heart_disease_classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# Predicting Heart Disease Using Machine Learning\n",
    "\n",
    "This notebook will look into using various different python-based machine learning and data science libraries in an attempt to build a machine learning model capable of predicting whether someone has heart disease based on their medical attributes.\n",
    "\n",
    "## Problem Definition\n",
    "\n",
    "> Given clinical parameters about a patient, can we predict whether they have heart disease or not?\n",
    "\n",
    "## Data\n",
    "\n",
    "Original Data From: https://archive.ics.uci.edu/dataset/45/heart+disease\n",
    "Also on Kaggle From: https://www.kaggle.com/datasets/cherngs/heart-disease-cleveland-uci\n",
    "This database contains 76 attributes, but all published experiments refer to using a subset of 14 of them.\n",
    "\n",
    "## Expectations\n",
    "\n",
    "> If we reach 95% accuracy at predicting whether a patient has heart disease during the proof of concept, we'll pursue the project\n",
    "\n",
    "## Features\n",
    "\n",
    "* age - age in years\n",
    "* sex - (1= male; 0 = female)\n",
    "* ср - chest pain type\n",
    "* trestbps - resting blood pressure (in mm Hg on admission to the hospital)\n",
    "* chol - serum cholestoral in mg/dl\n",
    "* fbs - (fasting blood sugar > 120 mg/d1) (1 = true; 0 = false)\n",
    "* restecg - resting electrocardiographic results\n",
    "* thalach - maximum heart rate achieved\n",
    "* exang - exercise induced angina (1 = yes; 0 = no)\n",
    "* oldpeaksT - depression induced by exercise relative to rest\n",
    "* slope - the slope of the peak exercise ST segment\n",
    "* ca - number of major vessels (0-3) colored by flourosopy\n",
    "* thal - 3 = normal; 6 = fixed defect; 7 = reversable defect\n",
    "* target - 1 or 0 (heart disease or not)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 507,
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "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, RandomizedSearchCV, GridSearchCV\n",
    "from sklearn.metrics import confusion_matrix, classification_report, precision_score, recall_score, f1_score, \\\n",
    "    RocCurveDisplay\n",
    "\n",
    "plt.style.use(\"seaborn-v0_8-dark\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-06-24T01:26:32.824806Z",
     "start_time": "2023-06-24T01:26:32.684617Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Data Exploration"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 508,
   "outputs": [
    {
     "data": {
      "text/plain": "(303, 14)"
     },
     "execution_count": 508,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"heart-disease.csv\")\n",
    "df.shape"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-06-24T01:26:32.893313Z",
     "start_time": "2023-06-24T01:26:32.690380Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 509,
   "outputs": [
    {
     "data": {
      "text/plain": "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n0   63    1   3       145   233    1        0      150      0      2.3      0   \n1   37    1   2       130   250    0        1      187      0      3.5      0   \n2   41    0   1       130   204    0        0      172      0      1.4      2   \n3   56    1   1       120   236    0        1      178      0      0.8      2   \n4   57    0   0       120   354    0        1      163      1      0.6      2   \n\n   ca  thal  target  \n0   0     1       1  \n1   0     2       1  \n2   0     2       1  \n3   0     2       1  \n4   0     2       1  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>age</th>\n      <th>sex</th>\n      <th>cp</th>\n      <th>trestbps</th>\n      <th>chol</th>\n      <th>fbs</th>\n      <th>restecg</th>\n      <th>thalach</th>\n      <th>exang</th>\n      <th>oldpeak</th>\n      <th>slope</th>\n      <th>ca</th>\n      <th>thal</th>\n      <th>target</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>63</td>\n      <td>1</td>\n      <td>3</td>\n      <td>145</td>\n      <td>233</td>\n      <td>1</td>\n      <td>0</td>\n      <td>150</td>\n      <td>0</td>\n      <td>2.3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>37</td>\n      <td>1</td>\n      <td>2</td>\n      <td>130</td>\n      <td>250</td>\n      <td>0</td>\n      <td>1</td>\n      <td>187</td>\n      <td>0</td>\n      <td>3.5</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>41</td>\n      <td>0</td>\n      <td>1</td>\n      <td>130</td>\n      <td>204</td>\n      <td>0</td>\n      <td>0</td>\n      <td>172</td>\n      <td>0</td>\n      <td>1.4</td>\n      <td>2</td>\n      <td>0</td>\n      <td>2</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>56</td>\n      <td>1</td>\n      <td>1</td>\n      <td>120</td>\n      <td>236</td>\n      <td>0</td>\n      <td>1</td>\n      <td>178</td>\n      <td>0</td>\n      <td>0.8</td>\n      <td>2</td>\n      <td>0</td>\n      <td>2</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>57</td>\n      <td>0</td>\n      <td>0</td>\n      <td>120</td>\n      <td>354</td>\n      <td>0</td>\n      <td>1</td>\n      <td>163</td>\n      <td>1</td>\n      <td>0.6</td>\n      <td>2</td>\n      <td>0</td>\n      <td>2</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 509,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-06-24T01:26:32.896911Z",
     "start_time": "2023-06-24T01:26:32.695952Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 510,
   "outputs": [
    {
     "data": {
      "text/plain": "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n298   57    0   0       140   241    0        1      123      1      0.2   \n299   45    1   3       110   264    0        1      132      0      1.2   \n300   68    1   0       144   193    1        1      141      0      3.4   \n301   57    1   0       130   131    0        1      115      1      1.2   \n302   57    0   1       130   236    0        0      174      0      0.0   \n\n     slope  ca  thal  target  \n298      1   0     3       0  \n299      1   0     3       0  \n300      1   2     3       0  \n301      1   1     3       0  \n302      1   1     2       0  ",
      "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>298</th>\n      <td>57</td>\n      <td>0</td>\n      <td>0</td>\n      <td>140</td>\n      <td>241</td>\n      <td>0</td>\n      <td>1</td>\n      <td>123</td>\n      <td>1</td>\n      <td>0.2</td>\n      <td>1</td>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>299</th>\n      <td>45</td>\n      <td>1</td>\n      <td>3</td>\n      <td>110</td>\n      <td>264</td>\n      <td>0</td>\n      <td>1</td>\n      <td>132</td>\n      <td>0</td>\n      <td>1.2</td>\n      <td>1</td>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>300</th>\n      <td>68</td>\n      <td>1</td>\n      <td>0</td>\n      <td>144</td>\n      <td>193</td>\n      <td>1</td>\n      <td>1</td>\n      <td>141</td>\n      <td>0</td>\n      <td>3.4</td>\n      <td>1</td>\n      <td>2</td>\n      <td>3</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>301</th>\n      <td>57</td>\n      <td>1</td>\n      <td>0</td>\n      <td>130</td>\n      <td>131</td>\n      <td>0</td>\n      <td>1</td>\n      <td>115</td>\n      <td>1</td>\n      <td>1.2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>3</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>302</th>\n      <td>57</td>\n      <td>0</td>\n      <td>1</td>\n      <td>130</td>\n      <td>236</td>\n      <td>0</td>\n      <td>0</td>\n      <td>174</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 510,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-06-24T01:26:32.996038Z",
     "start_time": "2023-06-24T01:26:32.700649Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 511,
   "outputs": [
    {
     "data": {
      "text/plain": "1    165\n0    138\nName: target, dtype: int64"
     },
     "execution_count": 511,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.target.value_counts()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-06-24T01:26:32.997402Z",
     "start_time": "2023-06-24T01:26:32.703935Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 512,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"target\"].value_counts().plot.bar(color=[\"coral\", \"plum\"]);"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-06-24T01:26:33.061186Z",
     "start_time": "2023-06-24T01:26:32.708093Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 513,
   "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()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-06-24T01:26:33.062726Z",
     "start_time": "2023-06-24T01:26:32.759781Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 514,
   "outputs": [
    {
     "data": {
      "text/plain": "age         0\nsex         0\ncp          0\ntrestbps    0\nchol        0\nfbs         0\nrestecg     0\nthalach     0\nexang       0\noldpeak     0\nslope       0\nca          0\nthal        0\ntarget      0\ndtype: int64"
     },
     "execution_count": 514,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-06-24T01:26:33.062972Z",
     "start_time": "2023-06-24T01:26:32.763134Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 515,
   "outputs": [
    {
     "data": {
      "text/plain": "              age         sex          cp    trestbps        chol         fbs  \\\ncount  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \nmean    54.366337    0.683168    0.966997  131.623762  246.264026    0.148515   \nstd      9.082101    0.466011    1.032052   17.538143   51.830751    0.356198   \nmin     29.000000    0.000000    0.000000   94.000000  126.000000    0.000000   \n25%     47.500000    0.000000    0.000000  120.000000  211.000000    0.000000   \n50%     55.000000    1.000000    1.000000  130.000000  240.000000    0.000000   \n75%     61.000000    1.000000    2.000000  140.000000  274.500000    0.000000   \nmax     77.000000    1.000000    3.000000  200.000000  564.000000    1.000000   \n\n          restecg     thalach       exang     oldpeak       slope          ca  \\\ncount  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \nmean     0.528053  149.646865    0.326733    1.039604    1.399340    0.729373   \nstd      0.525860   22.905161    0.469794    1.161075    0.616226    1.022606   \nmin      0.000000   71.000000    0.000000    0.000000    0.000000    0.000000   \n25%      0.000000  133.500000    0.000000    0.000000    1.000000    0.000000   \n50%      1.000000  153.000000    0.000000    0.800000    1.000000    0.000000   \n75%      1.000000  166.000000    1.000000    1.600000    2.000000    1.000000   \nmax      2.000000  202.000000    1.000000    6.200000    2.000000    4.000000   \n\n             thal      target  \ncount  303.000000  303.000000  \nmean     2.313531    0.544554  \nstd      0.612277    0.498835  \nmin      0.000000    0.000000  \n25%      2.000000    0.000000  \n50%      2.000000    1.000000  \n75%      3.000000    1.000000  \nmax      3.000000    1.000000  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>age</th>\n      <th>sex</th>\n      <th>cp</th>\n      <th>trestbps</th>\n      <th>chol</th>\n      <th>fbs</th>\n      <th>restecg</th>\n      <th>thalach</th>\n      <th>exang</th>\n      <th>oldpeak</th>\n      <th>slope</th>\n      <th>ca</th>\n      <th>thal</th>\n      <th>target</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>303.000000</td>\n      <td>303.000000</td>\n      <td>303.000000</td>\n      <td>303.000000</td>\n      <td>303.000000</td>\n      <td>303.000000</td>\n      <td>303.000000</td>\n      <td>303.000000</td>\n      <td>303.000000</td>\n      <td>303.000000</td>\n      <td>303.000000</td>\n      <td>303.000000</td>\n      <td>303.000000</td>\n      <td>303.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>54.366337</td>\n      <td>0.683168</td>\n      <td>0.966997</td>\n      <td>131.623762</td>\n      <td>246.264026</td>\n      <td>0.148515</td>\n      <td>0.528053</td>\n      <td>149.646865</td>\n      <td>0.326733</td>\n      <td>1.039604</td>\n      <td>1.399340</td>\n      <td>0.729373</td>\n      <td>2.313531</td>\n      <td>0.544554</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>9.082101</td>\n      <td>0.466011</td>\n      <td>1.032052</td>\n      <td>17.538143</td>\n      <td>51.830751</td>\n      <td>0.356198</td>\n      <td>0.525860</td>\n      <td>22.905161</td>\n      <td>0.469794</td>\n      <td>1.161075</td>\n      <td>0.616226</td>\n      <td>1.022606</td>\n      <td>0.612277</td>\n      <td>0.498835</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>29.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>94.000000</td>\n      <td>126.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>71.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>47.500000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>120.000000</td>\n      <td>211.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>133.500000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>2.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>55.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>130.000000</td>\n      <td>240.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>153.000000</td>\n      <td>0.000000</td>\n      <td>0.800000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>2.000000</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>61.000000</td>\n      <td>1.000000</td>\n      <td>2.000000</td>\n      <td>140.000000</td>\n      <td>274.500000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>166.000000</td>\n      <td>1.000000</td>\n      <td>1.600000</td>\n      <td>2.000000</td>\n      <td>1.000000</td>\n      <td>3.000000</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>77.000000</td>\n      <td>1.000000</td>\n      <td>3.000000</td>\n      <td>200.000000</td>\n      <td>564.000000</td>\n      <td>1.000000</td>\n      <td>2.000000</td>\n      <td>202.000000</td>\n      <td>1.000000</td>\n      <td>6.200000</td>\n      <td>2.000000</td>\n      <td>4.000000</td>\n      <td>3.000000</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 515,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-06-24T01:26:33.074614Z",
     "start_time": "2023-06-24T01:26:32.768660Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "df.sex.value_counts()"
   ],
   "metadata": {
    "collapsed": false,
    "is_executing": true
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "pd.crosstab(df.target, df.sex)"
   ],
   "metadata": {
    "collapsed": false,
    "is_executing": true
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(df.target, df.sex).plot.bar(color=[\"coral\", \"plum\"])\n",
    "plt.title(\"Heart Disease Frequency For Each Sex\")\n",
    "plt.xlabel(\"0 = No Disease, 1 = Disease\")\n",
    "plt.ylabel(\"Count\")\n",
    "plt.legend([\"Female\", \"Male\"]);"
   ],
   "metadata": {
    "collapsed": false,
    "is_executing": true,
    "ExecuteTime": {
     "start_time": "2023-06-24T01:26:32.806092Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.age.plot.hist(color=\"plum\");"
   ],
   "metadata": {
    "collapsed": false,
    "is_executing": true,
    "ExecuteTime": {
     "start_time": "2023-06-24T01:26:32.884831Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "scatter = ax.scatter(df.age, df.thalach, c=df.target, cmap=\"spring\");\n",
    "ax.set(xlabel=\"Age\", ylabel=\"Max Heart Rate\", title=\"Max Heart Rate and Age with Heart Disease\")\n",
    "ax.legend(*scatter.legend_elements());"
   ],
   "metadata": {
    "collapsed": false,
    "is_executing": true,
    "ExecuteTime": {
     "start_time": "2023-06-24T01:26:32.961062Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [
    {
     "data": {
      "text/plain": "target    0   1\ncp             \n0       104  39\n1         9  41\n2        18  69\n3         7  16",
      "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>"
     },
     "execution_count": 521,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df.cp, df.target)"
   ],
   "metadata": {
    "collapsed": false,
    "is_executing": true,
    "ExecuteTime": {
     "start_time": "2023-06-24T01:26:33.062655Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(df.cp, df.target).plot.bar(color=[\"coral\", \"plum\"])\n",
    "plt.title(\"Heart Disease Frequency per Chest Pain Type\")\n",
    "plt.xlabel(\"Chest Pain Type\")\n",
    "plt.ylabel(\"Count\")\n",
    "plt.legend([\"No Heart Disease\", \"Heart Disease\"]);"
   ],
   "metadata": {
    "collapsed": false,
    "is_executing": true,
    "ExecuteTime": {
     "start_time": "2023-06-24T01:26:33.066553Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [
    {
     "data": {
      "text/plain": "               age       sex        cp  trestbps      chol       fbs  \\\nage       1.000000 -0.098447 -0.068653  0.279351  0.213678  0.121308   \nsex      -0.098447  1.000000 -0.049353 -0.056769 -0.197912  0.045032   \ncp       -0.068653 -0.049353  1.000000  0.047608 -0.076904  0.094444   \ntrestbps  0.279351 -0.056769  0.047608  1.000000  0.123174  0.177531   \nchol      0.213678 -0.197912 -0.076904  0.123174  1.000000  0.013294   \nfbs       0.121308  0.045032  0.094444  0.177531  0.013294  1.000000   \nrestecg  -0.116211 -0.058196  0.044421 -0.114103 -0.151040 -0.084189   \nthalach  -0.398522 -0.044020  0.295762 -0.046698 -0.009940 -0.008567   \nexang     0.096801  0.141664 -0.394280  0.067616  0.067023  0.025665   \noldpeak   0.210013  0.096093 -0.149230  0.193216  0.053952  0.005747   \nslope    -0.168814 -0.030711  0.119717 -0.121475 -0.004038 -0.059894   \nca        0.276326  0.118261 -0.181053  0.101389  0.070511  0.137979   \nthal      0.068001  0.210041 -0.161736  0.062210  0.098803 -0.032019   \ntarget   -0.225439 -0.280937  0.433798 -0.144931 -0.085239 -0.028046   \n\n           restecg   thalach     exang   oldpeak     slope        ca  \\\nage      -0.116211 -0.398522  0.096801  0.210013 -0.168814  0.276326   \nsex      -0.058196 -0.044020  0.141664  0.096093 -0.030711  0.118261   \ncp        0.044421  0.295762 -0.394280 -0.149230  0.119717 -0.181053   \ntrestbps -0.114103 -0.046698  0.067616  0.193216 -0.121475  0.101389   \nchol     -0.151040 -0.009940  0.067023  0.053952 -0.004038  0.070511   \nfbs      -0.084189 -0.008567  0.025665  0.005747 -0.059894  0.137979   \nrestecg   1.000000  0.044123 -0.070733 -0.058770  0.093045 -0.072042   \nthalach   0.044123  1.000000 -0.378812 -0.344187  0.386784 -0.213177   \nexang    -0.070733 -0.378812  1.000000  0.288223 -0.257748  0.115739   \noldpeak  -0.058770 -0.344187  0.288223  1.000000 -0.577537  0.222682   \nslope     0.093045  0.386784 -0.257748 -0.577537  1.000000 -0.080155   \nca       -0.072042 -0.213177  0.115739  0.222682 -0.080155  1.000000   \nthal     -0.011981 -0.096439  0.206754  0.210244 -0.104764  0.151832   \ntarget    0.137230  0.421741 -0.436757 -0.430696  0.345877 -0.391724   \n\n              thal    target  \nage       0.068001 -0.225439  \nsex       0.210041 -0.280937  \ncp       -0.161736  0.433798  \ntrestbps  0.062210 -0.144931  \nchol      0.098803 -0.085239  \nfbs      -0.032019 -0.028046  \nrestecg  -0.011981  0.137230  \nthalach  -0.096439  0.421741  \nexang     0.206754 -0.436757  \noldpeak   0.210244 -0.430696  \nslope    -0.104764  0.345877  \nca        0.151832 -0.391724  \nthal      1.000000 -0.344029  \ntarget   -0.344029  1.000000  ",
      "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>"
     },
     "execution_count": 523,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlation_matrix = df.corr()\n",
    "correlation_matrix"
   ],
   "metadata": {
    "collapsed": false,
    "is_executing": true,
    "ExecuteTime": {
     "start_time": "2023-06-24T01:26:33.146217Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1300x1100 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(13, 11))\n",
    "ax = sns.heatmap(correlation_matrix, annot=True, linewidths=0.5, fmt=\".2f\", cmap=\"cool\")"
   ],
   "metadata": {
    "collapsed": false,
    "is_executing": true,
    "ExecuteTime": {
     "start_time": "2023-06-24T01:26:33.154435Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Modelling"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [
    {
     "data": {
      "text/plain": "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n0   63    1   3       145   233    1        0      150      0      2.3      0   \n1   37    1   2       130   250    0        1      187      0      3.5      0   \n2   41    0   1       130   204    0        0      172      0      1.4      2   \n3   56    1   1       120   236    0        1      178      0      0.8      2   \n4   57    0   0       120   354    0        1      163      1      0.6      2   \n\n   ca  thal  target  \n0   0     1       1  \n1   0     2       1  \n2   0     2       1  \n3   0     2       1  \n4   0     2       1  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>age</th>\n      <th>sex</th>\n      <th>cp</th>\n      <th>trestbps</th>\n      <th>chol</th>\n      <th>fbs</th>\n      <th>restecg</th>\n      <th>thalach</th>\n      <th>exang</th>\n      <th>oldpeak</th>\n      <th>slope</th>\n      <th>ca</th>\n      <th>thal</th>\n      <th>target</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>63</td>\n      <td>1</td>\n      <td>3</td>\n      <td>145</td>\n      <td>233</td>\n      <td>1</td>\n      <td>0</td>\n      <td>150</td>\n      <td>0</td>\n      <td>2.3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>37</td>\n      <td>1</td>\n      <td>2</td>\n      <td>130</td>\n      <td>250</td>\n      <td>0</td>\n      <td>1</td>\n      <td>187</td>\n      <td>0</td>\n      <td>3.5</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>41</td>\n      <td>0</td>\n      <td>1</td>\n      <td>130</td>\n      <td>204</td>\n      <td>0</td>\n      <td>0</td>\n      <td>172</td>\n      <td>0</td>\n      <td>1.4</td>\n      <td>2</td>\n      <td>0</td>\n      <td>2</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>56</td>\n      <td>1</td>\n      <td>1</td>\n      <td>120</td>\n      <td>236</td>\n      <td>0</td>\n      <td>1</td>\n      <td>178</td>\n      <td>0</td>\n      <td>0.8</td>\n      <td>2</td>\n      <td>0</td>\n      <td>2</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>57</td>\n      <td>0</td>\n      <td>0</td>\n      <td>120</td>\n      <td>354</td>\n      <td>0</td>\n      <td>1</td>\n      <td>163</td>\n      <td>1</td>\n      <td>0.6</td>\n      <td>2</td>\n      <td>0</td>\n      <td>2</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 525,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ],
   "metadata": {
    "collapsed": false,
    "is_executing": true,
    "ExecuteTime": {
     "start_time": "2023-06-24T01:26:33.416103Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "# Splitting data\n",
    "\n",
    "X = df.drop(\"target\", axis=1)\n",
    "y = df[\"target\"]\n",
    "\n",
    "np.random.seed(8)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
   ],
   "metadata": {
    "collapsed": false,
    "is_executing": true,
    "ExecuteTime": {
     "start_time": "2023-06-24T01:26:33.419749Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [
    {
     "data": {
      "text/plain": "((242, 13), (61, 13), (242,), (61,))"
     },
     "execution_count": 527,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
   ],
   "metadata": {
    "collapsed": false,
    "is_executing": true,
    "ExecuteTime": {
     "start_time": "2023-06-24T01:26:33.422140Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "**Testing three different machine learning algorithms:**\n",
    "\n",
    "1. Logistic Regression\n",
    "2. K-Nearest Neighbours Classifier\n",
    "3. Random Forest Classifier"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "models = {\"Logistic Regression\": LogisticRegression(), \"KNN\": KNeighborsClassifier(),\n",
    "          \"Random Forest Classifier\": RandomForestClassifier()}\n",
    "\n",
    "\n",
    "def train_and_score(models, X_train, y_train, X_test, y_test):\n",
    "    \"\"\"\n",
    "    models - Dictionary of models\n",
    "    X_train - training feature set\n",
    "    y_train - training target set\n",
    "    X_test - test feature set\n",
    "    y_test - target test set\n",
    "    \"\"\"\n",
    "    np.random.seed(8)\n",
    "    models_scores = {}\n",
    "\n",
    "    for name, model in models.items():\n",
    "        model.fit(X_train, y_train)\n",
    "        models_scores[name] = model.score(X_test, y_test)\n",
    "\n",
    "    return models_scores"
   ],
   "metadata": {
    "collapsed": false,
    "is_executing": true,
    "ExecuteTime": {
     "start_time": "2023-06-24T01:26:33.425845Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "**Baseline Model Testing & Comparison**"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yogeshprabhu/Documents/Computer Science/ztm ml/heart_disease_project/env/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/plain": "{'Logistic Regression': 0.8688524590163934,\n 'KNN': 0.6885245901639344,\n 'Random Forest Classifier': 0.8524590163934426}"
     },
     "execution_count": 529,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_scores = train_and_score(models, X_train, y_train, X_test, y_test)\n",
    "\n",
    "model_scores"
   ],
   "metadata": {
    "collapsed": false,
    "is_executing": true,
    "ExecuteTime": {
     "start_time": "2023-06-24T01:26:33.429157Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_compare = pd.DataFrame(model_scores, index=[\"accuracy\"])\n",
    "model_compare.T.plot.bar(color=[\"plum\"]);"
   ],
   "metadata": {
    "collapsed": false,
    "is_executing": true,
    "ExecuteTime": {
     "start_time": "2023-06-24T01:26:33.492321Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Experimenting & Evaluating Models\n",
    "\n",
    "* Hyperparameter Tuning\n",
    "* ROC Curve\n",
    "* Area under the curve (AUC)\n",
    "* Confusion Matrix\n",
    "* Cross-validation\n",
    "* Precision\n",
    "* Recall\n",
    "* F1 Score\n",
    "* Classification Report\n",
    "* Feature Importance"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yogeshprabhu/Documents/Computer Science/ztm ml/heart_disease_project/env/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/plain": "        KNN  Logistic Regression  Random Forest Classifier\n0  0.688525             0.868852                  0.852459",
      "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>KNN</th>\n      <th>Logistic Regression</th>\n      <th>Random Forest Classifier</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.688525</td>\n      <td>0.868852</td>\n      <td>0.852459</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 531,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(8)\n",
    "default_scores = {\"KNN\": KNeighborsClassifier(), \"Logistic Regression\": LogisticRegression(),\n",
    "                  \"Random Forest Classifier\": RandomForestClassifier()}\n",
    "\n",
    "for idx, i in enumerate(default_scores.values()):\n",
    "    i.fit(X_train, y_train)\n",
    "    default_scores[list(default_scores.keys())[idx]] = i.score(X_test, y_test)\n",
    "\n",
    "plt.bar(0, list(default_scores.values())[0], tick_label=list(default_scores.keys())[0], color=\"plum\")\n",
    "plt.bar(1, list(default_scores.values())[1], tick_label=list(default_scores.keys())[1], color=\"coral\")\n",
    "plt.bar(2, list(default_scores.values())[2], tick_label=list(default_scores.keys())[2], color=\"skyblue\")\n",
    "plt.title(\"Default Model Scores\")\n",
    "plt.xticks(range(0, 3), list(default_scores.keys()));\n",
    "\n",
    "pd.DataFrame(default_scores, index=[0])"
   ],
   "metadata": {
    "collapsed": false,
    "is_executing": true,
    "ExecuteTime": {
     "start_time": "2023-06-24T01:26:33.552759Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "**Hyperparameter Tuning**"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "training_scores = []\n",
    "test_scores = []\n",
    "\n",
    "n_values = range(1, 21)\n",
    "np.random.seed(8)\n",
    "\n",
    "knn_model = KNeighborsClassifier()\n",
    "\n",
    "for n in n_values:\n",
    "    knn_model.set_params(n_neighbors=n)\n",
    "    knn_model.fit(X_train, y_train)\n",
    "\n",
    "    training_scores.append(knn_model.score(X_train, y_train))\n",
    "    test_scores.append(knn_model.score(X_test, y_test))"
   ],
   "metadata": {
    "collapsed": false,
    "is_executing": true,
    "ExecuteTime": {
     "start_time": "2023-06-24T01:26:33.674425Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max Test Score: 68.85245901639344%\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(n_values, training_scores, label=\"Training Scores\", color=\"plum\")\n",
    "plt.plot(n_values, test_scores, label=\"Test Scores\", color=\"coral\")\n",
    "plt.xlabel(\"Number of neighbors\")\n",
    "plt.ylabel(\"Score\")\n",
    "plt.legend();\n",
    "print(f\"Max Test Score: {max(test_scores) * 100}%\")"
   ],
   "metadata": {
    "collapsed": false,
    "is_executing": true,
    "ExecuteTime": {
     "start_time": "2023-06-24T01:26:33.768156Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 6 candidates, totalling 30 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": "RandomizedSearchCV(cv=5, estimator=KNeighborsClassifier(), n_iter=6, n_jobs=-1,\n                   param_distributions={'n_neighbors': [14, 15, 16],\n                                        'weights': ['uniform', 'distance']},\n                   verbose=1)",
      "text/html": "<style>#sk-container-id-30 {color: black;background-color: white;}#sk-container-id-30 pre{padding: 0;}#sk-container-id-30 div.sk-toggleable {background-color: white;}#sk-container-id-30 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-30 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-30 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-30 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-30 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-30 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-30 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-30 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-30 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-30 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-30 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-30 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-30 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-30 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-30 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-30 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-30 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-30 div.sk-item {position: relative;z-index: 1;}#sk-container-id-30 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-30 div.sk-item::before, #sk-container-id-30 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-30 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-30 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-30 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-30 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-30 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-30 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-30 div.sk-label-container {text-align: center;}#sk-container-id-30 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-30 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-30\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=5, estimator=KNeighborsClassifier(), n_iter=6, n_jobs=-1,\n                   param_distributions={&#x27;n_neighbors&#x27;: [14, 15, 16],\n                                        &#x27;weights&#x27;: [&#x27;uniform&#x27;, &#x27;distance&#x27;]},\n                   verbose=1)</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-88\" type=\"checkbox\" ><label for=\"sk-estimator-id-88\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(cv=5, estimator=KNeighborsClassifier(), n_iter=6, n_jobs=-1,\n                   param_distributions={&#x27;n_neighbors&#x27;: [14, 15, 16],\n                                        &#x27;weights&#x27;: [&#x27;uniform&#x27;, &#x27;distance&#x27;]},\n                   verbose=1)</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-89\" type=\"checkbox\" ><label for=\"sk-estimator-id-89\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</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-90\" type=\"checkbox\" ><label for=\"sk-estimator-id-90\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
     },
     "execution_count": 534,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# KNN Hyperparameter Tuning with RandomizedSearchCV\n",
    "\n",
    "param_dict = {\"n_neighbors\": [14, 15, 16], \"weights\": [\"uniform\", \"distance\"]}\n",
    "np.random.seed(8)\n",
    "knn_model = KNeighborsClassifier()\n",
    "knn_tuned = RandomizedSearchCV(estimator=knn_model, param_distributions=param_dict, n_iter=6, n_jobs=-1, cv=5,\n",
    "                               verbose=1)\n",
    "knn_tuned.fit(X_train, y_train)"
   ],
   "metadata": {
    "collapsed": false,
    "is_executing": true,
    "ExecuteTime": {
     "start_time": "2023-06-24T01:26:33.850532Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [
    {
     "data": {
      "text/plain": "{'weights': 'uniform', 'n_neighbors': 15}"
     },
     "execution_count": 535,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_tuned.best_params_"
   ],
   "metadata": {
    "collapsed": false,
    "is_executing": true,
    "ExecuteTime": {
     "start_time": "2023-06-24T01:26:33.881995Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [
    {
     "data": {
      "text/plain": "0.639344262295082"
     },
     "execution_count": 537,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_hpt_score = knn_tuned.score(X_test, y_test)\n",
    "knn_hpt_score"
   ],
   "metadata": {
    "collapsed": false,
    "is_executing": true,
    "ExecuteTime": {
     "start_time": "2023-06-24T01:26:34.018202Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": "RandomizedSearchCV(cv=5, estimator=LogisticRegression(), n_iter=20, n_jobs=-1,\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                                        'dual': [False],\n                                        'max_iter': array([100, 150, 200, 250, 300, 350]),\n                                        'penalty': ['l2'],\n                                        'solver': ['liblinear',\n                                                   'newton-cholesky']},\n                   verbose=1)",
      "text/html": "<style>#sk-container-id-31 {color: black;background-color: white;}#sk-container-id-31 pre{padding: 0;}#sk-container-id-31 div.sk-toggleable {background-color: white;}#sk-container-id-31 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-31 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-31 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-31 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-31 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-31 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-31 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-31 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-31 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-31 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-31 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-31 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-31 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-31 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-31 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-31 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-31 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-31 div.sk-item {position: relative;z-index: 1;}#sk-container-id-31 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-31 div.sk-item::before, #sk-container-id-31 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-31 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-31 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-31 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-31 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-31 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-31 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-31 div.sk-label-container {text-align: center;}#sk-container-id-31 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-31 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-31\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=5, estimator=LogisticRegression(), n_iter=20, n_jobs=-1,\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;dual&#x27;: [False],\n                                        &#x27;max_iter&#x27;: array([100, 150, 200, 250, 300, 350]),\n                                        &#x27;penalty&#x27;: [&#x27;l2&#x27;],\n                                        &#x27;solver&#x27;: [&#x27;liblinear&#x27;,\n                                                   &#x27;newton-cholesky&#x27;]},\n                   verbose=1)</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-91\" type=\"checkbox\" ><label for=\"sk-estimator-id-91\" 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_jobs=-1,\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;dual&#x27;: [False],\n                                        &#x27;max_iter&#x27;: array([100, 150, 200, 250, 300, 350]),\n                                        &#x27;penalty&#x27;: [&#x27;l2&#x27;],\n                                        &#x27;solver&#x27;: [&#x27;liblinear&#x27;,\n                                                   &#x27;newton-cholesky&#x27;]},\n                   verbose=1)</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-92\" type=\"checkbox\" ><label for=\"sk-estimator-id-92\" 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-93\" type=\"checkbox\" ><label for=\"sk-estimator-id-93\" 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>"
     },
     "execution_count": 539,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Logistic Regression Hyperparameter Tuning with RandomizedSearchCV\n",
    "\n",
    "param_dict = {\"penalty\": [\"l2\"], \"dual\": [False], \"solver\": [\"liblinear\", \"newton-cholesky\"],\n",
    "              \"C\": np.logspace(-4, 4, 20), \"max_iter\": np.arange(100, 400, 50)}\n",
    "np.random.seed(8)\n",
    "lr_model = LogisticRegression()\n",
    "lr_tuned = RandomizedSearchCV(estimator=lr_model, param_distributions=param_dict, n_iter=20, n_jobs=-1, cv=5, verbose=1)\n",
    "\n",
    "lr_tuned.fit(X_train, y_train)"
   ],
   "metadata": {
    "collapsed": false,
    "is_executing": true,
    "ExecuteTime": {
     "start_time": "2023-06-24T01:26:34.093779Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [
    {
     "data": {
      "text/plain": "{'solver': 'liblinear',\n 'penalty': 'l2',\n 'max_iter': 150,\n 'dual': False,\n 'C': 1438.44988828766}"
     },
     "execution_count": 541,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_tuned.best_params_"
   ],
   "metadata": {
    "collapsed": false,
    "is_executing": true,
    "ExecuteTime": {
     "start_time": "2023-06-24T01:26:34.182032Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [
    {
     "data": {
      "text/plain": "0.8524590163934426"
     },
     "execution_count": 543,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_hpt_score = lr_tuned.score(X_test, y_test)\n",
    "lr_hpt_score"
   ],
   "metadata": {
    "collapsed": false,
    "is_executing": true,
    "ExecuteTime": {
     "start_time": "2023-06-24T01:26:34.280657Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": "RandomizedSearchCV(cv=5, estimator=RandomForestClassifier(), n_iter=20,\n                   n_jobs=-1,\n                   param_distributions={'max_depth': array([10, 20, 30, 40, 50, 60, 70, 80, 90]),\n                                        'max_features': ['sqrt'],\n                                        'min_samples_leaf': array([1, 3]),\n                                        'min_samples_split': [2, 5, 10],\n                                        'n_estimators': array([ 200,  400,  600,  800, 1000, 1200, 1400, 1600, 1800])},\n                   verbose=1)",
      "text/html": "<style>#sk-container-id-32 {color: black;background-color: white;}#sk-container-id-32 pre{padding: 0;}#sk-container-id-32 div.sk-toggleable {background-color: white;}#sk-container-id-32 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-32 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-32 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-32 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-32 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-32 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-32 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-32 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-32 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-32 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-32 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-32 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-32 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-32 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-32 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-32 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-32 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-32 div.sk-item {position: relative;z-index: 1;}#sk-container-id-32 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-32 div.sk-item::before, #sk-container-id-32 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-32 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-32 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-32 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-32 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-32 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-32 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-32 div.sk-label-container {text-align: center;}#sk-container-id-32 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-32 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-32\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=5, estimator=RandomForestClassifier(), n_iter=20,\n                   n_jobs=-1,\n                   param_distributions={&#x27;max_depth&#x27;: array([10, 20, 30, 40, 50, 60, 70, 80, 90]),\n                                        &#x27;max_features&#x27;: [&#x27;sqrt&#x27;],\n                                        &#x27;min_samples_leaf&#x27;: array([1, 3]),\n                                        &#x27;min_samples_split&#x27;: [2, 5, 10],\n                                        &#x27;n_estimators&#x27;: array([ 200,  400,  600,  800, 1000, 1200, 1400, 1600, 1800])},\n                   verbose=1)</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-94\" type=\"checkbox\" ><label for=\"sk-estimator-id-94\" 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                   n_jobs=-1,\n                   param_distributions={&#x27;max_depth&#x27;: array([10, 20, 30, 40, 50, 60, 70, 80, 90]),\n                                        &#x27;max_features&#x27;: [&#x27;sqrt&#x27;],\n                                        &#x27;min_samples_leaf&#x27;: array([1, 3]),\n                                        &#x27;min_samples_split&#x27;: [2, 5, 10],\n                                        &#x27;n_estimators&#x27;: array([ 200,  400,  600,  800, 1000, 1200, 1400, 1600, 1800])},\n                   verbose=1)</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-95\" type=\"checkbox\" ><label for=\"sk-estimator-id-95\" 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-96\" type=\"checkbox\" ><label for=\"sk-estimator-id-96\" 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>"
     },
     "execution_count": 544,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Random Forest Classifier Hyperparameter Tuning with RandomizedSearchCV\n",
    "param_dict = {\"n_estimators\": np.arange(200, 2000, 200), \"max_depth\": np.arange(10, 100, 10), \"max_features\": [\"sqrt\"],\n",
    "              \"min_samples_leaf\": np.arange(1, 4, 2), \"min_samples_split\": [2, 5, 10]}\n",
    "\n",
    "np.random.seed(8)\n",
    "rf_clf = RandomForestClassifier()\n",
    "rf_clf_tuned = RandomizedSearchCV(estimator=rf_clf, param_distributions=param_dict, n_iter=20, n_jobs=-1, cv=5,\n",
    "                                  verbose=1)\n",
    "\n",
    "rf_clf_tuned.fit(X_train, y_train)"
   ],
   "metadata": {
    "collapsed": false,
    "is_executing": true,
    "ExecuteTime": {
     "start_time": "2023-06-24T01:26:34.284454Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [
    {
     "data": {
      "text/plain": "{'n_estimators': 200,\n 'min_samples_split': 2,\n 'min_samples_leaf': 3,\n 'max_features': 'sqrt',\n 'max_depth': 10}"
     },
     "execution_count": 545,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_clf_tuned.best_params_"
   ],
   "metadata": {
    "collapsed": false,
    "is_executing": true,
    "ExecuteTime": {
     "start_time": "2023-06-24T01:26:41.586889Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [
    {
     "data": {
      "text/plain": "0.8524590163934426"
     },
     "execution_count": 546,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_hpt_score = rf_clf_tuned.score(X_test, y_test)\n",
    "rf_hpt_score"
   ],
   "metadata": {
    "collapsed": false,
    "is_executing": true,
    "ExecuteTime": {
     "start_time": "2023-06-24T01:26:41.590775Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [
    {
     "data": {
      "text/plain": "        KNN  Logistic Regression  Random Forest Classifier\n0  0.639344             0.852459                  0.852459",
      "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>KNN</th>\n      <th>Logistic Regression</th>\n      <th>Random Forest Classifier</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.639344</td>\n      <td>0.852459</td>\n      <td>0.852459</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 547,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tuned_scores = {\"KNN\": knn_hpt_score, \"Logistic Regression\": lr_hpt_score, \"Random Forest Classifier\": rf_hpt_score}\n",
    "plt.bar(0, list(tuned_scores.values())[0], tick_label=list(tuned_scores.keys())[0], color=\"plum\")\n",
    "plt.bar(1, list(tuned_scores.values())[1], tick_label=list(tuned_scores.keys())[1], color=\"coral\")\n",
    "plt.bar(2, list(tuned_scores.values())[2], tick_label=list(tuned_scores.keys())[2], color=\"skyblue\")\n",
    "plt.title(\"Hyperparameter Tuned Models with RandomizedSearchCV Scores\")\n",
    "plt.xticks(range(0, 3), list(tuned_scores.keys()));\n",
    "\n",
    "pd.DataFrame(tuned_scores, index=[0])"
   ],
   "metadata": {
    "collapsed": false,
    "is_executing": true,
    "ExecuteTime": {
     "start_time": "2023-06-24T01:26:41.602124Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": "GridSearchCV(cv=5, estimator=LogisticRegression(), n_jobs=-1,\n             param_grid={'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                         'dual': [False], 'penalty': ['l2'],\n                         'solver': ['liblinear']},\n             verbose=1)",
      "text/html": "<style>#sk-container-id-33 {color: black;background-color: white;}#sk-container-id-33 pre{padding: 0;}#sk-container-id-33 div.sk-toggleable {background-color: white;}#sk-container-id-33 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-33 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-33 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-33 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-33 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-33 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-33 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-33 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-33 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-33 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-33 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-33 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-33 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-33 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-33 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-33 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-33 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-33 div.sk-item {position: relative;z-index: 1;}#sk-container-id-33 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-33 div.sk-item::before, #sk-container-id-33 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-33 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-33 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-33 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-33 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-33 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-33 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-33 div.sk-label-container {text-align: center;}#sk-container-id-33 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-33 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-33\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=LogisticRegression(), n_jobs=-1,\n             param_grid={&#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;dual&#x27;: [False], &#x27;penalty&#x27;: [&#x27;l2&#x27;],\n                         &#x27;solver&#x27;: [&#x27;liblinear&#x27;]},\n             verbose=1)</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-97\" type=\"checkbox\" ><label for=\"sk-estimator-id-97\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=LogisticRegression(), n_jobs=-1,\n             param_grid={&#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;dual&#x27;: [False], &#x27;penalty&#x27;: [&#x27;l2&#x27;],\n                         &#x27;solver&#x27;: [&#x27;liblinear&#x27;]},\n             verbose=1)</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-98\" type=\"checkbox\" ><label for=\"sk-estimator-id-98\" 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-99\" type=\"checkbox\" ><label for=\"sk-estimator-id-99\" 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>"
     },
     "execution_count": 548,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Logistic Regression Hyperparameter Tuning with GridSearchCV\n",
    "\n",
    "param_dict = {\"penalty\": [\"l2\"], \"dual\": [False], \"solver\": [\"liblinear\"], \"C\": np.logspace(-4, 4, 20)}\n",
    "np.random.seed(8)\n",
    "lr_model = LogisticRegression()\n",
    "lr_gs_tuned = GridSearchCV(estimator=lr_model, param_grid=param_dict, n_jobs=-1, cv=5, verbose=1)\n",
    "\n",
    "lr_gs_tuned.fit(X_train, y_train)"
   ],
   "metadata": {
    "collapsed": false,
    "is_executing": true,
    "ExecuteTime": {
     "start_time": "2023-06-24T01:26:41.672077Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [
    {
     "data": {
      "text/plain": "{'C': 1.623776739188721, 'dual': False, 'penalty': 'l2', 'solver': 'liblinear'}"
     },
     "execution_count": 549,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_gs_tuned.best_params_"
   ],
   "metadata": {
    "collapsed": false,
    "is_executing": true,
    "ExecuteTime": {
     "start_time": "2023-06-24T01:26:41.739306Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [
    {
     "data": {
      "text/plain": "0.8688524590163934"
     },
     "execution_count": 550,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_hpt_score = lr_gs_tuned.score(X_test, y_test)\n",
    "lr_hpt_score"
   ],
   "metadata": {
    "collapsed": false,
    "is_executing": true,
    "ExecuteTime": {
     "start_time": "2023-06-24T01:26:41.742997Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 72 candidates, totalling 360 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": "GridSearchCV(cv=5, estimator=RandomForestClassifier(), n_jobs=-1,\n             param_grid={'max_depth': array([ 80,  90, 100]),\n                         'max_features': ['sqrt'],\n                         'min_samples_leaf': [3, 4, 5, 6],\n                         'min_samples_split': [10, 15, 20],\n                         'n_estimators': [50, 100]},\n             verbose=1)",
      "text/html": "<style>#sk-container-id-34 {color: black;background-color: white;}#sk-container-id-34 pre{padding: 0;}#sk-container-id-34 div.sk-toggleable {background-color: white;}#sk-container-id-34 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-34 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-34 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-34 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-34 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-34 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-34 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-34 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-34 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-34 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-34 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-34 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-34 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-34 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-34 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-34 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-34 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-34 div.sk-item {position: relative;z-index: 1;}#sk-container-id-34 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-34 div.sk-item::before, #sk-container-id-34 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-34 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-34 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-34 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-34 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-34 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-34 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-34 div.sk-label-container {text-align: center;}#sk-container-id-34 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-34 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-34\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=RandomForestClassifier(), n_jobs=-1,\n             param_grid={&#x27;max_depth&#x27;: array([ 80,  90, 100]),\n                         &#x27;max_features&#x27;: [&#x27;sqrt&#x27;],\n                         &#x27;min_samples_leaf&#x27;: [3, 4, 5, 6],\n                         &#x27;min_samples_split&#x27;: [10, 15, 20],\n                         &#x27;n_estimators&#x27;: [50, 100]},\n             verbose=1)</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-100\" type=\"checkbox\" ><label for=\"sk-estimator-id-100\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=RandomForestClassifier(), n_jobs=-1,\n             param_grid={&#x27;max_depth&#x27;: array([ 80,  90, 100]),\n                         &#x27;max_features&#x27;: [&#x27;sqrt&#x27;],\n                         &#x27;min_samples_leaf&#x27;: [3, 4, 5, 6],\n                         &#x27;min_samples_split&#x27;: [10, 15, 20],\n                         &#x27;n_estimators&#x27;: [50, 100]},\n             verbose=1)</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-101\" type=\"checkbox\" ><label for=\"sk-estimator-id-101\" 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-102\" type=\"checkbox\" ><label for=\"sk-estimator-id-102\" 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>"
     },
     "execution_count": 551,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Random Forest Classifier Hyperparameter Tuning with GridSearchCV\n",
    "param_dict = {\"n_estimators\": [50, 100], \"max_depth\": np.arange(80, 110, 10), \"max_features\": [\"sqrt\"],\n",
    "              \"min_samples_leaf\": [3, 4, 5, 6], \"min_samples_split\": [10, 15, 20]}\n",
    "\n",
    "np.random.seed(8)\n",
    "rf_clf = RandomForestClassifier()\n",
    "rf_gs_tuned = GridSearchCV(estimator=rf_clf, param_grid=param_dict, n_jobs=-1, cv=5, verbose=1)\n",
    "\n",
    "rf_gs_tuned.fit(X_train, y_train)"
   ],
   "metadata": {
    "collapsed": false,
    "is_executing": true,
    "ExecuteTime": {
     "start_time": "2023-06-24T01:26:41.748697Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [
    {
     "data": {
      "text/plain": "{'max_depth': 80,\n 'max_features': 'sqrt',\n 'min_samples_leaf': 3,\n 'min_samples_split': 20,\n 'n_estimators': 50}"
     },
     "execution_count": 552,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_gs_tuned.best_params_"
   ],
   "metadata": {
    "collapsed": false,
    "is_executing": true,
    "ExecuteTime": {
     "start_time": "2023-06-24T01:26:44.078428Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [
    {
     "data": {
      "text/plain": "0.8852459016393442"
     },
     "execution_count": 553,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_hpt_score = rf_gs_tuned.score(X_test, y_test)\n",
    "rf_hpt_score"
   ],
   "metadata": {
    "collapsed": false,
    "is_executing": true,
    "ExecuteTime": {
     "start_time": "2023-06-24T01:26:44.081695Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [
    {
     "data": {
      "text/plain": "   Logistic Regression  Random Forest Classifier\n0             0.868852                  0.885246",
      "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>Logistic Regression</th>\n      <th>Random Forest Classifier</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.868852</td>\n      <td>0.885246</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 554,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tuned_scores = {\"Logistic Regression\": lr_hpt_score, \"Random Forest Classifier\": rf_hpt_score}\n",
    "plt.bar(0, list(tuned_scores.values())[0], tick_label=list(tuned_scores.keys())[0], color=\"plum\")\n",
    "plt.bar(1, list(tuned_scores.values())[1], tick_label=list(tuned_scores.keys())[1], color=\"coral\")\n",
    "plt.title(\"Hyperparameter Tuned Models with GridSearchCV Scores\")\n",
    "plt.xticks(range(0, 2), list(tuned_scores.keys()));\n",
    "\n",
    "pd.DataFrame(tuned_scores, index=[0])"
   ],
   "metadata": {
    "collapsed": false,
    "is_executing": true,
    "ExecuteTime": {
     "start_time": "2023-06-24T01:26:44.087721Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "**ROC Curve & AUC**"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(8)\n",
    "y_predict = rf_gs_tuned.predict(X_test)\n",
    "\n",
    "RocCurveDisplay.from_estimator(rf_gs_tuned, X_test, y_test, color=\"plum\")\n",
    "plt.title(\"ROC Curve for GSCV Tuned Random Forest Classifier\");"
   ],
   "metadata": {
    "collapsed": false,
    "is_executing": true,
    "ExecuteTime": {
     "start_time": "2023-06-24T01:26:44.177648Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "**Confusion Matrix**"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_confusion_matrix(y_predict, y_test, model_name):\n",
    "    \"\"\"\n",
    "    Plots Confusion Matrix Using arrays of predicted y values and true y values\n",
    "    \"\"\"\n",
    "\n",
    "    fig, ax = plt.subplots()\n",
    "    ax = sns.heatmap(confusion_matrix(y_test, y_predict), annot=True, cmap=\"cool\")\n",
    "    plt.xlabel(\"Predicted Value\")\n",
    "    plt.ylabel(\"True Value\")\n",
    "    plt.title(model_name)\n",
    "\n",
    "\n",
    "plot_confusion_matrix(y_predict, y_test, \"GSCV Tuned Random Forest Classifier\")"
   ],
   "metadata": {
    "collapsed": false,
    "is_executing": true,
    "ExecuteTime": {
     "start_time": "2023-06-24T01:26:44.260825Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "**Classification**"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.78      0.95      0.86        22\n",
      "           1       0.97      0.85      0.90        39\n",
      "\n",
      "    accuracy                           0.89        61\n",
      "   macro avg       0.87      0.90      0.88        61\n",
      "weighted avg       0.90      0.89      0.89        61\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_predict))"
   ],
   "metadata": {
    "collapsed": false,
    "is_executing": true,
    "ExecuteTime": {
     "start_time": "2023-06-24T01:26:44.327268Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [
    {
     "data": {
      "text/plain": "{'max_depth': 80,\n 'max_features': 'sqrt',\n 'min_samples_leaf': 3,\n 'min_samples_split': 20,\n 'n_estimators': 50}"
     },
     "execution_count": 558,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_gs_tuned.best_params_"
   ],
   "metadata": {
    "collapsed": false,
    "is_executing": true,
    "ExecuteTime": {
     "start_time": "2023-06-24T01:26:44.331644Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "reset_rf_model = RandomForestClassifier(max_depth=90, max_features=\"sqrt\", min_samples_leaf=6, min_samples_split=20,\n",
    "                                        n_estimators=50)"
   ],
   "metadata": {
    "collapsed": false,
    "is_executing": true,
    "ExecuteTime": {
     "start_time": "2023-06-24T01:26:44.334564Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "**Cross Validated Accuracy**"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "cv_accuracy = cross_val_score(reset_rf_model, X, y, cv=5, scoring=\"accuracy\")\n",
    "np.mean(cv_accuracy)\n",
    "cv_accuracy = np.mean(cv_accuracy)"
   ],
   "metadata": {
    "collapsed": false,
    "is_executing": true,
    "ExecuteTime": {
     "start_time": "2023-06-24T01:26:44.337734Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "**Cross Validated Precision**"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "cv_precision = cross_val_score(reset_rf_model, X, y, cv=5, scoring=\"precision\")\n",
    "np.mean(cv_precision)\n",
    "cv_precision = np.mean(cv_precision)"
   ],
   "metadata": {
    "collapsed": false,
    "is_executing": true,
    "ExecuteTime": {
     "start_time": "2023-06-24T01:26:44.467338Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "**Cross Validated Recall**"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "cv_recall = cross_val_score(reset_rf_model, X, y, cv=5, scoring=\"recall\")\n",
    "np.mean(cv_recall)\n",
    "cv_recall = np.mean(cv_recall)"
   ],
   "metadata": {
    "collapsed": false,
    "is_executing": true,
    "ExecuteTime": {
     "start_time": "2023-06-24T01:26:44.598045Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "**Cross Validated F1-Score**"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 536,
   "outputs": [],
   "source": [
    "cv_f1 = cross_val_score(reset_rf_model, X, y, cv=5, scoring=\"f1\")\n",
    "np.mean(cv_f1)\n",
    "cv_f1 = np.mean(cv_f1)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-06-24T01:26:34.017821Z",
     "start_time": "2023-06-24T01:26:33.885037Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "**Cross Validated Metrics**"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 538,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cv_metrics = pd.DataFrame({\"Accuracy\": cv_accuracy, \"Precision\": cv_precision, \"Recall\": cv_recall, \"F1-Score\": cv_f1},\n",
    "                          index=[0])\n",
    "\n",
    "cv_metrics.T.plot.barh(title=\"Cross Validated Metrics\", legend=False, color=\"plum\");"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-06-24T01:26:34.092405Z",
     "start_time": "2023-06-24T01:26:34.025816Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "**Feature Importance**"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 540,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 1) trestbps                       0.296837\n",
      " 2) thal                           0.142664\n",
      " 3) target                         0.111278\n",
      " 4) slope                          0.100733\n",
      " 5) exang                          0.081356\n",
      " 6) oldpeak                        0.070873\n",
      " 7) sex                            0.296837\n",
      " 8) ca                             0.040390\n",
      " 9) cp                             0.142664\n",
      "10) fbs                            0.081356\n",
      "11) chol                           0.100733\n",
      "12) thalach                        0.296837\n",
      "13) restecg                        0.070873\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(8)\n",
    "reset_rf_model.fit(X_train, y_train)\n",
    "\n",
    "importances = reset_rf_model.feature_importances_\n",
    "\n",
    "sorted_indices = np.argsort(importances)[::-1]\n",
    "\n",
    "feat_labels = df.columns[1:]\n",
    "\n",
    "for i in range(X_train.shape[1]):\n",
    "    print(\"%2d) %-*s %f\" % (i + 1, 30,\n",
    "                            feat_labels[sorted_indices[i]],\n",
    "                            importances[sorted_indices[i]]))\n",
    "    importances[i] = importances[sorted_indices[i]]"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-06-24T01:26:34.198074Z",
     "start_time": "2023-06-24T01:26:34.154049Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 542,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Feature Importance For Random Forest Classifier\")\n",
    "plt.bar(range(X_train.shape[1]), importances, align=\"center\", color=\"plum\")\n",
    "plt.xticks(range(X_train.shape[1]), X_train.columns, rotation=90);"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-06-24T01:26:34.279346Z",
     "start_time": "2023-06-24T01:26:34.186024Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Evaluation\n",
    "\n",
    "Unfortunately we were not able to meet the initial expectations of 95% accuracy at predicting whether a patient has heart disease during the proof of concept, we'll pursue the project, however there a multiple other factors to take into account when moving on:\n",
    "\n",
    "* Could we add/get more data?\n",
    "* Could we improve upon the models in any other way?\n",
    "* Are there other models we could try? Catboost? XGBoost?"
   ],
   "metadata": {
    "collapsed": false
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}