end-to-end-heart-disease-classification.ipynb

{
 "cells": [
  {
   "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",
    "%matplotlib inline\n",
    "\n",
    "# Models from Scikit-Learn\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# Model Evaluation\n",
    "from sklearn.model_selection import train_test_split,cross_val_score\n",
    "from sklearn.model_selection import RandomizedSearchCV, GridSearchCV\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "from sklearn.metrics import precision_score,recall_score,f1_score\n",
    "from sklearn.metrics import plot_roc_curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(303, 14)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df =pd.read_csv(\"heart-disease.csv\")\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</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  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   0     1       1  \n",
       "1   0     2       1  \n",
       "2   0     2       1  \n",
       "3   0     2       1  \n",
       "4   0     2       1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>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>298</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>241</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>123</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>110</td>\n",
       "      <td>264</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>132</td>\n",
       "      <td>0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>68</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>144</td>\n",
       "      <td>193</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>141</td>\n",
       "      <td>0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>130</td>\n",
       "      <td>131</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>115</td>\n",
       "      <td>1</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>174</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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  \\\n",
       "298   57    0   0       140   241    0        1      123      1      0.2   \n",
       "299   45    1   3       110   264    0        1      132      0      1.2   \n",
       "300   68    1   0       144   193    1        1      141      0      3.4   \n",
       "301   57    1   0       130   131    0        1      115      1      1.2   \n",
       "302   57    0   1       130   236    0        0      174      0      0.0   \n",
       "\n",
       "     slope  ca  thal  target  \n",
       "298      1   0     3       0  \n",
       "299      1   0     3       0  \n",
       "300      1   2     3       0  \n",
       "301      1   1     3       0  \n",
       "302      1   1     2       0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    165\n",
       "0    138\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"target\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tI0kNMtwlqUGGuyQ1yHCXpAYZ7pLUoP8B4304fv57Ts0AAAAASUVORK5CYII=\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": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 303 entries, 0 to 302\n",
      "Data columns (total 14 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   age       303 non-null    int64  \n",
      " 1   sex       303 non-null    int64  \n",
      " 2   cp        303 non-null    int64  \n",
      " 3   trestbps  303 non-null    int64  \n",
      " 4   chol      303 non-null    int64  \n",
      " 5   fbs       303 non-null    int64  \n",
      " 6   restecg   303 non-null    int64  \n",
      " 7   thalach   303 non-null    int64  \n",
      " 8   exang     303 non-null    int64  \n",
      " 9   oldpeak   303 non-null    float64\n",
      " 10  slope     303 non-null    int64  \n",
      " 11  ca        303 non-null    int64  \n",
      " 12  thal      303 non-null    int64  \n",
      " 13  target    303 non-null    int64  \n",
      "dtypes: float64(1), int64(13)\n",
      "memory usage: 33.3 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Are there any missing values?\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>54.366337</td>\n",
       "      <td>0.683168</td>\n",
       "      <td>0.966997</td>\n",
       "      <td>131.623762</td>\n",
       "      <td>246.264026</td>\n",
       "      <td>0.148515</td>\n",
       "      <td>0.528053</td>\n",
       "      <td>149.646865</td>\n",
       "      <td>0.326733</td>\n",
       "      <td>1.039604</td>\n",
       "      <td>1.399340</td>\n",
       "      <td>0.729373</td>\n",
       "      <td>2.313531</td>\n",
       "      <td>0.544554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.082101</td>\n",
       "      <td>0.466011</td>\n",
       "      <td>1.032052</td>\n",
       "      <td>17.538143</td>\n",
       "      <td>51.830751</td>\n",
       "      <td>0.356198</td>\n",
       "      <td>0.525860</td>\n",
       "      <td>22.905161</td>\n",
       "      <td>0.469794</td>\n",
       "      <td>1.161075</td>\n",
       "      <td>0.616226</td>\n",
       "      <td>1.022606</td>\n",
       "      <td>0.612277</td>\n",
       "      <td>0.498835</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>126.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</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>47.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>211.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>133.500000</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>55.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>153.000000</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>274.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.600000</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>564.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.200000</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         fbs  \\\n",
       "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
       "mean    54.366337    0.683168    0.966997  131.623762  246.264026    0.148515   \n",
       "std      9.082101    0.466011    1.032052   17.538143   51.830751    0.356198   \n",
       "min     29.000000    0.000000    0.000000   94.000000  126.000000    0.000000   \n",
       "25%     47.500000    0.000000    0.000000  120.000000  211.000000    0.000000   \n",
       "50%     55.000000    1.000000    1.000000  130.000000  240.000000    0.000000   \n",
       "75%     61.000000    1.000000    2.000000  140.000000  274.500000    0.000000   \n",
       "max     77.000000    1.000000    3.000000  200.000000  564.000000    1.000000   \n",
       "\n",
       "          restecg     thalach       exang     oldpeak       slope          ca  \\\n",
       "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
       "mean     0.528053  149.646865    0.326733    1.039604    1.399340    0.729373   \n",
       "std      0.525860   22.905161    0.469794    1.161075    0.616226    1.022606   \n",
       "min      0.000000   71.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      0.000000  133.500000    0.000000    0.000000    1.000000    0.000000   \n",
       "50%      1.000000  153.000000    0.000000    0.800000    1.000000    0.000000   \n",
       "75%      1.000000  166.000000    1.000000    1.600000    2.000000    1.000000   \n",
       "max      2.000000  202.000000    1.000000    6.200000    2.000000    4.000000   \n",
       "\n",
       "             thal      target  \n",
       "count  303.000000  303.000000  \n",
       "mean     2.313531    0.544554  \n",
       "std      0.612277    0.498835  \n",
       "min      0.000000    0.000000  \n",
       "25%      2.000000    0.000000  \n",
       "50%      2.000000    1.000000  \n",
       "75%      3.000000    1.000000  \n",
       "max      3.000000    1.000000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    207\n",
       "0     96\n",
       "Name: sex, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Heart disease frequency according to Sex\n",
    "df.sex.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>sex</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>72</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sex      0    1\n",
       "target         \n",
       "0       24  114\n",
       "1       72   93"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compare target column with sex column\n",
    "pd.crosstab(df.target,df.sex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ixctv8j/11FMH3XbvvffmhBNOoKoAuOqqq4a11sfjCJkkSVotQ3lMxXCZOnUqRxxxxGPa3/e+9zFr1iw+/elPM3PmzEG3/eAHP8iRRx7JzjvvTFUxbdq0Zo/DyLJUOBZNnz695s6d27oMjRFr+jk5a4OWf4lKGrvmzZvX5D6rsWSwa5TkyqqaPtj6TllKkiQ1ZiCTJElqzEAmSZJW2li+5Wm4rcq1MZBJkqSVMmnSJO69915D2SCqinvvvZdJkyat1HZ+ylKSJK2UqVOnsmDBAhYtWtS6lFFp0qRJTJ06daW2MZBJkqSVMnHiRLbZZpvWZaxVnLKUJElqzEAmSZLUmIFMkiSpMQOZJElSYwYySZKkxgxkkiRJjRnIJEmSGjOQSZIkNWYgkyRJasxAJkmS1JiBTJIkqbFhC2RJvpzk7iTX97X9SZKLkszv/nxK154kn0tyc5Jrkzx3uOqSJEkabYZzhOxU4OUD2o4BLq6q7YCLu2WAfYDtutdhwEnDWJckSdKoMmyBrKq+D/xqQPN+wGnd+9OA/fvav1o9lwGbJtliuGqTJEkaTUb6HrLNq2ohQPfn07r2KcAdfest6NoeI8lhSeYmmbto0aJhLVaSJGkkjJab+jNIWw22YlWdXFXTq2r65MmTh7ksSZKk4TfSgeyuZVOR3Z93d+0LgK361psK3DnCtUmSJDUx0oHsAmBW934W8I2+9rd2n7Z8PrB42dSmJEnS2m7CcO04yZnAnsBmSRYAxwIfA76e5BDgduCAbvX/B7wCuBn4HXDQcNUlSZI02gxbIKuqN66g66WDrFvAO4arFkmSpNFstNzUL0mSNG4ZyCRJkhozkEmSJDVmIJMkSWrMQCZJktSYgUySJKkxA5kkSVJjBjJJkqTGDGSSJEmNGcgkSZIaM5BJkiQ1ZiCTJElqzEAmSZLU2ITWBUiSNJade+PC1iWMOq/ZfovWJYw5jpBJkiQ1ZiCTJElqzEAmSZLUmIFMkiSpMQOZJElSYwYySZKkxgxkkiRJjRnIJEmSGjOQSZIkNWYgkyRJasxAJkmS1JiBTJIkqTEDmSRJUmMGMkmSpMYMZJIkSY0ZyCRJkhozkEmSJDVmIJMkSWrMQCZJktSYgUySJKkxA5kkSVJjBjJJkqTGDGSSJEmNGcgkSZIaM5BJkiQ1ZiCTJElqzEAmSZLUmIFMkiSpMQOZJElSYwYySZKkxgxkkiRJjTUJZEmOSvLTJNcnOTPJpCTbJLk8yfwkZyVZr0VtkiRJI23EA1mSKcC7gOlV9WxgXeBA4OPAZ6pqO+DXwCEjXZskSVILraYsJwAbJJkAPAlYCMwEzu76TwP2b1SbJEnSiBrxQFZVvwCOB26nF8QWA1cCv6mqpd1qC4Apg22f5LAkc5PMXbRo0UiULEmSNKxaTFk+BdgP2AbYEtgQ2GeQVWuw7avq5KqaXlXTJ0+ePHyFSpIkjZAWU5YvA26tqkVV9RBwLrAHsGk3hQkwFbizQW2SJEkjrkUgux14fpInJQnwUuAG4BLgdd06s4BvNKhNkiRpxLW4h+xyejfv/wS4rqvhZOD9wLuT3Aw8FThlpGuTJElqYcITr7LmVdWxwLEDmm8BdmtQjiRJUlM+qV+SJKkxA5kkSVJjBjJJkqTGDGSSJEmNGcgkSZIaM5BJkiQ1ZiCTJElqzEAmSZLUmIFMkiSpMQOZJElSYwYySZKkxgxkkiRJjRnIJEmSGjOQSZIkNWYgkyRJasxAJkmS1NiE1gVIksaGhz78ntYljE4Hvrd1BVoLOEImSZLUmIFMkiSpMQOZJElSYwYySZKkxgxkkiRJjRnIJEmSGjOQSZIkNWYgkyRJasxAJkmS1JiBTJIkqTEDmSRJUmMGMkmSpMYMZJIkSY0ZyCRJkhozkEmSJDVmIJMkSWrsCQNZkouH0iZJkqRVM2FFHUkmAU8CNkvyFCBd15OBLUegNkmSpHFhhYEM+GvgSHrh60oeDWT3Af8yzHVJkiSNGysMZFX1WeCzSQ6vqhNGsCZJkqRx5fFGyACoqhOS7AFM61+/qr46jHVJkiSNG08YyJKcDmwLXA083DUXYCCTJElaA54wkAHTgWdWVQ13MZIkSePRUJ5Ddj3wp8NdiCRJ0ng1lBGyzYAbklwB/GFZY1XtO2xVSZIkjSNDCWTHDXcRkiRJ49lQPmV56UgUIkmSNF4N5VOW99P7VCXAesBE4IGqevJwFiZJkjReDGWEbOP+5ST7A7sNW0WSJEnjzFA+ZflHqup8YObqHDTJpknOTvKzJPOSvCDJnyS5KMn87s+nrM4xJEmSxoqhTFm+pm9xHXrPJVvdZ5J9FvhOVb0uyXr0vsT8A8DFVfWxJMcAxwDvX83jSJIkjXpD+ZTlX/W9XwrcBuy3qgdM8mTgRcDbAKrqQeDBJPsBe3arnQbMxkAmSZLGgaHcQ3bQGj7mnwGLgK8keQ5wJXAEsHlVLeyOuTDJ0wbbOMlhwGEAT3/609dwaZIkSSPvCe8hSzI1yXlJ7k5yV5JzkkxdjWNOAJ4LnFRVuwIP0JueHJKqOrmqplfV9MmTJ69GGZIkSaPDUG7q/wpwAbAlMAX4Zte2qhYAC6rq8m75bHoB7a4kWwB0f969GseQJEkaM4YSyCZX1Veqamn3OhVY5aGpqvolcEeS7bumlwI30At9s7q2WcA3VvUYkiRJY8lQbuq/J8mbgTO75TcC967mcQ8Hzug+YXkLcBC9cPj1JIcAtwMHrOYxJEmSxoShBLKDgROBz9B73MWPurZVVlVX03t8xkAvXZ39SpIkjUVD+ZTl7cC+I1CLJEnSuDSUB8NuQ2+KcVr/+lVlSJMkSVoDhjJleT5wCr1PVz4yvOVIkiSNP0MJZEuq6nPDXokkSdI4NZRA9tkkxwIXAn9Y1lhVPxm2qiRJksaRoQSynYC3ADN5dMqyumVJkiStpqEEslcDf9Z9CbgkSZLWsKE8qf8aYNPhLkSSJGm8GsoI2ebAz5LM4dF7yKqq9hu+siRJksaPoQSyY/veB3ghva9PkiRJ0hrwhFOWVXUpsBh4JXAqva83+vzwliVJkjR+rHCELMkzgAN59MvEzwJSVS8ZodokSZLGhcebsvwZ8F/AX1XVzQBJjhqRqiRJksaRx5uyfC3wS+CSJF9M8lJ695BJkiRpDVphIKuq86rqDcAOwGzgKGDzJCcl2WuE6pMkSVrrDeWm/geq6oyqehUwFbgaOGbYK5MkSRonhvJg2OWq6ldV9YWq8muTJEmS1pCVCmSSJEla8wxkkiRJjRnIJEmSGjOQSZIkNWYgkyRJasxAJkmS1JiBTJIkqTEDmSRJUmMGMkmSpMYMZJIkSY0ZyCRJkhozkEmSJDVmIJMkSWrMQCZJktSYgUySJKkxA5kkSVJjBjJJkqTGDGSSJEmNGcgkSZIaM5BJkiQ1ZiCTJElqzEAmSZLUmIFMkiSpMQOZJElSYwYySZKkxgxkkiRJjRnIJEmSGjOQSZIkNdYskCVZN8lVSb7VLW+T5PIk85OclWS9VrVJkiSNpJYjZEcA8/qWPw58pqq2A34NHNKkKkmSpBHWJJAlmQq8EvhStxxgJnB2t8ppwP4tapMkSRpprUbI/hl4H/BIt/xU4DdVtbRbXgBMGWzDJIclmZtk7qJFi4a/UkmSpGE24oEsyauAu6vqyv7mQVatwbavqpOranpVTZ88efKw1ChJkjSSJjQ45l8A+yZ5BTAJeDK9EbNNk0zoRsmmAnc2qE2SJGnEjfgIWVX9bVVNrappwIHAf1bVm4BLgNd1q80CvjHStUmSJLUwmp5D9n7g3UlupndP2SmN65EkSRoRLaYsl6uq2cDs7v0twG4t65EkSWphNI2QSZIkjUsGMkmSpMYMZJIkSY0ZyCRJkhozkEmSJDVmIJMkSWrMQCZJktSYgUySJKkxA5kkSVJjBjJJkqTGDGSSJEmNGcgkSZIaM5BJkiQ1ZiCTJElqzEAmSZLUmIFMkiSpMQOZJElSYwYySZKkxgxkkiRJjRnIJEmSGjOQSZIkNWYgkyRJasxAJkmS1JiBTJIkqTEDmSRJUmMGMkmSpMYMZJIkSY0ZyCRJkhozkEmSJDVmIJMkSWrMQCZJktSYgUySJKkxA5kkSVJjBjJJkqTGDGSSJEmNGcgkSZIaM5BJkiQ1ZiCTJElqbELrArTmPfTh97QuYXQ68L2tK5AkaVCOkEmSJDVmIJMkSWrMQCZJktSYgUySJKkxA5kkSVJjBjJJkqTGDGSSJEmNjXggS7JVkkuSzEvy0yRHdO1/kuSiJPO7P58y0rVJkiS10GKEbCnwnqraEXg+8I4kzwSOAS6uqu2Ai7tlSZKktd6IB7KqWlhVP+ne3w/MA6YA+wGndaudBuw/0rVJkiS10PQesiTTgF2By4HNq2oh9EIb8LQVbHNYkrlJ5i5atGikSpUkSRo2zQJZko2Ac4Ajq+q+oW5XVSdX1fSqmj558uThK1CSJGmENAlkSSbSC2NnVNW5XfNdSbbo+rcA7m5RmyRJ0khr8SnLAKcA86rq031dFwCzuvezgG+MdG2SJEktTGhwzL8A3gJcl+Tqru0DwMeAryc5BLgdOKBBbZIkSSNuxANZVf0AyAq6XzqStUiSJI0GPqlfkiSpMQOZJElSYwYySZKkxgxkkiRJjRnIJEmSGjOQSZIkNWYgkyRJasxAJkmS1JiBTJIkqTEDmSRJUmMGMkmSpMYMZJIkSY0ZyCRJkhozkEmSJDVmIJMkSWrMQCZJktSYgUySJKkxA5kkSVJjBjJJkqTGDGSSJEmNGcgkSZIaM5BJkiQ1ZiCTJElqzEAmSZLUmIFMkiSpMQOZJElSYwYySZKkxgxkkiRJjRnIJEmSGjOQSZIkNWYgkyRJasxAJkmS1JiBTJIkqTEDmSRJUmMGMkmSpMYMZJIkSY0ZyCRJkhozkEmSJDVmIJMkSWrMQCZJktSYgUySJKkxA5kkSVJjBjJJkqTGDGSSJEmNGcgkSZIaG1WBLMnLk9yY5OYkx7SuR5IkaSSMmkCWZF3gX4B9gGcCb0zyzLZVSZIkDb9RE8iA3YCbq+qWqnoQ+BqwX+OaJEmShl2qqnUNACR5HfDyqjq0W34LsHtVvXPAeocBh3WL2wM3jmihGss2A+5pXYSktY5/t2iotq6qyYN1TBjpSh5HBml7TFqsqpOBk4e/HK1tksytqumt65C0dvHvFq0Jo2nKcgGwVd/yVODORrVIkiSNmNEUyOYA2yXZJsl6wIHABY1rkiRJGnajZsqyqpYmeSfwXWBd4MtV9dPGZWnt4lS3pOHg3y1abaPmpn5JkqTxajRNWUqSJI1LBjJJkqTGDGSSJEmNGcgkSZIaGzWfspTWpCQ70PvqrSn0HjB8J3BBVc1rWpgkSYNwhExrnSTvp/ddqAGuoPeMuwBnJjmmZW2S1l5JDmpdg8YuH3uhtU6Sm4BnVdVDA9rXA35aVdu1qUzS2izJ7VX19NZ1aGxyylJro0eALYH/HtC+RdcnSaskybUr6gI2H8latHYxkGltdCRwcZL5wB1d29OBPwfe2awqSWuDzYG9gV8PaA/wo5EvR2sLA5nWOlX1nSTPAHajd1N/6H15/ZyqerhpcZLGum8BG1XV1QM7kswe+XK0tvAeMkmSpMb8lKUkSVJjBjJJkqTGDGSSlkvy8iQ3Jrl5TTyzLcm0JJXk8L62E5O8bSX2cVySXyS5Osn8JOcmeWZf/5f6l1tK8tEkdyT57UpuNy3J75NclWRekiuSzOrr39dn6ElrNwOZJACSrAv8C7AP8EzgjWso6NwNHNE9B25VfaaqdumeIXcW8J9JJgNU1aFVdcMaqHNN+Ca9D5Osip9X1a5VtSNwIHDUsgeNVtUFVfWxNVWkpNHHQCZpmd2Am6vqlqp6kN63Hey3Bva7CLgYmDWwI8kuSS5Lcm2S85I85Yl2VlVnARcC/7Pbx+wk05Osm+TUJNcnuS7JUV3/tkm+k+TKJP/Vfa0WSf4qyeXdqNT3kmzetb+4G427uuvbuGs/OsmcrtYPr6C2y6pq4apdpj/azy3Au4F3dcd+W5ITu/cHdOd4TZLvd23rJvlkX31/3bVvlOTiJD/prsl+XfuGSb7d7eP6JG/o2p+X5NLuWn03yRare+di+sAAAAQGSURBVC6ShsbHXkhaZgqPPrcNeo8K2X3gSkmOBt40yPbfr6p3rWDfHwP+I8mXB7R/FTi8qi5N8hHgWHrPkXsiPwF2GNC2CzClqp7d1blp134y8Paqmp9kd+BfgZnAD4DnV1UlORR4H/Ae4L3AO6rqh0k2ApYk2QvYjl5oDXBBkhdV1feHUCtJ3gQcPUjXzVX1upU4R4APAXtX1S/6zvEQYHFVzUiyPvDDJBfS+3m+uqruS7IZcFmSC4CXA3dW1Su7+jZJMhE4AdivqhZ1Ie2jwMFDOUdJq8dAJmmZDNL2mOfiVNUngU+uzI6r6tYkV9CNakEvBACbVtWlXdNpwL+vRq23AH+W5ATg28CFXaDaA/j3ZPkm63d/TgXO6kaB1gNu7dp/CHw6yRnAuVW1oAtkewFXdetsRC+gDSmQVdUZwBlDPLdlBjvHZfWdmuTrwLld217AzkmWhbtNuvoWAP+Y5EX0vqViCr0Hm14HHJ/k48C3quq/kjwbeDZwUXet1gVWe7RP0tAYyCQtswDYqm95KnDnwJVWcYQM4B+BsxliiHkCuwJz+xuq6tdJnkPvKervAF5Pb7TtN1W1yyD7OAH4dFVdkGRP4LhuPx9L8m3gFfRGlF5GLxz9U1V9YVWKXcURsl2BeQMbq+rt3UjfK4Grk+zS1Xd4VX13wHHfBkwGnldVDyW5DZhUVTcleV53jv/UjaadR++7Xl+wKucoafV4D5mkZeYA2yXZprsB/0DggoErVdUnuxvsB74eL4xRVT8DbgBe1S0vBn6d5C+7Vd4CXLqCzZdL8lp6I0JnDmjfDFinqs4BPgg8t6ruA25NckC3TrrQBr1RpF907/s/0bhtVV1XVR+nF/p2AL4LHNyNuJFkSpKnde8vTjLlCc79jBVcs0HDWJJpwPH0QuPAvm2r6vKq+hBwD70Q/V3gb7ppR5I8I8mG3Tne3YWxlwBbd/1bAr+rqn/rjvNc4EZgcpIXdOtMTPKsxzsvSWuOI2SSAKiqpUneSe8f93WBL1fVT9fwYT7Ko9N+0AtCn0/yJHpTjgetYLujkrwZ2BC4HphZVYsGrDMF+EqSZf+j+bfdn28CTkryd8BEeh9WuIbeiNi/J/kFcBmwTbf+kV14eZhegPyPqvpDkh2BH3fTeb8F3pzkHnrfkforgCSfoDct+6QkC4AvVdVxQ7w22ya5CpgE3A+cUFVfGWS9TybZjt6o2MXduVwLTAN+kl6Bi4D96U2TfjPJXOBq4GfdPnbq9vMI8BDwN1X1YDfl+bluOnkC8M/Amv4dkDQIvzpJklZRd9/VwVX17ta1SBrbDGSSJEmNeQ+ZJElSYwYySZKkxgxkkiRJjRnIJEmSGjOQSZIkNWYgkyRJauz/A9xFig4FHvJCAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a plot of crosstab\n",
    "pd.crosstab(df.target,df.sex).plot(kind=\"bar\",\n",
    "                                  figsize=(10,6),\n",
    "                                  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\"]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Age vs. Max Heart Rate for Heart Disease"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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",
    "# Scatter with positive examples\n",
    "plt.scatter(df.age[df.target==1],\n",
    "           df.thalach[df.target==1],\n",
    "           c=\"salmon\")\n",
    "\n",
    "# Scatter with negative examples\n",
    "plt.scatter(df.age[df.target==0],\n",
    "           df.thalach[df.target==0],\n",
    "           c=\"lightblue\")\n",
    "\n",
    "# Add some helpful info\n",
    "plt.title(\"Heart Disease in function of Age and Max Heart Rate\")\n",
    "plt.xlabel(\"Age\")\n",
    "plt.ylabel(\"Max heart rate\")\n",
    "plt.legend([\"Disease\",\"No Disease\"]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": [
    "# Check the distribution of the age column with a histogram\n",
    "df.age.plot.hist();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Heart Disease Frequency for Chest pain type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>cp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>104</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "target    0   1\n",
       "cp             \n",
       "0       104  39\n",
       "1         9  41\n",
       "2        18  69\n",
       "3         7  16"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df.cp,df.target)"
   ]
  },
  {
   "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>age</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.098447</td>\n",
       "      <td>-0.068653</td>\n",
       "      <td>0.279351</td>\n",
       "      <td>0.213678</td>\n",
       "      <td>0.121308</td>\n",
       "      <td>-0.116211</td>\n",
       "      <td>-0.398522</td>\n",
       "      <td>0.096801</td>\n",
       "      <td>0.210013</td>\n",
       "      <td>-0.168814</td>\n",
       "      <td>0.276326</td>\n",
       "      <td>0.068001</td>\n",
       "      <td>-0.225439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <td>-0.098447</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.049353</td>\n",
       "      <td>-0.056769</td>\n",
       "      <td>-0.197912</td>\n",
       "      <td>0.045032</td>\n",
       "      <td>-0.058196</td>\n",
       "      <td>-0.044020</td>\n",
       "      <td>0.141664</td>\n",
       "      <td>0.096093</td>\n",
       "      <td>-0.030711</td>\n",
       "      <td>0.118261</td>\n",
       "      <td>0.210041</td>\n",
       "      <td>-0.280937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cp</th>\n",
       "      <td>-0.068653</td>\n",
       "      <td>-0.049353</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.047608</td>\n",
       "      <td>-0.076904</td>\n",
       "      <td>0.094444</td>\n",
       "      <td>0.044421</td>\n",
       "      <td>0.295762</td>\n",
       "      <td>-0.394280</td>\n",
       "      <td>-0.149230</td>\n",
       "      <td>0.119717</td>\n",
       "      <td>-0.181053</td>\n",
       "      <td>-0.161736</td>\n",
       "      <td>0.433798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trestbps</th>\n",
       "      <td>0.279351</td>\n",
       "      <td>-0.056769</td>\n",
       "      <td>0.047608</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.123174</td>\n",
       "      <td>0.177531</td>\n",
       "      <td>-0.114103</td>\n",
       "      <td>-0.046698</td>\n",
       "      <td>0.067616</td>\n",
       "      <td>0.193216</td>\n",
       "      <td>-0.121475</td>\n",
       "      <td>0.101389</td>\n",
       "      <td>0.062210</td>\n",
       "      <td>-0.144931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chol</th>\n",
       "      <td>0.213678</td>\n",
       "      <td>-0.197912</td>\n",
       "      <td>-0.076904</td>\n",
       "      <td>0.123174</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.013294</td>\n",
       "      <td>-0.151040</td>\n",
       "      <td>-0.009940</td>\n",
       "      <td>0.067023</td>\n",
       "      <td>0.053952</td>\n",
       "      <td>-0.004038</td>\n",
       "      <td>0.070511</td>\n",
       "      <td>0.098803</td>\n",
       "      <td>-0.085239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fbs</th>\n",
       "      <td>0.121308</td>\n",
       "      <td>0.045032</td>\n",
       "      <td>0.094444</td>\n",
       "      <td>0.177531</td>\n",
       "      <td>0.013294</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.084189</td>\n",
       "      <td>-0.008567</td>\n",
       "      <td>0.025665</td>\n",
       "      <td>0.005747</td>\n",
       "      <td>-0.059894</td>\n",
       "      <td>0.137979</td>\n",
       "      <td>-0.032019</td>\n",
       "      <td>-0.028046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>restecg</th>\n",
       "      <td>-0.116211</td>\n",
       "      <td>-0.058196</td>\n",
       "      <td>0.044421</td>\n",
       "      <td>-0.114103</td>\n",
       "      <td>-0.151040</td>\n",
       "      <td>-0.084189</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.044123</td>\n",
       "      <td>-0.070733</td>\n",
       "      <td>-0.058770</td>\n",
       "      <td>0.093045</td>\n",
       "      <td>-0.072042</td>\n",
       "      <td>-0.011981</td>\n",
       "      <td>0.137230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thalach</th>\n",
       "      <td>-0.398522</td>\n",
       "      <td>-0.044020</td>\n",
       "      <td>0.295762</td>\n",
       "      <td>-0.046698</td>\n",
       "      <td>-0.009940</td>\n",
       "      <td>-0.008567</td>\n",
       "      <td>0.044123</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.378812</td>\n",
       "      <td>-0.344187</td>\n",
       "      <td>0.386784</td>\n",
       "      <td>-0.213177</td>\n",
       "      <td>-0.096439</td>\n",
       "      <td>0.421741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>exang</th>\n",
       "      <td>0.096801</td>\n",
       "      <td>0.141664</td>\n",
       "      <td>-0.394280</td>\n",
       "      <td>0.067616</td>\n",
       "      <td>0.067023</td>\n",
       "      <td>0.025665</td>\n",
       "      <td>-0.070733</td>\n",
       "      <td>-0.378812</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.288223</td>\n",
       "      <td>-0.257748</td>\n",
       "      <td>0.115739</td>\n",
       "      <td>0.206754</td>\n",
       "      <td>-0.436757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oldpeak</th>\n",
       "      <td>0.210013</td>\n",
       "      <td>0.096093</td>\n",
       "      <td>-0.149230</td>\n",
       "      <td>0.193216</td>\n",
       "      <td>0.053952</td>\n",
       "      <td>0.005747</td>\n",
       "      <td>-0.058770</td>\n",
       "      <td>-0.344187</td>\n",
       "      <td>0.288223</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.577537</td>\n",
       "      <td>0.222682</td>\n",
       "      <td>0.210244</td>\n",
       "      <td>-0.430696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>slope</th>\n",
       "      <td>-0.168814</td>\n",
       "      <td>-0.030711</td>\n",
       "      <td>0.119717</td>\n",
       "      <td>-0.121475</td>\n",
       "      <td>-0.004038</td>\n",
       "      <td>-0.059894</td>\n",
       "      <td>0.093045</td>\n",
       "      <td>0.386784</td>\n",
       "      <td>-0.257748</td>\n",
       "      <td>-0.577537</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.080155</td>\n",
       "      <td>-0.104764</td>\n",
       "      <td>0.345877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ca</th>\n",
       "      <td>0.276326</td>\n",
       "      <td>0.118261</td>\n",
       "      <td>-0.181053</td>\n",
       "      <td>0.101389</td>\n",
       "      <td>0.070511</td>\n",
       "      <td>0.137979</td>\n",
       "      <td>-0.072042</td>\n",
       "      <td>-0.213177</td>\n",
       "      <td>0.115739</td>\n",
       "      <td>0.222682</td>\n",
       "      <td>-0.080155</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.151832</td>\n",
       "      <td>-0.391724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thal</th>\n",
       "      <td>0.068001</td>\n",
       "      <td>0.210041</td>\n",
       "      <td>-0.161736</td>\n",
       "      <td>0.062210</td>\n",
       "      <td>0.098803</td>\n",
       "      <td>-0.032019</td>\n",
       "      <td>-0.011981</td>\n",
       "      <td>-0.096439</td>\n",
       "      <td>0.206754</td>\n",
       "      <td>0.210244</td>\n",
       "      <td>-0.104764</td>\n",
       "      <td>0.151832</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.344029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <td>-0.225439</td>\n",
       "      <td>-0.280937</td>\n",
       "      <td>0.433798</td>\n",
       "      <td>-0.144931</td>\n",
       "      <td>-0.085239</td>\n",
       "      <td>-0.028046</td>\n",
       "      <td>0.137230</td>\n",
       "      <td>0.421741</td>\n",
       "      <td>-0.436757</td>\n",
       "      <td>-0.430696</td>\n",
       "      <td>0.345877</td>\n",
       "      <td>-0.391724</td>\n",
       "      <td>-0.344029</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 -0.098447 -0.068653  0.279351  0.213678  0.121308   \n",
       "sex      -0.098447  1.000000 -0.049353 -0.056769 -0.197912  0.045032   \n",
       "cp       -0.068653 -0.049353  1.000000  0.047608 -0.076904  0.094444   \n",
       "trestbps  0.279351 -0.056769  0.047608  1.000000  0.123174  0.177531   \n",
       "chol      0.213678 -0.197912 -0.076904  0.123174  1.000000  0.013294   \n",
       "fbs       0.121308  0.045032  0.094444  0.177531  0.013294  1.000000   \n",
       "restecg  -0.116211 -0.058196  0.044421 -0.114103 -0.151040 -0.084189   \n",
       "thalach  -0.398522 -0.044020  0.295762 -0.046698 -0.009940 -0.008567   \n",
       "exang     0.096801  0.141664 -0.394280  0.067616  0.067023  0.025665   \n",
       "oldpeak   0.210013  0.096093 -0.149230  0.193216  0.053952  0.005747   \n",
       "slope    -0.168814 -0.030711  0.119717 -0.121475 -0.004038 -0.059894   \n",
       "ca        0.276326  0.118261 -0.181053  0.101389  0.070511  0.137979   \n",
       "thal      0.068001  0.210041 -0.161736  0.062210  0.098803 -0.032019   \n",
       "target   -0.225439 -0.280937  0.433798 -0.144931 -0.085239 -0.028046   \n",
       "\n",
       "           restecg   thalach     exang   oldpeak     slope        ca  \\\n",
       "age      -0.116211 -0.398522  0.096801  0.210013 -0.168814  0.276326   \n",
       "sex      -0.058196 -0.044020  0.141664  0.096093 -0.030711  0.118261   \n",
       "cp        0.044421  0.295762 -0.394280 -0.149230  0.119717 -0.181053   \n",
       "trestbps -0.114103 -0.046698  0.067616  0.193216 -0.121475  0.101389   \n",
       "chol     -0.151040 -0.009940  0.067023  0.053952 -0.004038  0.070511   \n",
       "fbs      -0.084189 -0.008567  0.025665  0.005747 -0.059894  0.137979   \n",
       "restecg   1.000000  0.044123 -0.070733 -0.058770  0.093045 -0.072042   \n",
       "thalach   0.044123  1.000000 -0.378812 -0.344187  0.386784 -0.213177   \n",
       "exang    -0.070733 -0.378812  1.000000  0.288223 -0.257748  0.115739   \n",
       "oldpeak  -0.058770 -0.344187  0.288223  1.000000 -0.577537  0.222682   \n",
       "slope     0.093045  0.386784 -0.257748 -0.577537  1.000000 -0.080155   \n",
       "ca       -0.072042 -0.213177  0.115739  0.222682 -0.080155  1.000000   \n",
       "thal     -0.011981 -0.096439  0.206754  0.210244 -0.104764  0.151832   \n",
       "target    0.137230  0.421741 -0.436757 -0.430696  0.345877 -0.391724   \n",
       "\n",
       "              thal    target  \n",
       "age       0.068001 -0.225439  \n",
       "sex       0.210041 -0.280937  \n",
       "cp       -0.161736  0.433798  \n",
       "trestbps  0.062210 -0.144931  \n",
       "chol      0.098803 -0.085239  \n",
       "fbs      -0.032019 -0.028046  \n",
       "restecg  -0.011981  0.137230  \n",
       "thalach  -0.096439  0.421741  \n",
       "exang     0.206754 -0.436757  \n",
       "oldpeak   0.210244 -0.430696  \n",
       "slope    -0.104764  0.345877  \n",
       "ca        0.151832 -0.391724  \n",
       "thal      1.000000 -0.344029  \n",
       "target   -0.344029  1.000000  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make a correlation matrix\n",
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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": [
    "# Let's make our correlation matrix a little prettier\n",
    "corr_matrix = df.corr()\n",
    "fig, ax = plt.subplots(figsize=(15,10))\n",
    "ax = sns.heatmap(corr_matrix,\n",
    "                annot=True,\n",
    "                linewidths=0.5,\n",
    "                fmt=\".2f\",\n",
    "                cmap=\"YlGnBu\")\n",
    "plt.yticks(rotation=0);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</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  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   0     1       1  \n",
       "1   0     2       1  \n",
       "2   0     2       1  \n",
       "3   0     2       1  \n",
       "4   0     2       1  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(\"target\",axis=1)\n",
    "y = df[\"target\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>63</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>241</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>123</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>110</td>\n",
       "      <td>264</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>132</td>\n",
       "      <td>0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>68</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>144</td>\n",
       "      <td>193</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>141</td>\n",
       "      <td>0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>130</td>\n",
       "      <td>131</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>115</td>\n",
       "      <td>1</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>174</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>303 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "0     63    1   3       145   233    1        0      150      0      2.3   \n",
       "1     37    1   2       130   250    0        1      187      0      3.5   \n",
       "2     41    0   1       130   204    0        0      172      0      1.4   \n",
       "3     56    1   1       120   236    0        1      178      0      0.8   \n",
       "4     57    0   0       120   354    0        1      163      1      0.6   \n",
       "..   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...   \n",
       "298   57    0   0       140   241    0        1      123      1      0.2   \n",
       "299   45    1   3       110   264    0        1      132      0      1.2   \n",
       "300   68    1   0       144   193    1        1      141      0      3.4   \n",
       "301   57    1   0       130   131    0        1      115      1      1.2   \n",
       "302   57    0   1       130   236    0        0      174      0      0.0   \n",
       "\n",
       "     slope  ca  thal  \n",
       "0        0   0     1  \n",
       "1        0   0     2  \n",
       "2        2   0     2  \n",
       "3        2   0     2  \n",
       "4        2   0     2  \n",
       "..     ...  ..   ...  \n",
       "298      1   0     3  \n",
       "299      1   0     3  \n",
       "300      1   2     3  \n",
       "301      1   1     3  \n",
       "302      1   1     2  \n",
       "\n",
       "[303 rows x 13 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1\n",
       "1      1\n",
       "2      1\n",
       "3      1\n",
       "4      1\n",
       "      ..\n",
       "298    0\n",
       "299    0\n",
       "300    0\n",
       "301    0\n",
       "302    0\n",
       "Name: target, Length: 303, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,\n",
    "                                                y,\n",
    "                                                test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
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       "      <td>163</td>\n",
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       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>51</td>\n",
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       "      <td>2</td>\n",
       "      <td>94</td>\n",
       "      <td>227</td>\n",
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       "<p>242 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "132   42    1   1       120   295    0        1      162      0      0.0   \n",
       "202   58    1   0       150   270    0        0      111      1      0.8   \n",
       "196   46    1   2       150   231    0        1      147      0      3.6   \n",
       "75    55    0   1       135   250    0        0      161      0      1.4   \n",
       "176   60    1   0       117   230    1        1      160      1      1.4   \n",
       "..   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...   \n",
       "188   50    1   2       140   233    0        1      163      0      0.6   \n",
       "71    51    1   2        94   227    0        1      154      1      0.0   \n",
       "106   69    1   3       160   234    1        0      131      0      0.1   \n",
       "270   46    1   0       120   249    0        0      144      0      0.8   \n",
       "102   63    0   1       140   195    0        1      179      0      0.0   \n",
       "\n",
       "     slope  ca  thal  \n",
       "132      2   0     2  \n",
       "202      2   0     3  \n",
       "196      1   0     2  \n",
       "75       1   0     2  \n",
       "176      2   2     3  \n",
       "..     ...  ..   ...  \n",
       "188      1   1     3  \n",
       "71       2   1     3  \n",
       "106      1   1     2  \n",
       "270      2   0     3  \n",
       "102      2   2     2  \n",
       "\n",
       "[242 rows x 13 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "132    1\n",
       "202    0\n",
       "196    0\n",
       "75     1\n",
       "176    0\n",
       "      ..\n",
       "188    0\n",
       "71     1\n",
       "106    1\n",
       "270    0\n",
       "102    1\n",
       "Name: target, Length: 242, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Put models in a dictionary\n",
    "models = {\"Logistic Regression\": LogisticRegression(),\n",
    "         \"KNN\": KNeighborsClassifier(),\n",
    "         \"Random Forest\": RandomForestClassifier()}\n",
    "\n",
    "# Create a function to fit and score models\n",
    "def fit_and_score(models,X_train,X_test,y_train,y_test):\n",
    "    \n",
    "    np.random.seed(42)\n",
    "    # Make a dictionary 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   \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\RAM\\Desktop\\sample_project_1\\env\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:938: 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": [
       "{'Logistic Regression': 0.8852459016393442,\n",
       " 'KNN': 0.6885245901639344,\n",
       " 'Random Forest': 0.8360655737704918}"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_scores = fit_and_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": "code",
   "execution_count": 27,
   "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": [
    "model_compare = pd.DataFrame(model_scores,index=[\"accuracy\"])\n",
    "model_compare.T.plot.bar();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we've got a baseline model...\n",
    "\n",
    "Let's look at the following:\n",
    "\n",
    "    * Hyperparameter tuning\n",
    "    * Feature importance\n",
    "    * Confusion matrix\n",
    "    * Cross-validation\n",
    "    * Precision\n",
    "    * Recall\n",
    "    * F1 score\n",
    "    * Classification report\n",
    "    * ROC curve\n",
    "    * Area under the curve"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hyperparameter tuning (by hand)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's tune KNN\n",
    "\n",
    "train_scores = []\n",
    "test_scores =[]\n",
    "# create a list of different values for n_neighbors\n",
    "neighbors = range(1,21)\n",
    "\n",
    "# Setup KNN instance\n",
    "knn = KNeighborsClassifier()\n",
    "\n",
    "# Loop through different n_neighbors\n",
    "for i in neighbors:\n",
    "    knn.set_params(n_neighbors=i)\n",
    "    \n",
    "    # Fit the algorithm\n",
    "    knn.fit(X_train,y_train)\n",
    "    \n",
    "    # Update the training scores list\n",
    "    train_scores.append(knn.score(X_train,y_train))\n",
    "    \n",
    "    # Update the test scores list\n",
    "    test_scores.append(knn.score(X_test,y_test))\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1.0,\n",
       " 0.8099173553719008,\n",
       " 0.7727272727272727,\n",
       " 0.743801652892562,\n",
       " 0.7603305785123967,\n",
       " 0.7520661157024794,\n",
       " 0.743801652892562,\n",
       " 0.7231404958677686,\n",
       " 0.71900826446281,\n",
       " 0.6942148760330579,\n",
       " 0.7272727272727273,\n",
       " 0.6983471074380165,\n",
       " 0.6900826446280992,\n",
       " 0.6942148760330579,\n",
       " 0.6859504132231405,\n",
       " 0.6735537190082644,\n",
       " 0.6859504132231405,\n",
       " 0.6652892561983471,\n",
       " 0.6818181818181818,\n",
       " 0.6694214876033058]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.6229508196721312,\n",
       " 0.639344262295082,\n",
       " 0.6557377049180327,\n",
       " 0.6721311475409836,\n",
       " 0.6885245901639344,\n",
       " 0.7213114754098361,\n",
       " 0.7049180327868853,\n",
       " 0.6885245901639344,\n",
       " 0.6885245901639344,\n",
       " 0.7049180327868853,\n",
       " 0.7540983606557377,\n",
       " 0.7377049180327869,\n",
       " 0.7377049180327869,\n",
       " 0.7377049180327869,\n",
       " 0.6885245901639344,\n",
       " 0.7213114754098361,\n",
       " 0.6885245901639344,\n",
       " 0.6885245901639344,\n",
       " 0.7049180327868853,\n",
       " 0.6557377049180327]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum KNN score on the test data: 75.41%\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": [
    "plt.plot(neighbors,train_scores,label=\"Train score\")\n",
    "plt.plot(neighbors,test_scores,label=\"Test score\")\n",
    "plt.xticks(np.arange(1,21,1))\n",
    "plt.xlabel(\"Number of neighbors\")\n",
    "plt.ylabel(\"Model score\")\n",
    "plt.legend()\n",
    "\n",
    "print(f\"Maximum KNN score on the test data: {max(test_scores)*100:.2f}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hyperparameter tuning with RandomizedSearchCV\n",
    "\n",
    "We're going to tune:\n",
    "\n",
    "* LogisticRegression()\n",
    "* RandomForestClassifier()\n",
    "\n",
    "...using RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a hyperparameter grid for LogisticRegression\n",
    "log_reg_grid = {\"C\": np.logspace(-4,4,20),\n",
    "               \"solver\": [\"liblinear\"]}\n",
    "\n",
    "# Create a hyperparameter grid for RandomForestClassifier\n",
    "rf_grid = {\"n_estimators\":np.arange(10,1000,50),\n",
    "          \"max_depth\":[None,3,5,10],\n",
    "          \"min_samples_split\":np.arange(2,20,2),\n",
    "          \"min_samples_leaf\":np.arange(1,20,2)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.00000000e-04, 2.63665090e-04, 6.95192796e-04, 1.83298071e-03,\n",
       "       4.83293024e-03, 1.27427499e-02, 3.35981829e-02, 8.85866790e-02,\n",
       "       2.33572147e-01, 6.15848211e-01, 1.62377674e+00, 4.28133240e+00,\n",
       "       1.12883789e+01, 2.97635144e+01, 7.84759970e+01, 2.06913808e+02,\n",
       "       5.45559478e+02, 1.43844989e+03, 3.79269019e+03, 1.00000000e+04])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.logspace(-4,4,20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    0.8s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, error_score=nan,\n",
       "                   estimator=LogisticRegression(C=1.0, class_weight=None,\n",
       "                                                dual=False, fit_intercept=True,\n",
       "                                                intercept_scaling=1,\n",
       "                                                l1_ratio=None, max_iter=100,\n",
       "                                                multi_class='auto', n_jobs=None,\n",
       "                                                penalty='l2', random_state=None,\n",
       "                                                solver='lbfgs', tol=0.0001,\n",
       "                                                verbose=0, warm_start=False),\n",
       "                   iid='deprecated', n_iter=20, n_jobs=None,\n",
       "                   param_distributions={'C':...\n",
       "       4.83293024e-03, 1.27427499e-02, 3.35981829e-02, 8.85866790e-02,\n",
       "       2.33572147e-01, 6.15848211e-01, 1.62377674e+00, 4.28133240e+00,\n",
       "       1.12883789e+01, 2.97635144e+01, 7.84759970e+01, 2.06913808e+02,\n",
       "       5.45559478e+02, 1.43844989e+03, 3.79269019e+03, 1.00000000e+04]),\n",
       "                                        'solver': ['liblinear']},\n",
       "                   pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
       "                   return_train_score=False, scoring=None, verbose=True)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tune LogisticRegression\n",
    "\n",
    "np.random.seed(42)\n",
    "# Setup random hyperparameter search for LogisticRegression\n",
    "rs_log_reg = RandomizedSearchCV(LogisticRegression(),\n",
    "                               param_distributions=log_reg_grid,\n",
    "                               cv=5,\n",
    "                               n_iter=20,\n",
    "                               verbose=True)\n",
    "# Fit random hyperparameter search model for LogisticRegression\n",
    "rs_log_reg.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'solver': 'liblinear', 'C': 0.23357214690901212}"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_log_reg.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8852459016393442"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_log_reg.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:  2.2min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, error_score=nan,\n",
       "                   estimator=RandomForestClassifier(bootstrap=True,\n",
       "                                                    ccp_alpha=0.0,\n",
       "                                                    class_weight=None,\n",
       "                                                    criterion='gini',\n",
       "                                                    max_depth=None,\n",
       "                                                    max_features='auto',\n",
       "                                                    max_leaf_nodes=None,\n",
       "                                                    max_samples=None,\n",
       "                                                    min_impurity_decrease=0.0,\n",
       "                                                    min_impurity_split=None,\n",
       "                                                    min_samples_leaf=1,\n",
       "                                                    min_samples_split=2,\n",
       "                                                    min_weight_fraction_leaf=0.0,\n",
       "                                                    n_estimators=100,\n",
       "                                                    n_jobs...\n",
       "                   param_distributions={'max_depth': [None, 3, 5, 10],\n",
       "                                        'min_samples_leaf': array([ 1,  3,  5,  7,  9, 11, 13, 15, 17, 19]),\n",
       "                                        'min_samples_split': array([ 2,  4,  6,  8, 10, 12, 14, 16, 18]),\n",
       "                                        'n_estimators': array([ 10,  60, 110, 160, 210, 260, 310, 360, 410, 460, 510, 560, 610,\n",
       "       660, 710, 760, 810, 860, 910, 960])},\n",
       "                   pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
       "                   return_train_score=False, scoring=None, verbose=True)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(42)\n",
    "\n",
    "rs_rf = RandomizedSearchCV(RandomForestClassifier(),\n",
    "                           param_distributions=rf_grid,\n",
    "                           cv=5,\n",
    "                           n_iter=20,\n",
    "                           verbose=True)\n",
    "rs_rf.fit(X_train,y_train)\n",
    "                           \n",
    "                          "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 210,\n",
       " 'min_samples_split': 4,\n",
       " 'min_samples_leaf': 19,\n",
       " 'max_depth': 3}"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_rf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8688524590163934"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_rf.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Logistic Regression': 0.8852459016393442,\n",
       " 'KNN': 0.6885245901639344,\n",
       " 'Random Forest': 0.8360655737704918}"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hyperparameter Tuning with GridSearchCV\n",
    "\n",
    "since our LogisticRegression model provides the best scores so far, We'll try and improve them again using GridSearchCV..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 30 candidates, totalling 150 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    0.7s finished\n"
     ]
    }
   ],
   "source": [
    "# Different hyperparameters for our LogisticRegression model\n",
    "log_reg_grid = {\"C\": np.logspace(-4,4,30),\n",
    "               \"solver\": [\"liblinear\"]}\n",
    "\n",
    "# Setup grid hyperparameter search for LogisticRegression\n",
    "gs_log_reg = GridSearchCV(LogisticRegression(),\n",
    "                         param_grid=log_reg_grid,\n",
    "                         cv=5,\n",
    "                         verbose=True)\n",
    "\n",
    "# Fit grid hyperparameter search model\n",
    "gs_log_reg.fit(X_train,y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 0.20433597178569418, 'solver': 'liblinear'}"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the best hyperparameters\n",
    "gs_log_reg.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8852459016393442"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate the grid search LogisticRegression model\n",
    "gs_log_reg.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To make comparisons and evaluate our trained model,first we need to make predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make predictions with tuned model\n",
    "y_preds = gs_log_reg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0,\n",
       "       0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "179    0\n",
       "228    0\n",
       "111    1\n",
       "246    0\n",
       "60     1\n",
       "      ..\n",
       "249    0\n",
       "104    1\n",
       "300    0\n",
       "193    0\n",
       "184    0\n",
       "Name: target, Length: 61, dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x1d710aba670>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# Plot Roc curve and calculate AUC metric\n",
    "plot_roc_curve(gs_log_reg,X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[25  4]\n",
      " [ 3 29]]\n"
     ]
    }
   ],
   "source": [
    "# Confusion matrix\n",
    "print(confusion_matrix(y_test,y_preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=1.5)\n",
    "\n",
    "def plot_conf_mat(y_test,y_preds):\n",
    "    fig, ax = plt.subplots(figsize=(3,3))\n",
    "    ax = sns.heatmap(confusion_matrix(y_test,y_preds),\n",
    "                    annot=True,\n",
    "                    cbar=False)\n",
    "    plt.xlabel(\"True label\")\n",
    "    plt.ylabel(\"predictd label\")\n",
    "plot_conf_mat(y_test,y_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.86      0.88        29\n",
      "           1       0.88      0.91      0.89        32\n",
      "\n",
      "    accuracy                           0.89        61\n",
      "   macro avg       0.89      0.88      0.88        61\n",
      "weighted avg       0.89      0.89      0.89        61\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,y_preds))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate evaluation metrics using cross-validation\n",
    "\n",
    "We're going to calculate precision,recall and f1-score of our model using cross-validation and to do so we'll be using `cross_val_score()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 0.20433597178569418, 'solver': 'liblinear'}"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs_log_reg.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a new classifier with best parameters\n",
    "clf = LogisticRegression(C=0.20433597178569418,\n",
    "                        solver=\"liblinear\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.81967213, 0.90163934, 0.86885246, 0.88333333, 0.75      ])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_acc = cross_val_score(clf,\n",
    "                        X,\n",
    "                        y,\n",
    "                        cv=5,\n",
    "                        scoring=\"accuracy\")\n",
    "cv_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8446994535519124"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_acc = np.mean(cv_acc)\n",
    "cv_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8207936507936507"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross-validated Precision\n",
    "cv_precision = cross_val_score(clf,\n",
    "                        X,\n",
    "                        y,\n",
    "                        cv=5,\n",
    "                        scoring=\"precision\")\n",
    "cv_precision = np.mean(cv_precision)\n",
    "cv_precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9212121212121213"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_recall = cross_val_score(clf,\n",
    "                        X,\n",
    "                        y,\n",
    "                        cv=5,\n",
    "                        scoring=\"recall\")\n",
    "cv_recall = np.mean(cv_recall)\n",
    "cv_recall      \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8673007976269721"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_f1 = cross_val_score(clf,\n",
    "                        X,\n",
    "                        y,\n",
    "                        cv=5,\n",
    "                        scoring=\"f1\")\n",
    "cv_f1 = np.mean(cv_f1)\n",
    "cv_f1      \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature importence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 0.20433597178569418, 'solver': 'liblinear'}"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs_log_reg.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=0.20433597178569418, class_weight=None, dual=False,\n",
       "                   fit_intercept=True, intercept_scaling=1, l1_ratio=None,\n",
       "                   max_iter=100, multi_class='auto', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='liblinear', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = LogisticRegression(C=0.20433597178569418,\n",
    "                        solver=\"liblinear\")\n",
    "clf.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.00316728, -0.86044651,  0.66067041, -0.01156993, -0.00166374,\n",
       "         0.04386107,  0.31275847,  0.02459361, -0.6041308 , -0.56862804,\n",
       "         0.45051628, -0.63609897, -0.67663373]])"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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