Trained Model/Heart-Model/heart.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Heart Disease Prediction\n",
    "**Dataset Link:** https://www.kaggle.com/datasets/johnsmith88/heart-disease-dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Loading and EDA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Description of the variables:\n",
    "\n",
    "1. age - age in years \n",
    "2. sex - (1 = male; 0 = female) \n",
    "3. cp - chest pain type \n",
    "    * 0: Typical angina: chest pain related decrease blood supply to the heart\n",
    "    * 1: Atypical angina: chest pain not related to heart\n",
    "    * 2: Non-anginal pain: typically esophageal spasms (non heart related)\n",
    "    * 3: Asymptomatic: chest pain not showing signs of disease\n",
    "4. trestbps - resting blood pressure (in mm Hg on admission to the hospital)\n",
    "    * anything above 130-140 is typically cause for concern\n",
    "5. chol - serum cholestoral in mg/dl \n",
    "    * serum = LDL + HDL + .2 * triglycerides\n",
    "    * above 200 is cause for concern\n",
    "6. fbs - (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false) \n",
    "    * '>126' mg/dL signals diabetes\n",
    "7. restecg - resting electrocardiographic results\n",
    "    * 0: Nothing to note\n",
    "    * 1: ST-T Wave abnormality\n",
    "        - can range from mild symptoms to severe problems\n",
    "        - signals non-normal heart beat\n",
    "    * 2: Possible or definite left ventricular hypertrophy\n",
    "        - Enlarged heart's main pumping chamber\n",
    "8. thalach - maximum heart rate achieved \n",
    "9. exang - exercise induced angina (1 = yes; 0 = no) \n",
    "10. oldpeak - ST depression induced by exercise relative to rest \n",
    "    * looks at stress of heart during excercise\n",
    "    * unhealthy heart will stress more\n",
    "11. slope - the slope of the peak exercise ST segment\n",
    "    * 0: Upsloping: better heart rate with excercise (uncommon)\n",
    "    * 1: Flatsloping: minimal change (typical healthy heart)\n",
    "    * 2: Downslopins: signs of unhealthy heart\n",
    "12. ca - number of major vessels (0-3) colored by flourosopy \n",
    "    * colored vessel means the doctor can see the blood passing through\n",
    "    * the more blood movement the better (no clots)\n",
    "13. thal - thalium stress result\n",
    "    * 1,3: normal\n",
    "    * 6: fixed defect: used to be defect but ok now\n",
    "    * 7: reversable defect: no proper blood movement when excercising \n",
    "14. target - have disease or not (1=yes, 0=no) (= the predicted attribute)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>125</td>\n",
       "      <td>212</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>168</td>\n",
       "      <td>0</td>\n",
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       "      <th>1</th>\n",
       "      <td>53</td>\n",
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       "      <td>140</td>\n",
       "      <td>203</td>\n",
       "      <td>1</td>\n",
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       "      <td>155</td>\n",
       "      <td>1</td>\n",
       "      <td>3.1</td>\n",
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       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>145</td>\n",
       "      <td>174</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>125</td>\n",
       "      <td>1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>61</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>148</td>\n",
       "      <td>203</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>161</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>138</td>\n",
       "      <td>294</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>106</td>\n",
       "      <td>0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   52    1   0       125   212    0        1      168      0      1.0      2   \n",
       "1   53    1   0       140   203    1        0      155      1      3.1      0   \n",
       "2   70    1   0       145   174    0        1      125      1      2.6      0   \n",
       "3   61    1   0       148   203    0        1      161      0      0.0      2   \n",
       "4   62    0   0       138   294    1        1      106      0      1.9      1   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   2     3       0  \n",
       "1   0     3       0  \n",
       "2   0     3       0  \n",
       "3   1     3       0  \n",
       "4   3     2       0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"heart.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>248</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>122</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112</td>\n",
       "      <td>149</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>125</td>\n",
       "      <td>0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>118</td>\n",
       "      <td>210</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>192</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "5    58    0   0       100   248    0        0      122      0      1.0   \n",
       "10   71    0   0       112   149    0        1      125      0      1.6   \n",
       "12   34    0   1       118   210    0        1      192      0      0.7   \n",
       "\n",
       "    slope  ca  thal  target  \n",
       "5       1   0     2       1  \n",
       "10      1   0     2       1  \n",
       "12      2   0     2       1  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.target==1][:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1025, 14)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
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   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1025 entries, 0 to 1024\n",
      "Data columns (total 14 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   age       1025 non-null   int64  \n",
      " 1   sex       1025 non-null   int64  \n",
      " 2   cp        1025 non-null   int64  \n",
      " 3   trestbps  1025 non-null   int64  \n",
      " 4   chol      1025 non-null   int64  \n",
      " 5   fbs       1025 non-null   int64  \n",
      " 6   restecg   1025 non-null   int64  \n",
      " 7   thalach   1025 non-null   int64  \n",
      " 8   exang     1025 non-null   int64  \n",
      " 9   oldpeak   1025 non-null   float64\n",
      " 10  slope     1025 non-null   int64  \n",
      " 11  ca        1025 non-null   int64  \n",
      " 12  thal      1025 non-null   int64  \n",
      " 13  target    1025 non-null   int64  \n",
      "dtypes: float64(1), int64(13)\n",
      "memory usage: 112.2 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1025.000000</td>\n",
       "      <td>1025.000000</td>\n",
       "      <td>1025.000000</td>\n",
       "      <td>1025.000000</td>\n",
       "      <td>1025.00000</td>\n",
       "      <td>1025.000000</td>\n",
       "      <td>1025.000000</td>\n",
       "      <td>1025.000000</td>\n",
       "      <td>1025.000000</td>\n",
       "      <td>1025.000000</td>\n",
       "      <td>1025.000000</td>\n",
       "      <td>1025.000000</td>\n",
       "      <td>1025.000000</td>\n",
       "      <td>1025.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>54.434146</td>\n",
       "      <td>0.695610</td>\n",
       "      <td>0.942439</td>\n",
       "      <td>131.611707</td>\n",
       "      <td>246.00000</td>\n",
       "      <td>0.149268</td>\n",
       "      <td>0.529756</td>\n",
       "      <td>149.114146</td>\n",
       "      <td>0.336585</td>\n",
       "      <td>1.071512</td>\n",
       "      <td>1.385366</td>\n",
       "      <td>0.754146</td>\n",
       "      <td>2.323902</td>\n",
       "      <td>0.513171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.072290</td>\n",
       "      <td>0.460373</td>\n",
       "      <td>1.029641</td>\n",
       "      <td>17.516718</td>\n",
       "      <td>51.59251</td>\n",
       "      <td>0.356527</td>\n",
       "      <td>0.527878</td>\n",
       "      <td>23.005724</td>\n",
       "      <td>0.472772</td>\n",
       "      <td>1.175053</td>\n",
       "      <td>0.617755</td>\n",
       "      <td>1.030798</td>\n",
       "      <td>0.620660</td>\n",
       "      <td>0.500070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>126.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>48.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>211.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>132.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>56.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>240.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>152.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>61.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>275.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>77.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>564.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               age          sex           cp     trestbps        chol  \\\n",
       "count  1025.000000  1025.000000  1025.000000  1025.000000  1025.00000   \n",
       "mean     54.434146     0.695610     0.942439   131.611707   246.00000   \n",
       "std       9.072290     0.460373     1.029641    17.516718    51.59251   \n",
       "min      29.000000     0.000000     0.000000    94.000000   126.00000   \n",
       "25%      48.000000     0.000000     0.000000   120.000000   211.00000   \n",
       "50%      56.000000     1.000000     1.000000   130.000000   240.00000   \n",
       "75%      61.000000     1.000000     2.000000   140.000000   275.00000   \n",
       "max      77.000000     1.000000     3.000000   200.000000   564.00000   \n",
       "\n",
       "               fbs      restecg      thalach        exang      oldpeak  \\\n",
       "count  1025.000000  1025.000000  1025.000000  1025.000000  1025.000000   \n",
       "mean      0.149268     0.529756   149.114146     0.336585     1.071512   \n",
       "std       0.356527     0.527878    23.005724     0.472772     1.175053   \n",
       "min       0.000000     0.000000    71.000000     0.000000     0.000000   \n",
       "25%       0.000000     0.000000   132.000000     0.000000     0.000000   \n",
       "50%       0.000000     1.000000   152.000000     0.000000     0.800000   \n",
       "75%       0.000000     1.000000   166.000000     1.000000     1.800000   \n",
       "max       1.000000     2.000000   202.000000     1.000000     6.200000   \n",
       "\n",
       "             slope           ca         thal       target  \n",
       "count  1025.000000  1025.000000  1025.000000  1025.000000  \n",
       "mean      1.385366     0.754146     2.323902     0.513171  \n",
       "std       0.617755     1.030798     0.620660     0.500070  \n",
       "min       0.000000     0.000000     0.000000     0.000000  \n",
       "25%       1.000000     0.000000     2.000000     0.000000  \n",
       "50%       1.000000     0.000000     2.000000     1.000000  \n",
       "75%       2.000000     1.000000     3.000000     1.000000  \n",
       "max       2.000000     4.000000     3.000000     1.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Checking for Null Values**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age         0\n",
       "sex         0\n",
       "cp          0\n",
       "trestbps    0\n",
       "chol        0\n",
       "fbs         0\n",
       "restecg     0\n",
       "thalach     0\n",
       "exang       0\n",
       "oldpeak     0\n",
       "slope       0\n",
       "ca          0\n",
       "thal        0\n",
       "target      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Checking wheather imbalance**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    0.513171\n",
       "0    0.486829\n",
       "Name: target, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.target.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.target.value_counts().plot(kind=\"bar\", color=[\"salmon\", \"lightblue\"]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**More Analysis**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    713\n",
       "0    312\n",
       "Name: sex, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sex.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a plot\n",
    "pd.crosstab(df.target, df.sex).plot(kind=\"bar\", figsize=(10,6), color=[\"salmon\", \"lightblue\"])\n",
    "\n",
    "plt.title(\"Heart Disease Frequency for Sex\")\n",
    "plt.xlabel(\"0 = No Disease, 1 = Disease\")\n",
    "plt.ylabel(\"Amount\")\n",
    "plt.legend([\"Female\", \"Male\"])\n",
    "plt.xticks(rotation=0);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(df.cp, df.target).plot(kind=\"bar\", \n",
    "                                   figsize=(10,6), \n",
    "                                   color=[\"lightblue\", \"salmon\"])\n",
    "\n",
    "plt.title(\"Heart Disease Frequency Per Chest Pain Type\")\n",
    "plt.xlabel(\"Chest Pain Type\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.legend([\"No Disease\", \"Disease\"])\n",
    "plt.xticks(rotation = 0);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "\n",
    "plt.scatter(df.age[df.target==1], \n",
    "            df.thalach[df.target==1], \n",
    "            c=\"salmon\")\n",
    "\n",
    "plt.scatter(df.age[df.target==0], \n",
    "            df.thalach[df.target==0], \n",
    "            c=\"lightblue\")\n",
    "\n",
    "plt.title(\"Heart Disease in function of Age and Max Heart Rate\")\n",
    "plt.xlabel(\"Age\")\n",
    "plt.legend([\"Disease\", \"No Disease\"])\n",
    "plt.ylabel(\"Max Heart Rate\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Histograms are a great way to check the distribution of a variable**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(bins=30, figsize=(15, 10));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Find the correlation between our independent variables**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.103240</td>\n",
       "      <td>-0.071966</td>\n",
       "      <td>0.271121</td>\n",
       "      <td>0.219823</td>\n",
       "      <td>0.121243</td>\n",
       "      <td>-0.132696</td>\n",
       "      <td>-0.390227</td>\n",
       "      <td>0.088163</td>\n",
       "      <td>0.208137</td>\n",
       "      <td>-0.169105</td>\n",
       "      <td>0.271551</td>\n",
       "      <td>0.072297</td>\n",
       "      <td>-0.229324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <td>-0.103240</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.041119</td>\n",
       "      <td>-0.078974</td>\n",
       "      <td>-0.198258</td>\n",
       "      <td>0.027200</td>\n",
       "      <td>-0.055117</td>\n",
       "      <td>-0.049365</td>\n",
       "      <td>0.139157</td>\n",
       "      <td>0.084687</td>\n",
       "      <td>-0.026666</td>\n",
       "      <td>0.111729</td>\n",
       "      <td>0.198424</td>\n",
       "      <td>-0.279501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cp</th>\n",
       "      <td>-0.071966</td>\n",
       "      <td>-0.041119</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.038177</td>\n",
       "      <td>-0.081641</td>\n",
       "      <td>0.079294</td>\n",
       "      <td>0.043581</td>\n",
       "      <td>0.306839</td>\n",
       "      <td>-0.401513</td>\n",
       "      <td>-0.174733</td>\n",
       "      <td>0.131633</td>\n",
       "      <td>-0.176206</td>\n",
       "      <td>-0.163341</td>\n",
       "      <td>0.434854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trestbps</th>\n",
       "      <td>0.271121</td>\n",
       "      <td>-0.078974</td>\n",
       "      <td>0.038177</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.127977</td>\n",
       "      <td>0.181767</td>\n",
       "      <td>-0.123794</td>\n",
       "      <td>-0.039264</td>\n",
       "      <td>0.061197</td>\n",
       "      <td>0.187434</td>\n",
       "      <td>-0.120445</td>\n",
       "      <td>0.104554</td>\n",
       "      <td>0.059276</td>\n",
       "      <td>-0.138772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chol</th>\n",
       "      <td>0.219823</td>\n",
       "      <td>-0.198258</td>\n",
       "      <td>-0.081641</td>\n",
       "      <td>0.127977</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.026917</td>\n",
       "      <td>-0.147410</td>\n",
       "      <td>-0.021772</td>\n",
       "      <td>0.067382</td>\n",
       "      <td>0.064880</td>\n",
       "      <td>-0.014248</td>\n",
       "      <td>0.074259</td>\n",
       "      <td>0.100244</td>\n",
       "      <td>-0.099966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fbs</th>\n",
       "      <td>0.121243</td>\n",
       "      <td>0.027200</td>\n",
       "      <td>0.079294</td>\n",
       "      <td>0.181767</td>\n",
       "      <td>0.026917</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.104051</td>\n",
       "      <td>-0.008866</td>\n",
       "      <td>0.049261</td>\n",
       "      <td>0.010859</td>\n",
       "      <td>-0.061902</td>\n",
       "      <td>0.137156</td>\n",
       "      <td>-0.042177</td>\n",
       "      <td>-0.041164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>restecg</th>\n",
       "      <td>-0.132696</td>\n",
       "      <td>-0.055117</td>\n",
       "      <td>0.043581</td>\n",
       "      <td>-0.123794</td>\n",
       "      <td>-0.147410</td>\n",
       "      <td>-0.104051</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.048411</td>\n",
       "      <td>-0.065606</td>\n",
       "      <td>-0.050114</td>\n",
       "      <td>0.086086</td>\n",
       "      <td>-0.078072</td>\n",
       "      <td>-0.020504</td>\n",
       "      <td>0.134468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thalach</th>\n",
       "      <td>-0.390227</td>\n",
       "      <td>-0.049365</td>\n",
       "      <td>0.306839</td>\n",
       "      <td>-0.039264</td>\n",
       "      <td>-0.021772</td>\n",
       "      <td>-0.008866</td>\n",
       "      <td>0.048411</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.380281</td>\n",
       "      <td>-0.349796</td>\n",
       "      <td>0.395308</td>\n",
       "      <td>-0.207888</td>\n",
       "      <td>-0.098068</td>\n",
       "      <td>0.422895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>exang</th>\n",
       "      <td>0.088163</td>\n",
       "      <td>0.139157</td>\n",
       "      <td>-0.401513</td>\n",
       "      <td>0.061197</td>\n",
       "      <td>0.067382</td>\n",
       "      <td>0.049261</td>\n",
       "      <td>-0.065606</td>\n",
       "      <td>-0.380281</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.310844</td>\n",
       "      <td>-0.267335</td>\n",
       "      <td>0.107849</td>\n",
       "      <td>0.197201</td>\n",
       "      <td>-0.438029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oldpeak</th>\n",
       "      <td>0.208137</td>\n",
       "      <td>0.084687</td>\n",
       "      <td>-0.174733</td>\n",
       "      <td>0.187434</td>\n",
       "      <td>0.064880</td>\n",
       "      <td>0.010859</td>\n",
       "      <td>-0.050114</td>\n",
       "      <td>-0.349796</td>\n",
       "      <td>0.310844</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.575189</td>\n",
       "      <td>0.221816</td>\n",
       "      <td>0.202672</td>\n",
       "      <td>-0.438441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>slope</th>\n",
       "      <td>-0.169105</td>\n",
       "      <td>-0.026666</td>\n",
       "      <td>0.131633</td>\n",
       "      <td>-0.120445</td>\n",
       "      <td>-0.014248</td>\n",
       "      <td>-0.061902</td>\n",
       "      <td>0.086086</td>\n",
       "      <td>0.395308</td>\n",
       "      <td>-0.267335</td>\n",
       "      <td>-0.575189</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.073440</td>\n",
       "      <td>-0.094090</td>\n",
       "      <td>0.345512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ca</th>\n",
       "      <td>0.271551</td>\n",
       "      <td>0.111729</td>\n",
       "      <td>-0.176206</td>\n",
       "      <td>0.104554</td>\n",
       "      <td>0.074259</td>\n",
       "      <td>0.137156</td>\n",
       "      <td>-0.078072</td>\n",
       "      <td>-0.207888</td>\n",
       "      <td>0.107849</td>\n",
       "      <td>0.221816</td>\n",
       "      <td>-0.073440</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.149014</td>\n",
       "      <td>-0.382085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thal</th>\n",
       "      <td>0.072297</td>\n",
       "      <td>0.198424</td>\n",
       "      <td>-0.163341</td>\n",
       "      <td>0.059276</td>\n",
       "      <td>0.100244</td>\n",
       "      <td>-0.042177</td>\n",
       "      <td>-0.020504</td>\n",
       "      <td>-0.098068</td>\n",
       "      <td>0.197201</td>\n",
       "      <td>0.202672</td>\n",
       "      <td>-0.094090</td>\n",
       "      <td>0.149014</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.337838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <td>-0.229324</td>\n",
       "      <td>-0.279501</td>\n",
       "      <td>0.434854</td>\n",
       "      <td>-0.138772</td>\n",
       "      <td>-0.099966</td>\n",
       "      <td>-0.041164</td>\n",
       "      <td>0.134468</td>\n",
       "      <td>0.422895</td>\n",
       "      <td>-0.438029</td>\n",
       "      <td>-0.438441</td>\n",
       "      <td>0.345512</td>\n",
       "      <td>-0.382085</td>\n",
       "      <td>-0.337838</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               age       sex        cp  trestbps      chol       fbs  \\\n",
       "age       1.000000 -0.103240 -0.071966  0.271121  0.219823  0.121243   \n",
       "sex      -0.103240  1.000000 -0.041119 -0.078974 -0.198258  0.027200   \n",
       "cp       -0.071966 -0.041119  1.000000  0.038177 -0.081641  0.079294   \n",
       "trestbps  0.271121 -0.078974  0.038177  1.000000  0.127977  0.181767   \n",
       "chol      0.219823 -0.198258 -0.081641  0.127977  1.000000  0.026917   \n",
       "fbs       0.121243  0.027200  0.079294  0.181767  0.026917  1.000000   \n",
       "restecg  -0.132696 -0.055117  0.043581 -0.123794 -0.147410 -0.104051   \n",
       "thalach  -0.390227 -0.049365  0.306839 -0.039264 -0.021772 -0.008866   \n",
       "exang     0.088163  0.139157 -0.401513  0.061197  0.067382  0.049261   \n",
       "oldpeak   0.208137  0.084687 -0.174733  0.187434  0.064880  0.010859   \n",
       "slope    -0.169105 -0.026666  0.131633 -0.120445 -0.014248 -0.061902   \n",
       "ca        0.271551  0.111729 -0.176206  0.104554  0.074259  0.137156   \n",
       "thal      0.072297  0.198424 -0.163341  0.059276  0.100244 -0.042177   \n",
       "target   -0.229324 -0.279501  0.434854 -0.138772 -0.099966 -0.041164   \n",
       "\n",
       "           restecg   thalach     exang   oldpeak     slope        ca  \\\n",
       "age      -0.132696 -0.390227  0.088163  0.208137 -0.169105  0.271551   \n",
       "sex      -0.055117 -0.049365  0.139157  0.084687 -0.026666  0.111729   \n",
       "cp        0.043581  0.306839 -0.401513 -0.174733  0.131633 -0.176206   \n",
       "trestbps -0.123794 -0.039264  0.061197  0.187434 -0.120445  0.104554   \n",
       "chol     -0.147410 -0.021772  0.067382  0.064880 -0.014248  0.074259   \n",
       "fbs      -0.104051 -0.008866  0.049261  0.010859 -0.061902  0.137156   \n",
       "restecg   1.000000  0.048411 -0.065606 -0.050114  0.086086 -0.078072   \n",
       "thalach   0.048411  1.000000 -0.380281 -0.349796  0.395308 -0.207888   \n",
       "exang    -0.065606 -0.380281  1.000000  0.310844 -0.267335  0.107849   \n",
       "oldpeak  -0.050114 -0.349796  0.310844  1.000000 -0.575189  0.221816   \n",
       "slope     0.086086  0.395308 -0.267335 -0.575189  1.000000 -0.073440   \n",
       "ca       -0.078072 -0.207888  0.107849  0.221816 -0.073440  1.000000   \n",
       "thal     -0.020504 -0.098068  0.197201  0.202672 -0.094090  0.149014   \n",
       "target    0.134468  0.422895 -0.438029 -0.438441  0.345512 -0.382085   \n",
       "\n",
       "              thal    target  \n",
       "age       0.072297 -0.229324  \n",
       "sex       0.198424 -0.279501  \n",
       "cp       -0.163341  0.434854  \n",
       "trestbps  0.059276 -0.138772  \n",
       "chol      0.100244 -0.099966  \n",
       "fbs      -0.042177 -0.041164  \n",
       "restecg  -0.020504  0.134468  \n",
       "thalach  -0.098068  0.422895  \n",
       "exang     0.197201 -0.438029  \n",
       "oldpeak   0.202672 -0.438441  \n",
       "slope    -0.094090  0.345512  \n",
       "ca        0.149014 -0.382085  \n",
       "thal      1.000000 -0.337838  \n",
       "target   -0.337838  1.000000  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix = df.corr()\n",
    "corr_matrix "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr_matrix = df.corr()\n",
    "plt.figure(figsize=(15, 10))\n",
    "sns.heatmap(corr_matrix, \n",
    "            annot=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Checking the outlier**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1025.000000</td>\n",
       "      <td>1025.000000</td>\n",
       "      <td>1025.000000</td>\n",
       "      <td>1025.000000</td>\n",
       "      <td>1025.00000</td>\n",
       "      <td>1025.000000</td>\n",
       "      <td>1025.000000</td>\n",
       "      <td>1025.000000</td>\n",
       "      <td>1025.000000</td>\n",
       "      <td>1025.000000</td>\n",
       "      <td>1025.000000</td>\n",
       "      <td>1025.000000</td>\n",
       "      <td>1025.000000</td>\n",
       "      <td>1025.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>54.434146</td>\n",
       "      <td>0.695610</td>\n",
       "      <td>0.942439</td>\n",
       "      <td>131.611707</td>\n",
       "      <td>246.00000</td>\n",
       "      <td>0.149268</td>\n",
       "      <td>0.529756</td>\n",
       "      <td>149.114146</td>\n",
       "      <td>0.336585</td>\n",
       "      <td>1.071512</td>\n",
       "      <td>1.385366</td>\n",
       "      <td>0.754146</td>\n",
       "      <td>2.323902</td>\n",
       "      <td>0.513171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.072290</td>\n",
       "      <td>0.460373</td>\n",
       "      <td>1.029641</td>\n",
       "      <td>17.516718</td>\n",
       "      <td>51.59251</td>\n",
       "      <td>0.356527</td>\n",
       "      <td>0.527878</td>\n",
       "      <td>23.005724</td>\n",
       "      <td>0.472772</td>\n",
       "      <td>1.175053</td>\n",
       "      <td>0.617755</td>\n",
       "      <td>1.030798</td>\n",
       "      <td>0.620660</td>\n",
       "      <td>0.500070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>126.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>48.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>211.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>132.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>56.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>240.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>152.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>61.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>275.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>77.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>564.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               age          sex           cp     trestbps        chol  \\\n",
       "count  1025.000000  1025.000000  1025.000000  1025.000000  1025.00000   \n",
       "mean     54.434146     0.695610     0.942439   131.611707   246.00000   \n",
       "std       9.072290     0.460373     1.029641    17.516718    51.59251   \n",
       "min      29.000000     0.000000     0.000000    94.000000   126.00000   \n",
       "25%      48.000000     0.000000     0.000000   120.000000   211.00000   \n",
       "50%      56.000000     1.000000     1.000000   130.000000   240.00000   \n",
       "75%      61.000000     1.000000     2.000000   140.000000   275.00000   \n",
       "max      77.000000     1.000000     3.000000   200.000000   564.00000   \n",
       "\n",
       "               fbs      restecg      thalach        exang      oldpeak  \\\n",
       "count  1025.000000  1025.000000  1025.000000  1025.000000  1025.000000   \n",
       "mean      0.149268     0.529756   149.114146     0.336585     1.071512   \n",
       "std       0.356527     0.527878    23.005724     0.472772     1.175053   \n",
       "min       0.000000     0.000000    71.000000     0.000000     0.000000   \n",
       "25%       0.000000     0.000000   132.000000     0.000000     0.000000   \n",
       "50%       0.000000     1.000000   152.000000     0.000000     0.800000   \n",
       "75%       0.000000     1.000000   166.000000     1.000000     1.800000   \n",
       "max       1.000000     2.000000   202.000000     1.000000     6.200000   \n",
       "\n",
       "             slope           ca         thal       target  \n",
       "count  1025.000000  1025.000000  1025.000000  1025.000000  \n",
       "mean      1.385366     0.754146     2.323902     0.513171  \n",
       "std       0.617755     1.030798     0.620660     0.500070  \n",
       "min       0.000000     0.000000     0.000000     0.000000  \n",
       "25%       1.000000     0.000000     2.000000     0.000000  \n",
       "50%       1.000000     0.000000     2.000000     1.000000  \n",
       "75%       2.000000     1.000000     3.000000     1.000000  \n",
       "max       2.000000     4.000000     3.000000     1.000000  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.boxplot(figsize=(15, 10));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=df.chol);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=df.trestbps);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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a3CRwuO/xTDeTTguWuyQ1yHKXFjcLbO17vKWbSacFy11a3BeAHUnOS/JK4HLg3jFnklbMz1CVFlFVx5P8IfBZYANwsKqeGHMsacW8FVKSGuS2jCQ1yHKXpAZZ7pLUIMtdkhpkuUtSgyx3SWqQ5S5JDfpvBeNfhTd23IoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=df.thalach);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Feature Selection**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "target = df['target']\n",
    "data = df.drop(columns=['target'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>125</td>\n",
       "      <td>212</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>168</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>203</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>155</td>\n",
       "      <td>1</td>\n",
       "      <td>3.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>145</td>\n",
       "      <td>174</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>125</td>\n",
       "      <td>1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>61</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>148</td>\n",
       "      <td>203</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>161</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>138</td>\n",
       "      <td>294</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>106</td>\n",
       "      <td>0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   52    1   0       125   212    0        1      168      0      1.0      2   \n",
       "1   53    1   0       140   203    1        0      155      1      3.1      0   \n",
       "2   70    1   0       145   174    0        1      125      1      2.6      0   \n",
       "3   61    1   0       148   203    0        1      161      0      0.0      2   \n",
       "4   62    0   0       138   294    1        1      106      0      1.9      1   \n",
       "\n",
       "   ca  thal  \n",
       "0   2     3  \n",
       "1   0     3  \n",
       "2   0     3  \n",
       "3   1     3  \n",
       "4   3     2  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>499</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0\n",
       "target     \n",
       "1       526\n",
       "0       499"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count = pd.DataFrame(df.value_counts(\"target\"))\n",
    "count"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(data, target)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_with_score(models, X_train, X_test, y_train, y_test):\n",
    "    \"\"\"\n",
    "    Fits and evaluates given machine learning models.\n",
    "    models : a dict of different Scikit-Learn machine learning models\n",
    "    X_train : training data\n",
    "    X_test : testing data\n",
    "    y_train : labels assosciated with training data\n",
    "    y_test : labels assosciated with test data\n",
    "    \n",
    "    \"\"\"\n",
    "    # Random seed for reproducible results\n",
    "    np.random.seed(42)\n",
    "    # Make a list to keep model scores\n",
    "    model_scores = {}\n",
    "    # Loop through models\n",
    "    for name, model in models.items():\n",
    "        # Fit the model to the data\n",
    "        model.fit(X_train, y_train)\n",
    "        # Evaluate the model and append its score to model_scores\n",
    "        model_scores[name] = model.score(X_test, y_test)\n",
    "    return model_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/programmer/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:444: 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"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'RandomForest': 0.9883268482490273,\n",
       " 'KNN': 0.7237354085603113,\n",
       " 'SVC': 0.6653696498054474,\n",
       " 'LogisticRegression': 0.8287937743190662}"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models = {\"RandomForest\": RandomForestClassifier(),\n",
    "          \"KNN\": KNeighborsClassifier(),\n",
    "          \"SVC\": SVC(),\n",
    "          \"LogisticRegression\": LogisticRegression()}\n",
    "\n",
    "model_scores = model_with_score(models=models,\n",
    "                             X_train=X_train,\n",
    "                             X_test=X_test,\n",
    "                             y_train=y_train,\n",
    "                             y_test=y_test)\n",
    "model_scores "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Hyper Tunning**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 5 candidates, totalling 25 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/programmer/anaconda3/lib/python3.8/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "/home/programmer/anaconda3/lib/python3.8/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "/home/programmer/anaconda3/lib/python3.8/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "/home/programmer/anaconda3/lib/python3.8/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "/home/programmer/anaconda3/lib/python3.8/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "/home/programmer/anaconda3/lib/python3.8/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.model_selection import KFold\n",
    "\n",
    "np.random.seed(42)\n",
    "\n",
    "# Define a grid of hyperparameters\n",
    "grid = {\"n_estimators\": [10, 100, 200, 500, 1000, 1200],\n",
    "        \"max_depth\": [None, 5, 10, 20, 30],\n",
    "        \"max_features\": [\"auto\", \"sqrt\"],\n",
    "        \"min_samples_split\": [2, 4, 6],\n",
    "        \"min_samples_leaf\": [1, 2, 4]}\n",
    "\n",
    "cv=KFold(n_splits=5,random_state=None,shuffle=False)\n",
    "\n",
    "rs_RFC = RandomizedSearchCV(estimator=RandomForestClassifier(),\n",
    "                                param_distributions=grid,\n",
    "                                cv=cv,\n",
    "                                n_iter=5,\n",
    "                                verbose=True)\n",
    "\n",
    "rs_RFC.fit(X_train, y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 500,\n",
       " 'min_samples_split': 2,\n",
       " 'min_samples_leaf': 1,\n",
       " 'max_features': 'auto',\n",
       " 'max_depth': 30}"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_RFC.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9883268482490273"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_RFC.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Checking the accuracy**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy-Score: 0.99\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.98      0.99       127\n",
      "           1       0.98      1.00      0.99       130\n",
      "\n",
      "    accuracy                           0.99       257\n",
      "   macro avg       0.99      0.99      0.99       257\n",
      "weighted avg       0.99      0.99      0.99       257\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
    "y_pred = rs_RFC.predict(X_test)\n",
    "print(f'Accuracy-Score: {accuracy_score(y_test, y_pred):.2f}')\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAd0AAAFlCAYAAACupDJ2AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Il7ecAAAACXBIWXMAAAsTAAALEwEAmpwYAAAQJklEQVR4nO3cf9zfc73H8efnuq6NiW3NyrJESqGEcqyTOtVJom6hc0qpVCfsxNHB0skRSoQjtJIyE0pFOnFoyM+KmGxJOc1Ri4NtjNGMtuwan/MHdtIuXWPX9fqu7X6/3a7bbd/P57Pv+/W5Xbfrelyf7+f6Xk3btgEABl9XpwcAgNWF6AJAEdEFgCKiCwBFRBcAioguABTpKVjDe5IAWN00fW10pQsARUQXAIqILgAUqbinu4xHfntdJ5aFVdoam7yu32OGDduwYBJYvSxadMdyH+tKFwCKiC4AFBFdACgiugBQRHQBoIjoAkAR0QWAIqILAEVEFwCKiC4AFBFdACgiugBQRHQBoIjoAkAR0QWAIqILAEVEFwCKiC4AFBFdACgiugBQRHQBoIjoAkAR0QWAIqILAEVEFwCKiC4AFBFdACgiugBQRHQBoIjoAkAR0QWAIqILAEVEFwCKiC4AFBFdACgiugBQRHQBoIjoAkAR0QWAIqILAEVEFwCKiC4AFBFdACgiugBQRHQBoIjoAkAR0QWAIqILAEVEFwCKiC4AFBFdACgiugBQRHQBoIjoAkAR0QWAIqILAEVEFwCKiC4AFBFdACgiugBQRHQBoIjoAkAR0QWAIqILAEVEFwCKiC4AFBFdACgiugBQRHQBoIjoAkAR0QWAIqILAEV6Oj0AA+fwiV/PT6b9MqNGDM/5Xz1qmf0X/WhqTv/+xWnbNs8ZtmYO3fdDefnGL1qhNRf39ubTJ07OjJl3ZMQ6a+cLn9onY9cbnam/+HUmnvm99C5ZkiE9PZnw0d0ybsvNV2gtWB2sscYaueKKczN06ND09PTk/PMvzlFHfbHTYzFAXOmuQnbe/vX52hETnnb/2DGjc8axB+e8k4/K+PftnCO+8o3lfu7Zc+flowcfu8z28y67JsOf85xcNPk/sscuO2TimecmSUYOXzsnHb5/zjv5qBx14F759AmTn/kJwWrokUceyY477p5x43bKuHE7ZYcd3phtt92602MxQPq90m2aZtMkuyQZ+8Sm2UkubNv2lsEcjGdum1e+PLPnznva/VtttsnSf2+56Uty77wHlj6e8qPr8p0Lr0jvkiXZ4uUb59P7fCjd3f3/TPbj62/MPu/fNUny1tdvk2MmfStt22azl2y49JiXbjg2f1zcm8W9vRk6ZMizODNYvfzhDwuTJEOG9KSnZ0jatu3wRAyUv/hdtWmaTyU5J0mT5IYnPpokZzdNc/Dgj8dgOe+yq7PdNlskSW67a05+ePUN+cYXDsn3Tvpcurq6ctGPpy7X88y9f37We96oJElPd3fWXmtY5i94+CnHXH7t9Gz2kg0FF5ZTV1dXrr/+4tx554256qprMm3aTZ0eiQHS35Xunkle0bZt759ubJrmxCS/TrLs642P7x+fZHySTJo0KePHjx+AURkoN/zqlpx/2TX5xnGHJEl+dtOM3PK7O/L+Az+XJPnj4t6MGjE8SXLAUSdl9tz70rvk0dx93/15z8cPT5J8YOe3Zte3vqHftWbeMTsTz/xeJh150CCdDax6Hnvssbz2tW/PiBHD893vnprNN39ZZsz4TafHYgD0F93Hkqyf5I4/2/6CJ/b1qW3bU5Oc+uTDZz0dA+43t9+Vz375jHz1iAkZOXztJI9/gnb++9dl/4+8Z5njJx768SSP39M97Iun5fRjn/oCx3rrjszc+x7ImNGjsuTRR/PwwkVLn/eeeQ/kwM+flM9P2DsbvOD5g3tisAp68MEF+clPrssOO7xJdFcR/d20OyDJlU3TXNI0zalPfPwwyZVJ9h/06RhQd997fw48+is5+hN7Z6OxY5ZuH7flZrn82um5f/6CJMmDDz2cOfc+/b3hP/WmcVvnwiuvTZJc/tPp2fZVm6Vpmix4eGH2++zE7P+Rd2frzTfp51mAJ40ePSojnnilac0118hb3vKG3HrrzA5PxUD5i1e6bdv+sGmalyXZNk/9Rappbds+OtjD8cz823GnZPrN/5P5Cx7O9h+ekH0/sGuWLHn807Tb29+cU865IPMXPJzPf/WsJEl3d3fOmfiZvORFY7PfHv+Qjx12fB5r2/R0d+eQffbI+s8f3e+a79rh73LICafmHXt/KiPWfk6O+9THkiTnTLkid949N5POvjCTzr4wSXLKkQdl3ZHDB+nsYdUwZszzM3nyienu7kpXV1e+//0pueSSqzo9FgOkKfituGUWeOS31w32mrDaWWOT1/V7zLBhG/Z7DPDMLFr053dgkzz+S8fL8D5dACgiugBQRHQBoIjoAkAR0QWAIqILAEVEFwCKiC4AFBFdACgiugBQRHQBoIjoAkAR0QWAIqILAEVEFwCKiC4AFBFdACgiugBQRHQBoIjoAkAR0QWAIqILAEVEFwCKiC4AFBFdACgiugBQRHQBoIjoAkAR0QWAIqILAEVEFwCKiC4AFBFdACgiugBQRHQBoIjoAkAR0QWAIqILAEVEFwCKiC4AFBFdACgiugBQRHQBoIjoAkAR0QWAIqILAEVEFwCKiC4AFBFdACgiugBQRHQBoIjoAkAR0QWAIqILAEVEFwCKiC4AFBFdACgiugBQRHQBoIjoAkAR0QWAIqILAEVEFwCKiC4AFBFdACgiugBQRHQBoIjoAkAR0QWAIqILAEVEFwCKiC4AFGnath3sNQZ9AQBYyTR9bXSlCwBFRBcAioguABQRXQAoIroAUER0AaCI6AJAEdEFgCI9HVl06NhOLAurtCWLZ/d7TO+82womgdXLkNEbL/exrnQBoIjoAkAR0QWAIqILAEVEFwCKiC4AFBFdACgiugBQRHQBoIjoAkAR0QWAIqILAEVEFwCKiC4AFBFdACgiugBQRHQBoIjoAkAR0QWAIqILAEVEFwCKiC4AFBFdACgiugBQRHQBoIjoAkAR0QWAIqILAEVEFwCKiC4AFBFdACgiugBQRHQBoIjoAkAR0QWAIqILAEVEFwCKiC4AFBFdACgiugBQRHQBoIjoAkAR0QWAIqILAEVEFwCKiC4AFBFdACgiugBQRHQBoIjoAkAR0QWAIqILAEVEFwCKiC4AFBFdACgiugBQRHQBoIjoAkAR0QWAIqILAEVEFwCKiC4AFBFdACgiugBQRHQBoIjoAkAR0QWAIqILAEVEFwCKiC4AFBFdACgiugBQpKfTA7ByetsOb8qJJ34u3V1dOf2Ms3PcF07u9EiwUjv06BNz9bU3ZNRzR+a/vnXKMvuvumZqTpr8zXQ1Xenu7s7B+4/Pq7d85Qqt+eCCh/KJw47JnHvmZv0x6+WEI/89I4avkymXXpWvf/t7SZustdawHHbQftl0k41XaC0GRtO27WCvscwCPUPHDvaarICurq7c8utrsuPbd8+sWXfn+qkX54N77Jtbbvltp0fjL1iyeHa/x/TOu61gktXT9JtuzlrDhuWQI4/vM7oLFy7KsGFrpmma3Drz9hx02NH5wdmTl+u5b7jxV7ng4svz+UM/8ZTtJ5z89YwYvk722mO3nHbWuVnw0EOZsO+e+cXNM7LxhhtkxPB1cs3Uafnq6d/O2ZMnDsRp0ocho/v8gabpa6OXl1nGtn+zdX73u//N7bffmd7e3px77gXZ+Z1v6/RYsFLbZqstMmL4Ok+7f621hqVpHv8+vOiPf0ya//+efPq3/zPv3fNf864P7ZOvnHbWcq/5o2umZpedtk+S7LLT9rnq6qlJkq232HzpLK96xaaZe++8Z3w+DI5nHd2maf5pIAdh5bH+2DG5a9acpY9nzb47668/poMTwarhip9cm3fuvnf2PejwHHnIgUmSa3/289w5a3bOOe1L+f6ZJ2fGrTMz/aabl+v57v/9/Dxv9Kgkyeh1n5v7fz9/mWPOm3JpXv/abQbsHFgxK3JP94gkZ/S1o2ma8UnGJ8mkSZMyfvz4FVgGYNWw/Ru3y/Zv3C7Tb7o5X5n8zZz2pWNy3bQbc90NN+bdH9kvSbJw0aLccdecbLPVFtl97wOyeHFvFi5alAcXPJR//PC/JEkm7PvRbDfuNU957qZpll5JP+mGn/8y5025LGd97fiaE6RffzG6TdP86ul2JVnv6f5f27anJjn1yYfPbjQ6Zc7se7LBC9df+viFY1+QOXPu6eBEsGrZZqstMmvOPfn9/AeTNtlrj/dmt13fvsxxT96Hfbp7uus+d2Tum/dAnjd6VO6b90BGjRyxdN+tM2/P4cdOzCknHJmRI4YP6vmw/Pp7eXm9JB9K8s4+Pu4f3NHolGnTb8pLX/ribLTRBhkyZEh2222X/GDKZZ0eC/6q3TlrTp78xdUZt87M4sW9GTlieF637atz/kWXZeHCRUmSuffN6/Nl4r686fWvzQWXXJEkueCSK/LmN/xtkuTue+7NAYccmWMO/2Q2etELB/5keNb6e3l5SpK127a96c93NE3z48EYiM579NFHs/8Bh+bii76T7q6unPmN72bGjN90eixYqX3yM8dm2i9+lfnzF+Qtu34w++65R5YsWZIkee+73pHLf/zTXHjJlenp6cmaawzN8Z87OE3TZLtxr8ltd9yVD/zzhCTJWsPWzDGHfzLrPndkv2vutcdu+cRhR+e8KZdm/THPzwlHHpIk+doZ38mDCx7KUcc//la/7u7unHv6lwfnxHlGvGUIVhHeMgSd4S1DALASEl0AKCK6AFBEdAGgiOgCQBHRBYAiogsARUQXAIqILgAUEV0AKCK6AFBEdAGgiOgCQBHRBYAiogsARUQXAIqILgAUEV0AKCK6AFBEdAGgiOgCQBHRBYAiogsARUQXAIqILgAUEV0AKCK6AFBEdAGgiOgCQBHRBYAiogsARUQXAIqILgAUEV0AKCK6AFBEdAGgiOgCQBHRBYAiogsARUQXAIqILgAUEV0AKCK6AFBEdAGgiOgCQBHRBYAiogsARUQXAIqILgAUEV0AKCK6AFBEdAGgiOgCQBHRBYAiogsARUQXAIqILgAUEV0AKCK6AFBEdAGgiOgCQBHRBYAiogsARUQXAIqILgAUEV0AKCK6AFBEdAGgiOgCQBHRBYAiogsARUQXAIo0bdsO9hqDvgAArGSavja60gWAIqILAEVEFwCKiC4AFOkpWKPPm8n8dWiaZnzbtqd2eg5Y3fjaWzW50qU/4zs9AKymfO2tgkQXAIqILgAUEV36454SdIavvVVQxV+kAgDiShcAyogufWqaZsemaW5tmmZm0zQHd3oeWF00TXN60zT3Nk3z352ehYEnuiyjaZruJCcn2SnJ5kl2b5pm885OBauNM5Ps2OkhGByiS1+2TTKzbdvb2rZdnOScJLt0eCZYLbRte3WSBzo9B4NDdOnL2CR3/cnjWU9sA2AFiC4AFBFd+jI7yQZ/8viFT2wDYAWILn2ZlmSTpmle3DTN0CTvS3Jhh2cC+KsnuiyjbdslSfZLcmmSW5Kc27btrzs7FawemqY5O8nUJC9vmmZW0zR7dnomBo6/SAUARVzpAkAR0QWAIqILAEVEFwCKiC4AFBFdACgiugBQRHQBoMj/Ae7AISPB2fL6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "c_m = confusion_matrix(y_test, y_pred)\n",
    "plt.figure(figsize=(8,6))\n",
    "sns.heatmap(c_m, annot=True, linewidths=8.5 , cbar=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/programmer/anaconda3/lib/python3.8/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function plot_roc_curve is deprecated; Function :func:`plot_roc_curve` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: :meth:`sklearn.metric.RocCurveDisplay.from_predictions` or :meth:`sklearn.metric.RocCurveDisplay.from_estimator`.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import plot_roc_curve\n",
    "\n",
    "plot_roc_curve(rs_RFC ,X_test, y_test);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save Trained Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['heart_model.pkl']"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import joblib\n",
    "joblib.dump(rs_RFC,\"heart_model.pkl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Load the saved model and making prediction**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "heart_model = joblib.load(\"heart_model.pkl\")"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "authorship_tag": "ABX9TyNxUP+CnCmcSPwDKMsTk+sp",
   "include_colab_link": true,
   "name": "Heart EDA.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}