heart_disease_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b41173a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline \n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\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,
   "id": "770cb8cc",
   "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,
   "id": "7b32f6c8",
   "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",
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       "      <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",
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       "    <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,
   "id": "338a0902",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>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",
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       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
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       "      <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,
   "id": "25e05791",
   "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,
   "id": "e532f0b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"target\"].value_counts().plot(kind=\"bar\", color=[\"salmon\", \"lightblue\"]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1ea47b08",
   "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,
   "id": "4bd60bcb",
   "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,
   "id": "e74f6086",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\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,
   "id": "7d8e7266",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    207\n",
       "0     96\n",
       "Name: sex, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sex.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a9c7e244",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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,
   "id": "889acb25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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 = Disesease\")\n",
    "plt.ylabel(\"Amount\")\n",
    "plt.legend([\"Female\", \"Male\"])\n",
    "plt.xticks(rotation=0);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "271c780e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "162    11\n",
       "160     9\n",
       "163     9\n",
       "152     8\n",
       "173     8\n",
       "       ..\n",
       "202     1\n",
       "184     1\n",
       "121     1\n",
       "192     1\n",
       "90      1\n",
       "Name: thalach, Length: 91, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"thalach\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1ab9c774",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "plt.scatter(df.age[df.target==1],\n",
    "            df.thalach[df.target==1],\n",
    "            color=\"salmon\")\n",
    "\n",
    "plt.scatter(df.age[df.target==0],\n",
    "            df.thalach[df.target==0],\n",
    "           color=\"lightblue\")\n",
    "\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([\"Disese\", \"No Disese\"]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "19a6439c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.age.plot.hist();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6b5297a2",
   "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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df.cp, df.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d12beb32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(df.cp, df.target).plot(kind=\"bar\",\n",
    "                                   figsize=(10, 6),\n",
    "                                   color=[\"salmon\", \"lightblue\"])\n",
    "\n",
    "plt.title(\"Heart Disease Frequency Per Chect Pain Type\")\n",
    "plt.xlabel(\"Chect Pain Type\")\n",
    "plt.ylabel(\"Amount\")\n",
    "plt.legend([\"No Diseases\", \"Diseases\"])\n",
    "plt.xticks(rotation=0);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5651d7c6",
   "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": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "35556f5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "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",
    "                 linewidth=0.5,\n",
    "                 fmt=\".2f\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c84fc73",
   "metadata": {},
   "source": [
    "## 5. Modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6e172b8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split data into x and y\n",
    "x = df.drop(\"target\", axis=1)\n",
    "\n",
    "y = df[\"target\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "03e28694",
   "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": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "fae24184",
   "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": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d130cc52",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(x,y,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1cab32b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>51</td>\n",
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       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<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": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3786c301",
   "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,\n",
       " 242)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train, len(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "bf85bb40",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mosta\\anaconda3\\lib\\site-packages\\xgboost\\compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import MultiIndex, Int64Index\n"
     ]
    }
   ],
   "source": [
    "from xgboost import XGBClassifier,XGBRegressor\n",
    "from sklearn import svm\n",
    "\n",
    "\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "from catboost import CatBoostClassifier\n",
    "\n",
    "\n",
    "models = {\"Logistic Regression\": LogisticRegression(),\n",
    "          \"KNN\": KNeighborsClassifier(),\n",
    "          \"Random Forest\": RandomForestClassifier(),\n",
    "          \"XGBClassifier\":XGBClassifier(),\n",
    "          \"XGBRegressor\":XGBRegressor(),\n",
    "          \"svm\":svm.SVC(),\n",
    "          \"GaussianNB\":GaussianNB(),\n",
    "          \"CatBoostClassifier\":CatBoostClassifier()}\n",
    "\n",
    "def fit_and_score(models, x_train, x_test, y_train, y_test):\n",
    "    np.random.seed(42)\n",
    "    model_scores = {}\n",
    "    for name, model in models.items():\n",
    "        model.fit(x_train, y_train)\n",
    "        model_scores[name] = model.score(x_test, y_test)\n",
    "    return model_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "976d3e83",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mosta\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\mosta\\anaconda3\\lib\\site-packages\\xgboost\\sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "C:\\Users\\mosta\\anaconda3\\lib\\site-packages\\xgboost\\data.py:250: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  elif isinstance(data.columns, (pd.Int64Index, pd.RangeIndex)):\n",
      "C:\\Users\\mosta\\anaconda3\\lib\\site-packages\\xgboost\\data.py:250: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  elif isinstance(data.columns, (pd.Int64Index, pd.RangeIndex)):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[15:16:58] WARNING: C:\\Windows\\Temp\\abs_557yfx631l\\croots\\recipe\\xgboost-split_1659548953302\\work\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
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    {
     "data": {
      "text/plain": [
       "{'Logistic Regression': 0.8852459016393442,\n",
       " 'KNN': 0.6885245901639344,\n",
       " 'Random Forest': 0.8360655737704918,\n",
       " 'XGBClassifier': 0.819672131147541,\n",
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       " 'svm': 0.7049180327868853,\n",
       " 'GaussianNB': 0.8688524590163934,\n",
       " 'CatBoostClassifier': 0.8360655737704918}"
      ]
     },
     "execution_count": 27,
     "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",
    "\n",
    "model_scores"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c18fc6c6",
   "metadata": {},
   "source": [
    "### Model Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "dcc4419c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_compare = pd.DataFrame(model_scores, index=[\"accuracy\"])\n",
    "model_compare.T.plot.bar();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "002f5f06",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's tune KNN\n",
    "\n",
    "train_scores = []\n",
    "test_scores = []\n",
    "\n",
    "# Create a list of differnt 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))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "484933ad",
   "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": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a6a95436",
   "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": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e0edbbf3",
   "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 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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": "code",
   "execution_count": 33,
   "id": "04e84acd",
   "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": 34,
   "id": "e4b624a7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=5, estimator=LogisticRegression(), n_iter=20,\n",
       "                   param_distributions={&#x27;C&#x27;: 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]),\n",
       "                                        &#x27;solver&#x27;: [&#x27;liblinear&#x27;]},\n",
       "                   verbose=True)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(cv=5, estimator=LogisticRegression(), n_iter=20,\n",
       "                   param_distributions={&#x27;C&#x27;: 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]),\n",
       "                                        &#x27;solver&#x27;: [&#x27;liblinear&#x27;]},\n",
       "                   verbose=True)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=LogisticRegression(), n_iter=20,\n",
       "                   param_distributions={'C': 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]),\n",
       "                                        'solver': ['liblinear']},\n",
       "                   verbose=True)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tune LogisticRegression\n",
    "\n",
    "np.random.seed(42)\n",
    "\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",
    "\n",
    "# Fit random hyperparameter search model for LogisticRegression\n",
    "rs_log_reg.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "020e26dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'solver': 'liblinear', 'C': 0.23357214690901212}"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_log_reg.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "58feaa16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8852459016393442"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_log_reg.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "fb41fd90",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=5, estimator=RandomForestClassifier(), n_iter=20,\n",
       "                   param_distributions={&#x27;max_depth&#x27;: [None, 3, 5, 10],\n",
       "                                        &#x27;min_samples_leaf&#x27;: array([ 1,  3,  5,  7,  9, 11, 13, 15, 17, 19]),\n",
       "                                        &#x27;min_samples_split&#x27;: array([ 2,  4,  6,  8, 10, 12, 14, 16, 18]),\n",
       "                                        &#x27;n_estimators&#x27;: array([ 10,  60, 110, 160, 210, 260, 310, 360, 410, 460, 510, 560, 610,\n",
       "       660, 710, 760, 810, 860, 910, 960])},\n",
       "                   verbose=True)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(cv=5, estimator=RandomForestClassifier(), n_iter=20,\n",
       "                   param_distributions={&#x27;max_depth&#x27;: [None, 3, 5, 10],\n",
       "                                        &#x27;min_samples_leaf&#x27;: array([ 1,  3,  5,  7,  9, 11, 13, 15, 17, 19]),\n",
       "                                        &#x27;min_samples_split&#x27;: array([ 2,  4,  6,  8, 10, 12, 14, 16, 18]),\n",
       "                                        &#x27;n_estimators&#x27;: array([ 10,  60, 110, 160, 210, 260, 310, 360, 410, 460, 510, 560, 610,\n",
       "       660, 710, 760, 810, 860, 910, 960])},\n",
       "                   verbose=True)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=RandomForestClassifier(), n_iter=20,\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",
       "                   verbose=True)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Setup random seed\n",
    "np.random.seed(42)\n",
    "\n",
    "# Setup random hyperparameter search for RandomForestClassifier()\n",
    "rs_rf = RandomizedSearchCV(RandomForestClassifier(),\n",
    "                       param_distributions=rf_grid,\n",
    "                       cv=5,\n",
    "                       n_iter=20,\n",
    "                       verbose=True)\n",
    "\n",
    "# Fit random hyperparameter search model for RandomForestClassifier()\n",
    "rs_rf.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "a0574360",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 210,\n",
       " 'min_samples_split': 4,\n",
       " 'min_samples_leaf': 19,\n",
       " 'max_depth': 3}"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find the best hyperparameters\n",
    "rs_rf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "97315e53",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8688524590163934"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate the randomized search RandomForestClassifier model\n",
    "rs_rf.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "72f2a7a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Logistic Regression': 0.8852459016393442,\n",
       " 'KNN': 0.6885245901639344,\n",
       " 'Random Forest': 0.8360655737704918,\n",
       " 'XGBClassifier': 0.819672131147541,\n",
       " 'XGBRegressor': 0.47178018648170816,\n",
       " 'svm': 0.7049180327868853,\n",
       " 'GaussianNB': 0.8688524590163934,\n",
       " 'CatBoostClassifier': 0.8360655737704918}"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "b4f71441",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 30 candidates, totalling 150 fits\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=LogisticRegression(),\n",
       "             param_grid={&#x27;C&#x27;: array([1.00000000e-04, 1.88739182e-04, 3.56224789e-04, 6.72335754e-04,\n",
       "       1.26896100e-03, 2.39502662e-03, 4.52035366e-03, 8.53167852e-03,\n",
       "       1.61026203e-02, 3.03919538e-02, 5.73615251e-02, 1.08263673e-01,\n",
       "       2.04335972e-01, 3.85662042e-01, 7.27895384e-01, 1.37382380e+00,\n",
       "       2.59294380e+00, 4.89390092e+00, 9.23670857e+00, 1.74332882e+01,\n",
       "       3.29034456e+01, 6.21016942e+01, 1.17210230e+02, 2.21221629e+02,\n",
       "       4.17531894e+02, 7.88046282e+02, 1.48735211e+03, 2.80721620e+03,\n",
       "       5.29831691e+03, 1.00000000e+04]),\n",
       "                         &#x27;solver&#x27;: [&#x27;liblinear&#x27;]},\n",
       "             verbose=True)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=LogisticRegression(),\n",
       "             param_grid={&#x27;C&#x27;: array([1.00000000e-04, 1.88739182e-04, 3.56224789e-04, 6.72335754e-04,\n",
       "       1.26896100e-03, 2.39502662e-03, 4.52035366e-03, 8.53167852e-03,\n",
       "       1.61026203e-02, 3.03919538e-02, 5.73615251e-02, 1.08263673e-01,\n",
       "       2.04335972e-01, 3.85662042e-01, 7.27895384e-01, 1.37382380e+00,\n",
       "       2.59294380e+00, 4.89390092e+00, 9.23670857e+00, 1.74332882e+01,\n",
       "       3.29034456e+01, 6.21016942e+01, 1.17210230e+02, 2.21221629e+02,\n",
       "       4.17531894e+02, 7.88046282e+02, 1.48735211e+03, 2.80721620e+03,\n",
       "       5.29831691e+03, 1.00000000e+04]),\n",
       "                         &#x27;solver&#x27;: [&#x27;liblinear&#x27;]},\n",
       "             verbose=True)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5, estimator=LogisticRegression(),\n",
       "             param_grid={'C': array([1.00000000e-04, 1.88739182e-04, 3.56224789e-04, 6.72335754e-04,\n",
       "       1.26896100e-03, 2.39502662e-03, 4.52035366e-03, 8.53167852e-03,\n",
       "       1.61026203e-02, 3.03919538e-02, 5.73615251e-02, 1.08263673e-01,\n",
       "       2.04335972e-01, 3.85662042e-01, 7.27895384e-01, 1.37382380e+00,\n",
       "       2.59294380e+00, 4.89390092e+00, 9.23670857e+00, 1.74332882e+01,\n",
       "       3.29034456e+01, 6.21016942e+01, 1.17210230e+02, 2.21221629e+02,\n",
       "       4.17531894e+02, 7.88046282e+02, 1.48735211e+03, 2.80721620e+03,\n",
       "       5.29831691e+03, 1.00000000e+04]),\n",
       "                         'solver': ['liblinear']},\n",
       "             verbose=True)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": 42,
   "id": "c2c7c0b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 0.20433597178569418, 'solver': 'liblinear'}"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the best hyperparmaters\n",
    "gs_log_reg.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "c3dca2ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8852459016393442"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluatethw grid search LogisticRegression model\n",
    "gs_log_reg.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "3fb0071a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make predictions with tuned model\n",
    "y_preds = gs_log_reg.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "7020d9cc",
   "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": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "2a1d5c2c",
   "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": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "1a17d3a7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mosta\\anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function plot_roc_curve is deprecated; Function :func:`plot_roc_curve` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: :meth:`sklearn.metrics.RocCurveDisplay.from_predictions` or :meth:`sklearn.metrics.RocCurveDisplay.from_estimator`.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x1ed428e1c40>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot ROC curve and calculate and calculate AUC metric\n",
    "plot_roc_curve(gs_log_reg, x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "1c1ed5e9",
   "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": 49,
   "id": "7a1a3d04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 300x300 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",
    "    \"\"\"\n",
    "    Plots a nice looking confusion matrix using Seaborn's heatmap()\n",
    "    \"\"\"\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(\"Predict label\")\n",
    "    \n",
    "plot_conf_mat(y_test, y_preds)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49fe255f",
   "metadata": {},
   "source": [
    "Now we've got a ROC curve, an AUC metric and a confusion matrix, let's get a classification report as well as cross-validated precision, recall and f1-score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "14d8dd2b",
   "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": "code",
   "execution_count": 51,
   "id": "10005279",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 0.20433597178569418, 'solver': 'liblinear'}"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check best hyperparameters\n",
    "gs_log_reg.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "a57eb80f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a new classifier with best parameters\n",
    "clf = LogisticRegression(C=0.20433597178569418,\n",
    "                         solver=\"liblinear\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "49add46d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.81967213, 0.90163934, 0.86885246, 0.88333333, 0.75      ])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross-validated accuracy\n",
    "cv_acc = cross_val_score(clf,\n",
    "                         x,\n",
    "                         y,\n",
    "                         cv=5,\n",
    "                         scoring=\"accuracy\")\n",
    "cv_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "3d4c5e68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8446994535519124"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_acc = np.mean(cv_acc)\n",
    "cv_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "6db766d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.775     , 0.88571429, 0.85714286, 0.86111111, 0.725     ])"
      ]
     },
     "execution_count": 55,
     "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "266aff79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8207936507936507"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_precision = np.mean(cv_precision)\n",
    "cv_precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "9813766c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.93939394, 0.93939394, 0.90909091, 0.93939394, 0.87878788])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross-validated recall\n",
    "cv_recall = cross_val_score(clf,\n",
    "                         x,\n",
    "                         y,\n",
    "                         cv=5,\n",
    "                         scoring=\"recall\")\n",
    "cv_recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "44b4cfa8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9212121212121213"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_recall = np.mean(cv_recall)\n",
    "cv_recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "41c744ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.84931507, 0.91176471, 0.88235294, 0.89855072, 0.79452055])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross-validated f1-score\n",
    "cv_f1 = cross_val_score(clf,\n",
    "                         x,\n",
    "                         y,\n",
    "                         cv=5,\n",
    "                         scoring=\"f1\")\n",
    "cv_f1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "e3d8b0b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8673007976269721"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_f1 = np.mean(cv_f1)\n",
    "cv_f1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "9bda9609",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize cross-validated metrics\n",
    "cv_metrics = pd.DataFrame({\"Accuracy\": cv_acc,\n",
    "                           \"Precision\": cv_precision,\n",
    "                           \"Recall\": cv_recall,\n",
    "                           \"F1\": cv_f1},\n",
    "                          index=[0])\n",
    "\n",
    "cv_metrics.T.plot.bar(title=\"Cross-validation classification metrics\",\n",
    "                      legend=False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "01650402",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(C=0.20433597178569418, solver=&#x27;liblinear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" checked><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(C=0.20433597178569418, solver=&#x27;liblinear&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(C=0.20433597178569418, solver='liblinear')"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit an instance of LogisticRefression\n",
    "clf = LogisticRegression(C=0.20433597178569418,\n",
    "                         solver=\"liblinear\")\n",
    "\n",
    "clf.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "47ca2b9a",
   "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": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check coef_\n",
    "clf.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "0b4840c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'age': 0.0031672801993431563,\n",
       " 'sex': -0.8604465072345515,\n",
       " 'cp': 0.6606704082033799,\n",
       " 'trestbps': -0.01156993168080875,\n",
       " 'chol': -0.001663744504776871,\n",
       " 'fbs': 0.043861071652469864,\n",
       " 'restecg': 0.31275846822418324,\n",
       " 'thalach': 0.024593613737779126,\n",
       " 'exang': -0.6041308000615746,\n",
       " 'oldpeak': -0.5686280368396555,\n",
       " 'slope': 0.4505162797258308,\n",
       " 'ca': -0.6360989676086223,\n",
       " 'thal': -0.6766337263029825}"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Match coef's of features to columns\n",
    "feature_dict = dict(zip(df.columns, list(clf.coef_[0])))\n",
    "feature_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "e2ec7881",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Feature Importance'}>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize feature importance\n",
    "feature_df = pd.DataFrame(feature_dict, index=[0])\n",
    "feature_df.T.plot.bar(title=\"Feature Importance\", legend=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "0088e359",
   "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>sex</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>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>114</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "target    0   1\n",
       "sex            \n",
       "0        24  72\n",
       "1       114  93"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df[\"sex\"], df[\"target\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "0f8f59aa",
   "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>slope</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>91</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "target   0    1\n",
       "slope          \n",
       "0       12    9\n",
       "1       91   49\n",
       "2       35  107"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df[\"slope\"], df[\"target\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "22bf5317",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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