholesterol</th>\n",
       "      <td id=\"T_ac1f0_row4_col0\" class=\"data row4 col0\" >0.118021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ac1f0_level0_row5\" class=\"row_heading level0 row5\" >FBS over 120</th>\n",
       "      <td id=\"T_ac1f0_row5_col0\" class=\"data row5 col0\" >-0.016319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ac1f0_level0_row6\" class=\"row_heading level0 row6\" >EKG results</th>\n",
       "      <td id=\"T_ac1f0_row6_col0\" class=\"data row6 col0\" >0.182091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ac1f0_level0_row7\" class=\"row_heading level0 row7\" >Max HR</th>\n",
       "      <td id=\"T_ac1f0_row7_col0\" class=\"data row7 col0\" >-0.418514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ac1f0_level0_row8\" class=\"row_heading level0 row8\" >Exercise angina</th>\n",
       "      <td id=\"T_ac1f0_row8_col0\" class=\"data row8 col0\" >0.419303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ac1f0_level0_row9\" class=\"row_heading level0 row9\" >ST depression</th>\n",
       "      <td id=\"T_ac1f0_row9_col0\" class=\"data row9 col0\" >0.417967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ac1f0_level0_row10\" class=\"row_heading level0 row10\" >Slope of ST</th>\n",
       "      <td id=\"T_ac1f0_row10_col0\" class=\"data row10 col0\" >0.337616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ac1f0_level0_row11\" class=\"row_heading level0 row11\" >Number of vessels fluro</th>\n",
       "      <td id=\"T_ac1f0_row11_col0\" class=\"data row11 col0\" >0.455336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ac1f0_level0_row12\" class=\"row_heading level0 row12\" >Thallium</th>\n",
       "      <td id=\"T_ac1f0_row12_col0\" class=\"data row12 col0\" >0.525020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ac1f0_level0_row13\" class=\"row_heading level0 row13\" >target</th>\n",
       "      <td id=\"T_ac1f0_row13_col0\" class=\"data row13 col0\" >1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1ee790de650>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corelation = df.corr()[['target']]\n",
    "corelation.style.background_gradient(cmap='coolwarm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "bd2deef6",
   "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>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Chest pain type</th>\n",
       "      <th>BP</th>\n",
       "      <th>Cholesterol</th>\n",
       "      <th>FBS over 120</th>\n",
       "      <th>EKG results</th>\n",
       "      <th>Max HR</th>\n",
       "      <th>Exercise angina</th>\n",
       "      <th>ST depression</th>\n",
       "      <th>Slope of ST</th>\n",
       "      <th>Number of vessels fluro</th>\n",
       "      <th>Thallium</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>52.706667</td>\n",
       "      <td>0.553333</td>\n",
       "      <td>2.820000</td>\n",
       "      <td>128.866667</td>\n",
       "      <td>244.213333</td>\n",
       "      <td>0.153333</td>\n",
       "      <td>0.860</td>\n",
       "      <td>158.333333</td>\n",
       "      <td>0.153333</td>\n",
       "      <td>0.622667</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>0.286667</td>\n",
       "      <td>3.786667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>56.591667</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>3.616667</td>\n",
       "      <td>134.441667</td>\n",
       "      <td>256.466667</td>\n",
       "      <td>0.141667</td>\n",
       "      <td>1.225</td>\n",
       "      <td>138.858333</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>1.584167</td>\n",
       "      <td>1.816667</td>\n",
       "      <td>1.150000</td>\n",
       "      <td>5.833333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age       Sex  Chest pain type          BP  Cholesterol  \\\n",
       "target                                                                  \n",
       "0       52.706667  0.553333         2.820000  128.866667   244.213333   \n",
       "1       56.591667  0.833333         3.616667  134.441667   256.466667   \n",
       "\n",
       "        FBS over 120  EKG results      Max HR  Exercise angina  ST depression  \\\n",
       "target                                                                          \n",
       "0           0.153333        0.860  158.333333         0.153333       0.622667   \n",
       "1           0.141667        1.225  138.858333         0.550000       1.584167   \n",
       "\n",
       "        Slope of ST  Number of vessels fluro  Thallium  \n",
       "target                                                  \n",
       "0          1.400000                 0.286667  3.786667  \n",
       "1          1.816667                 1.150000  5.833333  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('target').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "ac680c31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Explore the distribution of the target variable 'target'\n",
    "sns.countplot(x='target', data=df)\n",
    "plt.xlabel('Heart Disease (0: No, 1: Yes)')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Distribution of Heart Disease')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f65b31c9",
   "metadata": {},
   "source": [
    "## looks like approximately equal (balanced dataset)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a1de760",
   "metadata": {},
   "source": [
    "## building the model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "fc8f445a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "from sklearn.metrics import f1_score\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "fbd063bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8518518518518519\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.83      0.86        30\n",
      "           1       0.81      0.88      0.84        24\n",
      "\n",
      "    accuracy                           0.85        54\n",
      "   macro avg       0.85      0.85      0.85        54\n",
      "weighted avg       0.86      0.85      0.85        54\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "# Select all columns except the target as features\n",
    "features = df.columns.drop('target')\n",
    "X = df[features]\n",
    "y = df['target']\n",
    "\n",
    "# Split the data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42 , stratify=y)\n",
    "\n",
    "# Train the Logistic Regression model\n",
    "model = LogisticRegression()\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# Predictions\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "# Model evaluation\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "report = classification_report(y_test, y_pred)\n",
    "\n",
    "print('Accuracy:', accuracy)\n",
    "print('Classification Report:\\n', report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "ede0dded",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Extract the coefficients and corresponding feature names\n",
    "coef = model.coef_[0]\n",
    "features = df.drop('target', axis=1).columns\n",
    "\n",
    "# Plot the coefficients\n",
    "plt.bar(features, coef)\n",
    "plt.xlabel('Features')\n",
    "plt.ylabel('Coefficient Magnitude')\n",
    "plt.title('Logistic Regression Coefficients')\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5242952f",
   "metadata": {},
   "source": [
    "## Using Other Classification Algorithms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "d615e70a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest F1 Score: 0.816326530612245\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# Create and train the model\n",
    "rf_model = RandomForestClassifier()\n",
    "rf_model.fit(X_train, y_train)\n",
    "\n",
    "# Predictions\n",
    "rf_y_pred = rf_model.predict(X_test)\n",
    "\n",
    "# Calculate F1 score for Random Forest model\n",
    "rf_f1 = f1_score(y_test, rf_y_pred)\n",
    "\n",
    "print('Random Forest F1 Score:', rf_f1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "d9bf3a24",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Parameters for SVC: {'C': 100, 'kernel': 'poly'}\n",
      "Best F1 Score for SVC: 0.7414548151390256\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "# Define the hyperparameters and their values to test\n",
    "param_grid_svc = {'C': [0.1, 1, 10, 100], 'kernel': ['rbf', 'poly', 'sigmoid']}\n",
    "\n",
    "# Create the GridSearchCV object\n",
    "grid_search_svc = GridSearchCV(SVC(), param_grid_svc, cv=5, scoring='f1')\n",
    "\n",
    "# Fit the grid search to the data\n",
    "grid_search_svc.fit(X_train, y_train)\n",
    "\n",
    "# Print the best parameters and corresponding f1 score\n",
    "print(\"Best Parameters for SVC:\", grid_search_svc.best_params_)\n",
    "print(\"Best F1 Score for SVC:\", grid_search_svc.best_score_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "8a464826",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: xgboost in c:\\users\\hp\\anaconda3\\lib\\site-packages (2.0.0)Note: you may need to restart the kernel to use updated packages.\n",
      "\n",
      "Requirement already satisfied: scipy in c:\\users\\hp\\anaconda3\\lib\\site-packages (from xgboost) (1.10.0)\n",
      "Requirement already satisfied: numpy in c:\\users\\hp\\anaconda3\\lib\\site-packages (from xgboost) (1.23.5)\n"
     ]
    }
   ],
   "source": [
    "pip install xgboost\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "f87bdefe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Parameters for XGBoost: {'learning_rate': 0.2, 'max_depth': 3, 'n_estimators': 100}\n",
      "Best F1 Score for XGBoost: 0.7993292482766167\n",
      "XGBoost F1 Score: 0.816326530612245\n"
     ]
    }
   ],
   "source": [
    "import xgboost as xgb\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# Define the hyperparameters and their values to test\n",
    "param_grid_xgb = {\n",
    "    'max_depth': [3, 6, 9],\n",
    "    'n_estimators': [50, 100, 200],\n",
    "    'learning_rate': [0.01, 0.1, 0.2]\n",
    "}\n",
    "\n",
    "# Create the XGBoost classifier\n",
    "xgb_classifier = xgb.XGBClassifier(random_state=42)\n",
    "\n",
    "# Create the GridSearchCV object\n",
    "grid_search_xgb = GridSearchCV(xgb_classifier, param_grid_xgb, cv=5, scoring='f1')\n",
    "\n",
    "# Fit the grid search to the data\n",
    "grid_search_xgb.fit(X_train, y_train)\n",
    "\n",
    "# Print the best parameters and corresponding f1 score\n",
    "print(\"Best Parameters for XGBoost:\", grid_search_xgb.best_params_)\n",
    "print(\"Best F1 Score for XGBoost:\", grid_search_xgb.best_score_)\n",
    "\n",
    "# Train XGBoost with the best parameters\n",
    "best_xgb = xgb.XGBClassifier(**grid_search_xgb.best_params_, random_state=42)\n",
    "best_xgb.fit(X_train, y_train)\n",
    "\n",
    "# Predictions\n",
    "xgb_y_pred = best_xgb.predict(X_test)\n",
    "\n",
    "# Calculate F1 score for XGBoost model\n",
    "xgb_f1 = f1_score(y_test, xgb_y_pred)\n",
    "\n",
    "print('XGBoost F1 Score:', xgb_f1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "3f499756",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "7e0fe52a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.7962962962962963\n",
      "F1 Score: 0.7843137254901961\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      0.77      0.81        30\n",
      "           1       0.74      0.83      0.78        24\n",
      "\n",
      "    accuracy                           0.80        54\n",
      "   macro avg       0.80      0.80      0.80        54\n",
      "weighted avg       0.80      0.80      0.80        54\n",
      "\n"
     ]
    }
   ],
   "source": [
    "clf = DecisionTreeClassifier(random_state=42)\n",
    "\n",
    "# Train the classifier\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "# Predict on the test set\n",
    "y_pred = clf.predict(X_test)\n",
    "\n",
    "# Evaluate the model\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "f1 = f1_score(y_test, y_pred)\n",
    "\n",
    "print(\"Accuracy:\", accuracy)\n",
    "print(\"F1 Score:\", f1)\n",
    "\n",
    "# You can also print a classification report for more detailed metrics\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "7bd3ae41",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Parameters: {'criterion': 'entropy', 'max_depth': None, 'min_samples_leaf': 4, 'min_samples_split': 2}\n",
      "Accuracy: 0.8333333333333334\n",
      "F1 Score: 0.830188679245283\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.77      0.84        30\n",
      "           1       0.76      0.92      0.83        24\n",
      "\n",
      "    accuracy                           0.83        54\n",
      "   macro avg       0.84      0.84      0.83        54\n",
      "weighted avg       0.85      0.83      0.83        54\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# Define the hyperparameters and their values to test\n",
    "param_grid = {\n",
    "    'criterion': ['gini', 'entropy'],\n",
    "    'max_depth': [None, 10, 20, 30, 40, 50],\n",
    "    'min_samples_split': [2, 5, 10],\n",
    "    'min_samples_leaf': [1, 2, 4]\n",
    "}\n",
    "\n",
    "# Initialize the decision tree classifier\n",
    "clf = DecisionTreeClassifier(random_state=42)\n",
    "\n",
    "# Initialize GridSearchCV\n",
    "grid_search = GridSearchCV(clf, param_grid, cv=5, n_jobs=-1)\n",
    "\n",
    "# Fit the grid search to the data\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "# Print the best parameters found\n",
    "print(\"Best Parameters:\", grid_search.best_params_)\n",
    "\n",
    "# Train the classifier with the best parameters\n",
    "best_clf = grid_search.best_estimator_\n",
    "best_clf.fit(X_train, y_train)\n",
    "\n",
    "# Predict on the test set\n",
    "y_pred = best_clf.predict(X_test)\n",
    "\n",
    "# Evaluate the model\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "f1 = f1_score(y_test, y_pred)\n",
    "\n",
    "print(\"Accuracy:\", accuracy)\n",
    "print(\"F1 Score:\", f1)\n",
    "\n",
    "# You can also print a classification report for more detailed metrics\n",
    "print(classification_report(y_test, y_pred))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6dea7e63",
   "metadata": {},
   "source": [
    "## it seems Logistic Regression is the best model in this case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cb1ec8be",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d0845da2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "88e588ae",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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