Heart attack.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2d667870",
   "metadata": {},
   "source": [
    "# Heart Attack Predicting Project"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be742eee",
   "metadata": {},
   "source": [
    "## Problem definition: To predict whether the patients does come heart attack or not"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c9c4ad8",
   "metadata": {},
   "source": [
    "### Features:\n",
    "Age : Age of the patient\n",
    "\n",
    "Sex : Sex of the patient\n",
    "\n",
    "exang: exercise induced angina (1 = yes; 0 = no)\n",
    "\n",
    "ca: number of major vessels (0-3)\n",
    "\n",
    "cp : Chest Pain type chest pain type\n",
    "\n",
    "Value 1: typical angina\n",
    "Value 2: atypical angina\n",
    "Value 3: non-anginal pain\n",
    "Value 4: asymptomatic\n",
    "trtbps : resting blood pressure (in mm Hg)\n",
    "\n",
    "chol : cholestoral in mg/dl fetched via BMI sensor\n",
    "\n",
    "fbs : (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false)\n",
    "\n",
    "rest_ecg : resting electrocardiographic results\n",
    "\n",
    "Value 0: normal\n",
    "Value 1: having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV)\n",
    "Value 2: showing probable or definite left ventricular hypertrophy by Estes' criteria\n",
    "thalach : maximum heart rate achieved\n",
    "\n",
    "target : 0= less chance of heart attack 1= more chance of heart attack\n",
    "\n",
    "n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0cd1b4db",
   "metadata": {},
   "source": [
    "## Data Exploration:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "25294e0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set_theme(color_codes=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cad732a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"C:/Users/vikra/Downloads/heart.csv\")\n",
    "o2_saturation = pd.read_csv(\"C:/Users/vikra/Downloads/o2Saturation.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "190ade33",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trtbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalachh</th>\n",
       "      <th>exng</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slp</th>\n",
       "      <th>caa</th>\n",
       "      <th>thall</th>\n",
       "      <th>output</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>63</td>\n",
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       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trtbps  chol  fbs  restecg  thalachh  exng  oldpeak  slp  \\\n",
       "0   63    1   3     145   233    1        0       150     0      2.3    0   \n",
       "1   37    1   2     130   250    0        1       187     0      3.5    0   \n",
       "2   41    0   1     130   204    0        0       172     0      1.4    2   \n",
       "3   56    1   1     120   236    0        1       178     0      0.8    2   \n",
       "4   57    0   0     120   354    0        1       163     1      0.6    2   \n",
       "\n",
       "   caa  thall  output  \n",
       "0    0      1       1  \n",
       "1    0      2       1  \n",
       "2    0      2       1  \n",
       "3    0      2       1  \n",
       "4    0      2       1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9c2711c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 303 entries, 0 to 302\n",
      "Data columns (total 14 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   age       303 non-null    int64  \n",
      " 1   sex       303 non-null    int64  \n",
      " 2   cp        303 non-null    int64  \n",
      " 3   trtbps    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   thalachh  303 non-null    int64  \n",
      " 8   exng      303 non-null    int64  \n",
      " 9   oldpeak   303 non-null    float64\n",
      " 10  slp       303 non-null    int64  \n",
      " 11  caa       303 non-null    int64  \n",
      " 12  thall     303 non-null    int64  \n",
      " 13  output    303 non-null    int64  \n",
      "dtypes: float64(1), int64(13)\n",
      "memory usage: 33.3 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1489c727",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trtbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalachh</th>\n",
       "      <th>exng</th>\n",
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       "      <th>slp</th>\n",
       "      <th>caa</th>\n",
       "      <th>thall</th>\n",
       "      <th>output</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",
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      ],
      "text/plain": [
       "              age         sex          cp      trtbps        chol         fbs  \\\n",
       "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
       "mean    54.366337    0.683168    0.966997  131.623762  246.264026    0.148515   \n",
       "std      9.082101    0.466011    1.032052   17.538143   51.830751    0.356198   \n",
       "min     29.000000    0.000000    0.000000   94.000000  126.000000    0.000000   \n",
       "25%     47.500000    0.000000    0.000000  120.000000  211.000000    0.000000   \n",
       "50%     55.000000    1.000000    1.000000  130.000000  240.000000    0.000000   \n",
       "75%     61.000000    1.000000    2.000000  140.000000  274.500000    0.000000   \n",
       "max     77.000000    1.000000    3.000000  200.000000  564.000000    1.000000   \n",
       "\n",
       "          restecg    thalachh        exng     oldpeak         slp         caa  \\\n",
       "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
       "mean     0.528053  149.646865    0.326733    1.039604    1.399340    0.729373   \n",
       "std      0.525860   22.905161    0.469794    1.161075    0.616226    1.022606   \n",
       "min      0.000000   71.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      0.000000  133.500000    0.000000    0.000000    1.000000    0.000000   \n",
       "50%      1.000000  153.000000    0.000000    0.800000    1.000000    0.000000   \n",
       "75%      1.000000  166.000000    1.000000    1.600000    2.000000    1.000000   \n",
       "max      2.000000  202.000000    1.000000    6.200000    2.000000    4.000000   \n",
       "\n",
       "            thall      output  \n",
       "count  303.000000  303.000000  \n",
       "mean     2.313531    0.544554  \n",
       "std      0.612277    0.498835  \n",
       "min      0.000000    0.000000  \n",
       "25%      2.000000    0.000000  \n",
       "50%      2.000000    1.000000  \n",
       "75%      3.000000    1.000000  \n",
       "max      3.000000    1.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "17e69c68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Age')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's see the distribution of age column \n",
    "data[\"age\"].hist(bins=20)\n",
    "plt.xlabel(\"Age\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "caba3e33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2af3bc1a550>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x800 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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APzymwsdc1a4pXti2dHEdWkzTrAosAT179pQk7dy5s1poadOmjVJSUrR79+6g5bt371anTp0a3I/PF/0nqN9vNcn9NAfMVfgae64Cb69Ylh1WCDEO1vh8VlTfHgq3H6uWfnhMhY+5QizE9ZuSU6dO1SWXXBK07NNPP5Uk9ejRo1q9YRgaOHCg1q9fH7Q8Jyen2kepAQCAs8R1aDnzzDO1du1aLViwQN98843effdd3XLLLTr77LOrrrlSVFSkvLy8qnUmTJig1atXa/Hixfryyy917733avPmzbr44otjtRkAAKARxHVoOf300zV//ny9/fbbOuecc3TrrbdqxIgRmjVrVlXNzJkzdd5551V9f8opp2jWrFl64YUXNGbMGK1bt04LFy7kwnIAADhcXJ3TMmfOnGrLfv3rX+vXv/51vdYZPXq0Ro8e3ZitAQCAGIvrIy0AAAABhBYAAOAIhBYAAOAIhBYAAOAIhBYAAOAIhBYAAOAIhBYAAOAIhBYAAOAIhBYAAOAIhBYAAOAIhBYAAOAIcfW3hwAgXhmGIcMIv962Jdu2o9cQ0AIRWgAgBMMwlJGRIpcr/IPTfr+l/PwSggvQiAgtABCCYUgul6lVa75QcUlFyPpWKR6dPbyHDKPyiAuAxkFoAYAwFZdUqKikPNZtAC0WJ+ICAABHILQAAABHILQAAABHILQAAABHILQAAABHILQAAABHILQAAABHILQAAABHILQAAABHILQAAABHILQAAABHILQAAABHILQAAABHILQAAABHILQAAABHcMe6AQDRYRiGTNOQJJnmT/+vjW1Ltm03RWsAEBFCC9AMGYahthnJMl0uSVJ6ekrIdSy/X3n5XoILgLhFaAGaIcOQTJdLe9e/IbfKVVZaIcuqPYy4k1PVYciZMozKIy4AEI8ILUAz5i89IFPl8nvL6wwtAOAEnIgLAAAcIa5Cy6JFizRu3LigZe+8847Gjh2rE088UdnZ2brnnntUWlpa6xh+v1/9+/dX7969g/49/PDD0W4fAABEUdy8PbR06VLNnz9fgwYNqlq2ceNGXX311ZoyZYpGjhypHTt26I477lBBQYFmz55d4zhff/21ysrKtGLFCrVr165qeUpK6BMRAQBA/Ip5aNm1a5fuvPNO5eTkqHv37kG3vfjiixoyZIiuuOIKSVL37t113XXX6bbbbtOMGTOUkJBQbbzc3Fy1atVKffr0aYr2AQBAE4l5aPn888/l8Xi0cuVK/d///Z++//77qtsuvfRSmWbwO1imaaqiokLFxcVq27ZttfFyc3OVmZkZ9b4BAEDTinloyc7OVnZ2do239e3bN+j7iooKPf300+rXr1+NgUWStm7dKp/Pp4kTJ2rLli3q1KmTLr74Yv3P//xPg3t1u6N3CpDLZQZ9Re2Yq9ACF5IzTEOyKr/WNVuBerfbDOtTRodetM4OcdG6SMavr/pcRK+mfkI9pho6fnPC8w+xFPPQEi6fz6epU6dq27ZtWrp0aa1127Ztk2VZmjJlijp37qx3331X06dPV0VFhc4777yI7980DWVkpEa8frjS0pKjfh/NBXMVWkKCW1ZpmRITPXXWmUmVt4dzEbpDJSZ5ZKn627SNNX59JSZ55AsjIyTW0k+ox1RDx29OeP4hFhwRWoqLi3Xttddq/fr1WrBggfr3719r7apVq+T3+5WaWhkw+vTpox9++EFPPvlkg0KLZdkqLCyJeP1QXC5TaWnJKiz0yu+3onY/zQFzFZppGkpPT1F5uU9uSWVlFbLreMXvOhg89u8vCftIS3p6ispKK+T3loesr+/49XVoP94w+nEfPFgS6CfUY6qh4zcnPP9q1xQvbFu6uA8tu3fv1h/+8Ad9//33evLJJ5WVlVVnfVJSUrVlvXr10sqVKxvci88X/Seo3281yf00B8xV7QJvTwSCim3Zdf7yNA7e5vNZ9Xp7yAoxbqTj11d9+7Fq6ae2x1Rjjd+c8PxDLMT1m5L79+/XxRdfrLy8PC1dujRkYCksLNTgwYO1bNmyoOWffvqpevbsGc1WAQBAlMX1kZbZs2fr22+/1RNPPKG2bdtqz549Vbe1bdtWLpdLBQUFkqQ2bdooLS1NJ598sh544AG1a9dORx11lN544w2tXLlSixYtitFWAACAxhC3ocXv9+u1115TRUWFLr744mq3v/322+ratasmT54sSVqyZIkkadasWXr44Yd15513at++fcrMzNRDDz2kYcOGNWn/AACgccVVaJkzZ07V/10ulz755JOQ6wTCSkCrVq00ffp0TZ8+vdH7AwAAsROVc1p27twZjWEBAEALFlFoOfbYY2s9CrJx40b9+te/blBTAAAAhwv77aGnnnpKJSWV1ymxbVsvvfSS1qxZU63uo48+qvFvAgGxZhiGjNAXM61i25WPdThH5c849A85nBoA8Sfs0FJWVqYFCxZIqnzCv/TSS9VqTNNU69atdeWVVzZeh0AjMAxDbTOSZbpcYa9j+f3Ky/cSXBwgEEKSkj3yh1GflOw5ZD1+voBThB1arrzyyqow0qdPH/31r3+t88q0QDwxDMl0ubQn53X5vAdC1ruTU9VhyJkyjMojLohvgQMn3p07VJS/P2S9mZEuKbNeR94AxF5Enx7asmVLY/cBNAmf94D83uJYt4EosXwVsitCX2bf8lU0QTcAGlvEH3n+4IMP9M9//lNer1eWFXwpZ8MwNGvWrAY3BwAAEBBRaHnqqad07733KjExUW3btq12UhsnuQEAgMYWUWh57rnndM4552jmzJl8UggAADSJiK7TsnfvXp133nkEFgAA0GQiCi19+/bVtm3bGrsXAACAWkX09tAtt9yia6+9VikpKRowYICSk5Or1fzsZz9rcHMAAAABEYWW888/X5Zl6ZZbbqn1pNvNmzc3qDEAAIBDRRRa/vSnP/EJIQAA0KQiCi3nnntuY/cBAABQp4hCy4YNG0LWZGVlRTI0AACIAdu2o/YuSmONHVFoGTdunAzDCPpDcoc3wzktAADEv8LCQv35z3/Wb3/720Y/4FBeXq65c+eqX79++s1vftPg8SIKLc8++2y1ZSUlJdq4caNWrFihhx9+uMGNAQCA6Nu8ebNWrFihsWPHNvrYu3fv1jPPPKPZs2c3yngRhZbBgwfXuPzUU09VSkqKHn30US1atKhBjQEAABwqoovL1WXQoEFav359Yw8LAAAO4/f7tXTpUp1zzjnq37+/Tj31VM2dO1dlZWWSKk/nGDduXNA6OTk56t27t3JycpSTk6Px48dLksaPH19VO27cON18881auHChfv7zn+ukk07SVVddpe+//75qnJtvvlnZ2dlBY3/33Xfq3bu3li1bpu+++06nn366JGn69OnVaiPR6KHlnXfeUWpqamMPCwAADnPHHXdo9uzZOuOMM/Too4/qwgsv1HPPPaerrroq6LzT2hx33HG64447qsa68847q257++23tWzZMt12222aMWOGNm/erHHjxsnr9YbVW8eOHbVgwQJJ0pVXXln1/4aI6O2hQCo7lGVZ2rlzp77//nv94Q9/aHBjAJof0wz/0wO2rbB2ukBL9cUXX+hvf/ubbrjhBk2aNEmS9Itf/EIdO3bU1KlTtWbNmpBjtGrVSj169JAk9ejRo+r/kuT1erVs2TIdeeSRkqRjjjlGY8aM0fLly3X++eeHHDshIUHHHnusJKlbt27q27dvvbfxcBGFlpp2JKZpqlevXrr88sujcjIPAOcyPAmyLFsZGeEfhfX7LeXnlxBcgFoETsU466yzgpafddZZmj59unJycho0/sCBA6sCi1T5dwePPPJIbdiwIazQEg0RhZYlS5Y0dh8AmjHT5ZZpGlr93pcqOlAesr5VikdnD+8hw6g84gKguv3790uSOnToELTc7XYrIyNDRUVFDRq/U6dO1Za1a9eu6n5jIaLQErBmzRqtX79ehYWFatu2rU466SQNGzassXoD0MwUl1SoqCR0aAEQWnp6uiRpz549OuKII6qWV1RUKD8/XxkZGfr666/l9/uD1ispKQlr/Pz8/GrL9u7dq27dukmqvD5bpGNHKqITccvLy3XZZZdp0qRJWrx4sd555x09/vjjmjRpkiZMmKDycnZKAABEU+DyI6tXrw5avnr1avn9fp100klq1aqVdu7cGXT7hx9+GPS9y+WqcfwPP/wwKLh89tln+u677zR06FBJUmpqqvLz86s+qVSfsSMV0ZGWhx9+WB9++KHuvfdenXXWWXK5XPL5fFq1apVmzJihRx99VNdcc02jNgoAAH7So0cPjRkzRg899JC8Xq+ysrK0efNmLViwQEOGDNGwYcO0a9cuvfPOO5o9e7ays7O1ceNGLV++PGic1q1bS5L+9a9/KT09XX369JFUeSLuZZddpiuvvFIHDhzQAw88oF69eunss8+WJJ122mlasmSJbr31Vp133nnaunWrFi9eHBRUAmOvXbtWmZmZGjBgQIO2OaLQsmrVKl199dVBl+R1u90aPXq09u3bpxdeeIHQAgBAlM2cOVNHHXWUXn75ZT3++OPq2LGjxo8fr6uuukqmaWrs2LH65ptv9Morr+jFF19UVlaWHnrooaATaXv27Kmzzz5bS5cu1XvvvadVq1ZJqrzu2sknn6xbb71VkpSdna2pU6cqISFBUuUnlaZNm6YlS5bo9ddf13HHHacFCxbo97//fdXYrVq10oQJE/SXv/xF7777rj744AN5PJ6Itzei0JKXl1frR5f69u2rXbt2RdwQAAAIj8vl0pVXXqkrr7yy1ttvvPFG3XjjjUHLc3Nzq/5vmqbmzZtXbV3DMHT11Vfr6quvrvX+L730Ul166aVByz799NOg72+++WbdfPPNIbclHBGd09KtW7dq71sFbNiwQV26dGlQUwAAAIeL6EjL73//e82ZM0dJSUk666yz1L59e+3du1erVq3S448/XmcqAwAAiEREoeX888/Xpk2bNHfu3KBDSrZta8yYMVVX5gMAAM4Tr9djiyi0lJeXa+bMmbr00ku1fv167d+/X4Zh6IwzzlBmZmZj9wgAAFC/c1pyc3M1duxYLV68WJKUmZmp888/XxdccIEefPBBXX/99dq+fXtUGgUAAC1b2KHlu+++0/jx47V3714dffTRQbd5PB5NnTpVBQUFuuCCC/j0EAAAaHRhh5bHHntMbdq00SuvvKKRI0cG3ZacnKxLLrlEf/vb35SYmKhFixZF1MyiRYs0bty4oGWbN2/WRRddpBNOOEHZ2dl69tlnQ47z97//XaNGjVL//v01evRorV27NqJ+AABA/Ag7tKxdu1aXXXaZ2rZtW2tNhw4ddOmll+qDDz6odyNLly7V/Pnzg5bl5+drwoQJ6tatm15++WX98Y9/1Ny5c/Xyyy/XOs66det000036fe//71eeeUVDR06VJMmTdKXX35Z754AAED8CPtE3N27d6t79+4h63r16lXt7xzUZdeuXbrzzjuVk5NTbfy//vWv8ng8uvvuu+V2u5WZmakdO3boscce09ixY2sc7/HHH9cZZ5yh8ePHS5KmTZumjz76SM8884zuvvvusPsCAADxJewjLW3bttXu3btD1uXn51f95clwfP755/J4PFq5cmW1v0mwceNGDR48WG73T9nq5JNP1tdff629e/dWG8uyLP3nP/+p+mNOAUOGDNGGDRvC7gkAAMSfsI+0ZGVladmyZTrrrLPqrFu+fHmtl/ivSXZ2trKzs2u8befOnerVq1fQso4dO0qSfvzxR7Vv3z7otsLCQpWUlKhz587V1qnP0Z/auN0RXUA4LC6XGfQVtYtkrkzTqPpqH/x/OPVutynLssO6D8OovOx1uGzblh3e0PUW6N8wDcmq/FrXbNV3eyOdT9P86f/h1Hs8LtlhTJJpVm6dYRhh/QwCNYc/lmp7TB26vdHoX4ru46Exsa+KLdu267WfifX9WpalBQsW6KWXXlJRUZGysrJ0xx136Mgjj4yoj7BDy7hx43T++edrzpw5uu6665SYmBh0e3l5uebPn681a9bosccei6iZw5WWllb9YaaAwP0e+qewD62XVOM6NdXXh2kayshIbdAY4UhLS476fTQXkcxVYpJHlhJC1plJlX/QKz09JeyxbcuSYYa/I69vfSQSEtyySsuUmFj3HyiLZHul8OfTffD+ExI9SrZCj9u6VaIsy673z9jtccvtcYWuO/gC5PDxQ91fYpJHvjCCRST9W5YdViCKF+yrYsMwDP37kx+0v7hhv9PqI71Von7e/2cRrfvII4/o+eef15w5c9S5c2fdd999uuyyy/Tqq69W+10djrBDy/HHH6/p06dr1qxZWrFihYYOHaquXbvK7/frhx9+UE5OjvLz83XNNddo2LBh9W6kJklJSSovLw9aFggfKSnVd66BQFPTOsnJDXuCWZatwsKSBo1RF5fLVFpasgoLvfL7w9irt2CRzJVpGkpPT1FZaYX83vKQ9a6Dv4j37y8J+8hDenqK9q5/Q/7SA6HHT0pV+8Ejwh6/vgL9lJf75JZUVlYhu477iXR7w53PhKQKSVJ5WYW8YdSnp3hkmoZee/8rFZeEru+QkaLTsrrJsiVfhT9kvc9X+bgJPIZCPaYO3d5o9J+a4tFZp2RG7fHQmNhX1a4pXthK0v7iMuUXNV1oiVR5ebmeeuop3XjjjTr11FMlSQ888ICGDRumN954Q2effXa9x6zXFXEvvPBC9enTR08++aTefvvtqgCRmpqqU045RZdeemm181IaonPnztXOowl836lTp2r1bdq0UUpKSo3r1FRfX4EdXTT5/VaT3E9zUJ+5CryCtSw7rF8KxsEan8+q19slFSXF8nuLQ9Zb9Ry/vgL9BIKKHWK7I93ecOczUGNZqld90YFyFR4IvXNOOXikyLYV1tsxgZrDH0O1PaYi3d5w+4/24yEa2FchlC1btujAgQNB55mmpaWpb9++2rBhQ/RDiySddNJJOumkkyRJeXl5crvdSktLq/cdhyMrK0svvvii/H6/XK7KQ77r1q3T0UcfrXbt2lWrNwxDAwcO1Pr16/Xb3/62anlOTo4GDRoUlR4BAEB1gXNJu3TpErS8IeeZNugN9bZt20YtsEjS2LFjVVxcrFtvvVVffPGFli1bpqefflqXX355VU1RUZHy8vKqvp8wYYJWr16txYsX68svv9S9996rzZs36+KLL45anwAAIJjX65XUuOeZxvXp3+3atdMTTzyh7du3a8yYMVqwYIGmTp2qMWPGVNXMnDlT5513XtX3p5xyimbNmqUXXnhBY8aM0bp167Rw4UL+kCMAAE0oKSlJUuOeZxrRX3mOljlz5lRb1r9/f/3lL3+p1zqjR4/W6NGjG7M1AABQD4G3hXbv3q1u3bpVLd+9e7d69+4d0ZhxfaQFAAA4U58+fdSqVSvl5ORULSssLNSmTZuUlZUV0ZhxdaQFAAA0DwkJCbrooos0d+5ctW3bVkcccYTuu+8+de7cWSNGjIhoTEILAAAOkt4qMXRRnNzflClT5PP5dNttt6m0tFRZWVl68skn5fHUfcHL2hBaAABwCNu2I746bUPvN5LL+LtcLt1000266aabGqUPzmkBAMAhYvF3h2J5v4cjtAAAAEcgtAAAAEcgtAAAAEcgtAAAAEcgtAAAAEcgtAAAAEfgOi1wLMOQTDO8j+GFW9fSMZ+NyzDC+6hoJB8nNQxD9VnNtiuvtQE4GaEFjpXWOkmmy1W/leLkWgPxxvAkyLYsZWSk1nNF5rMmgRCSmORRShg5ISnZc8h6oVcwDEMZGSlyucI/WO73W8rPLyG4wNEILXAs0+XSnpzX5fMeCFmb0Ka92p0wXPyKrZnpcsswTe1Z/4Z8JcUh65nP8JTu+V5Fe/eFrDMz0iVlhp0BDUNyuUytWvOFiksqQta3SvHo7OE9ZBiVR1zgXJFemTYe7nfRokV6//33tWTJkojHILTA0XzeA/J7Q/+S9SelNEE3zudnPhuV7ffJrigPWWf5QgePmhSXVKioJPT4aD4Mw1DxlnXylxQ22X26UtLUqs/JDRpj6dKlmj9/vgYNGtSgcQgtAAA4iL+kUP7i/Fi3EZZdu3bpzjvvVE5Ojrp3797g8fj0EAAAiIrPP/9cHo9HK1eu1IABAxo8HkdaAABAVGRnZys7O7vRxuNICwAAcARCCwAAcARCCwAAcARCCwAAcARCCwAAcAQ+PQQAgIO4UtKa9f3VhdACAIBD2Lbd4KvTRnq/Db2M/5w5cxrcB28PAQDgELH4u0OxvN/DEVoAAIAjEFoAAIAjEFoAAIAjEFoAAIAj8OkhoA6mGd7JZ+HWNTeupBTZth2yzkxMliQZRngn9DXVSX+maVT9O/T7muoAxB6hBaiB4UmQbVnKyEit54ot45eb4fbIsmx1GHJmvdZLTPIoJXTGUWJS5a4pWtOZmFjZf3p6StDyw78/XLx8ggJoqQgtQA1Ml1uGaWrP+jfkKykOWZ/Qpr3anTBcLeVXmuFyyzQNvbryfRUWFISs79L1Z8rOHqjSPd+raO++kPWtfZ0kHdXwRmvhdlf2v/r9L1VUXC7TNJSY5FFZaYUsq3qq6tg2WdmDu0etHwDhIbQAdfB7D8jvDR1a/El1v0JvroqLS1RUeCBkXZq3VJJk+32yK8pD1ls+X4N7C8eBkgoVlVSGFp8teb3lNYaW1GRPk/QDoG6ciAsAABwh7o+05OTkaPz48TXe1rVrV7399tvVln/44Ye64IILqi1/9tlnNWTIkEbvEQAARF/ch5YTTzxR77//ftCy//73v5o8ebKuuuqqGtfJzc1Vt27d9PzzzwctT09Pj1qfAAAguuI+tCQkJKhDhw5V35eUlGj27NkaM2aMxo4dW+M6W7duVY8ePYLWAwAAzua4c1oWLlwor9eradOm1VqTm5urzMzMJuwKAABEm6NCS15enp5++mldccUVatOmTa1127Zt01dffaVzzz1Xv/jFLzRhwgR98sknTdcoAABodHH/9tChnn/+ebVu3Vq/+93vaq358ccfVVRUpJKSEt12221yuVx67rnndNFFF2nZsmXq0aNHxPfvdkcv47lcZtBX1O7QOTJNQ3YYVysNdcXTWNW73WaNH7FtqMD4hmlIVuXXuh5ZkfYf/hVuf/panyviGmZ4V6M1zYaNf+j21zx+4Pbo9hPu8z+Wjzf2VYglR4WW5cuXa/To0UpKSqq1pkuXLtqwYYOSk5Pl8VReW+H444/Xpk2btGTJEs2YMSOi+zZNo/5XR41AWlpy1O+jOUlM8shSQsg6d2LlYyEh0S23Hft6M6myPtQVWBsqIcEtq7RMiYl1X2ekvv17Eip3HS63KbfHFbLePPhb3DTDqw+8QEhI8Cg5jKdEoJ+Gjl/bPCUkug9+9SjZil4/9X3+JyZ55AvrCsON/3hjX4VYcExo2bJli7799ludc845IWvT0tKCvjdNU5mZmdq1a1fE929ZtgoLSyJePxSXy1RaWrIKC73y+8PYK7ZggbmSpLLSCvm9oS9WlpBUIUkqL/PJFwf1roNBa//+kqgdaUlPT1F5uU9uSWVlFbLruJ/69q/yyou/+X2WfBX+kOWWZVV9Dafe56usLy+vkDeMfipSPQ0a3zQNJSZ6VFZW8xVxy1Mqxy8vi24/4T7/Az/fstLw+nEfPBjTGI839lW1a4oXti2dY0LLxo0b1a5dO/Xp06fOujVr1uiaa67RypUrdeSRR0qSfD6ftmzZohEjRjSoh8COJZr8fqtJ7qe5sCw7rJ1woCZe6o2DNT6fFdW3hwJBxQ7RV6Tba9sK6w8mBkrCrw/0rTD7aZzxa9v+n+Ynuv2E+/wP/Hzr+/NqzMcb+yrEgmPelNy0aZN69+5d42179uzRgQOVlxIfOHCgMjIyNG3aNH322WfKzc3VtGnTVFBQoEsuuaQJOwYAAI3JMaFlz549tX5i6JRTTtFTTz0lSWrVqpWefvpptW/fXhMnTtTvfvc7FRQU6LnnnlP79u2bsGMAANCYHPP20OOPP17rbbm5uUHfd+vWTQ899FC0WwIAAE3IMUdaAABAy0ZoAQAAjuCYt4eA5iicC4MFhPtJlOakvheva2kMw6jXxesApyO0ADFgeBJkW1a9rutg+f3Ky/e2iOBimJUXZEtI9CgljE/VBi7+1lIEQkhSskehrwJTWffTes3/8YPmq2U904E4YbrcMkxTe9a/IV9Jcch6d3KqOgw5U4bx0zVPmjPj4BGo0j3fq2jvvpD1afYRko6KclfxI3DgxLtzh4ry94esNzPSJWW22CNSaD4ILUAM+b0H5PeGDi0tle33ya4IfcVX2+9rgm7ij+WrCGt+LF9FE3QDRB8n4gIAAEcgtAAAAEcgtAAAAEcgtAAAAEcgtAAAAEcgtAAAAEcgtAAAAEcgtAAAAEcgtAAAAEcgtAAAAEcgtAAAAEcgtAAAAEcgtAAAAEcgtAAAAEcgtAAAAEdwx7oBANHjSkqRKY9cSpBh2bXWmYnJTdgVAESG0AI0Q4YhWZat9oPPDHsdy7JluBOi2BUANAyhBWiGDMOQaRp69dX35S0uls/nl23XfqQlvV07nX3WUJludgkA4hd7KKAZKy4uUUnRAfkq6g4trqSUJuwKACLDibgAAMARCC0AAMARCC0AAMARCC0AAMARCC0AAMARCC0AAMARCC0AAMARCC0AAMARCC0AAMARCC0AAMARHBFadu3apd69e1f7t2zZshrr8/PzdcMNNygrK0uDBw/WjBkz5PV6m7hrAADQmBzxt4e2bNmixMREvfXWWzIMo2p569ata6yfMmWKvF6vnn76aRUWFurWW29VSUmJ7rnnnqZqGQAANDJHhJatW7eqe/fu6tixY8jajz76SOvXr9drr72mzMxMSdLdd9+tyy67TNdff706deoU7XYBAEAUOOLtodzc3KoAEsrGjRvVoUOHoPrBgwfLMAx9+OGH0WoRAABEmSNCy9atW5WXl6cLL7xQP//5z3X++edrzZo1Ndbu2rVLXbp0CVqWkJCgNm3a6Mcff2yKdgEAQBTE/dtDPp9PX331lXr06KGbb75ZrVq10urVqzVp0iQtXrxYQ4cODar3er1KSEioNk5iYqLKysoa1IvbHb2M53KZQV9Ru0PnyDQN2aZRR/VPdYGvZhTr3SmpMkKXy52cUq96V9LBercpy7JD1x+co6qxDclQ7XcUqDPqub2GoaDzzEKOHzf1B/s3g3/GtW171e2u8J6jpiuyfsJ9/v/08zWiMn44982+CrEQ96HF7XYrJydHLpdLSUlJkqR+/fpp27ZtevLJJ6uFlqSkJJWXl1cbp6ysTCkpKRH3YZqGMjJSI14/XGlpyVG/j+YkMckjS9VD6uHciR5JUkKiW2678es9KSmyLFvts0aErA2IpD49vX6P4cAvFrfbVWedaVbWJSS4ZSaHsb0JlbsOl9uU21P32IeOb5rxUR94AZKQ4FHyIU+5xIM/98MlJLqr6pP8IYeXx+OOqJ/6Pv/dYc5/pOPXhX0VYiHuQ4skpaZWDws9e/bU+++/X215586d9dZbbwUtKy8vV0FBQVgn8tbGsmwVFpZEvH4oLpeptLRkFRZ65fdbUbuf5iAwV5JUVlohv7d6SD1cQlKFJKm8zCdfFOrlt2Wahla9+r4KC/aHLO/ctYuyTxuo1a+t1f59eSHrW7dJ1znnnBL24yMwR4Fan88v1XGAxrIq68rLfSoPZ3vLfZIkv8+SryL0b/HA+JYVH/U+X2B7K+T1lss0DSUmelRWVlHjkayK1MowU7bnexWH8fNKt38m6ah691Pfn68vzPmv7/jh3Df7quqa4oVtSxf3oWXbtm363e9+p0cffVRDhgypWv7ZZ5+pR48e1eqzsrI0d+5c7dixQ0cddZQkaf369ZKkk046qUG9BJ740eT3W01yP82FZdlhvV0SqIl2fVFRiQr3F4esb922tLK+2BtWvVyVT9X6Pj7sQOu2ZNu1b0fgJrue22uHGLfa+HFTf7B/S0HbW9vP+2AmkuX3ySoP/Taz5fdF1E/9f752VMevC/sqxELcvymZmZmpY445Rnfffbc2btyoL7/8UrNnz9Z///tfXXnllfL7/dqzZ49KSyt/CQwYMEADBw7Uddddp08++UTr1q3THXfcodGjR/NxZwAAHCzuQ4tpmlq4cKH69++va6+9VmPGjNHHH3+sxYsXq1evXvrxxx91yimn6LXXXpNUecLZggUL1LVrV1188cW69tprNXz4cN11112x3RAAANAgcf/2kCS1b99es2fPrvG2rl27Kjc3N2hZu3bt9NBDDzVFawAAoInE/ZEWAAAAidACAAAcgtACAAAcgdACAAAcgdACAAAcgdACAAAcgdACAAAcgdACAAAcgdACAAAcgdACAAAcgdACAAAcgdACAAAcwRF/MBHRYRiGDCP8etuWbNuO2vjhMs3IB3UlpYS1DWZicsT34WRmYrJcya1C1yUkNUE3LY/LFd7ryHDrGqK252/g+WeaRtBzsb77ByAShJYWyjAMtc1Ilulyhb2O5fcrL98b1o4pkvFty5Jh1nNnHGYqMtweWZatDkPODHtoy7JluBPq149DJSRUzk+7E4bXb8X6/rxQo8SkJFmWrbS0+oVlwwz/+VWvcQ1DGRkpdYaj9PSUoO/9fkv5+SUEF0QVoaWFMgzJdLm0J+d1+bwHQta7k1PVYciZMozKV1SNPX5Cm/Zqd8Jw7Vn/hnwlxXXWmqahlA6d1PrYnyvcYy6Gyy3TNPTqyvdVWFAQsj69XTudfdZQme6W8RTxeFwyTUOrVq/V/n37QtZ36fozZWcPrH/IRI0SEj0yTUOr/7Fe+/PyQ9Z3PqKzsn85IGrzbxiVR3NWrflCxSUVQbeZpqHEJI/KSitkWZU7g1YpHp09vEfY+wcgUi1jj4xa+bwH5PfWHRKaYnx/UuWrNn8Y9bZpyC5Pj6if4uISFRWGDlGupJSQNc1R8QFvWPOT5i1tgm5anqLCYhXmFYSsa53eOvrNSCouqVBRSXnQMtM05LMlr7e8KrQATYWXSQAAwBEILQAAwBEILQAAwBEILQAAwBEILQAAwBEILQAAwBEILQAAwBEILQAAwBEILQAAwBEILQAAwBEILQAAwBEILQAAwBEILQAAwBEILQAAwBEILQAAwBHcsW4AOJQrKUW2bddZY5qGjISkJunHTEyWK7lV6Lqm6sc0ZJpGWHUIzTAkwzBkGMbB7w0ZNUxdTctagkPn5tBlP90WvKw+jzvbVsjnOnA4QgviguH2yLJsdRhyZtjrWJYtw50QlX4SEir7aXfC8PqtaEbn4KVhuiRJ6ekpURm/pQnMZ0KiRynWT8uTkjw11icktqxdZSCEJCV75K+l5tC5Skqu/H9GRmrY92H5/crL9xJcUC8t65mIuGW43DJNQ6+ufF+FBQV11xqGMjq0169HDpHpjs5D2ONxyTQNrVq9Vvv37QtZ36Xrz5SdPVBG1EJL5bj7Pn5P5fl7QtYndeyqjL6D1UIPEIRkHDwiULrnexXt3SfDMOR2m/L5rBp/iabZR0g6qom7jJ3AERTvzh0qyt9/2G3V58rVvp2kzLAfn+7kVHUYcqYMo/KICxAuQgviSnFxiYoKD9RZYxiGPCnhv6JrUD8HvCH7kaQ0b2kTdCNZpSXye4tD1vnLvE3QjfPZfp/sinLJMGTLJbvCX2Nosf2+GHQXe5avonJ+DlXDXAXmJ9zHJxApTsQFAACO4IgjLQUFBbr//vv1r3/9S8XFxerdu7duuOEGDRo0qMb6Rx99VPPnz6+2PDc3N8qdAgCAaHFEaLn++uu1Z88e3X///WrXrp2WLFmiiRMn6pVXXtExxxxTrT43N1f/8z//o5tuuikG3QIAgGiI+7eHduzYoQ8++EB33XWXBg0apKOPPlq33367OnbsqFdffbXGdbZu3aq+ffuqQ4cOQf8AAIBzxX1oycjI0GOPPabjjz++alng2gGFhYXV6svLy/X111/XeAQGAAA4V9y/PZSWlqZf/vKXQctef/117dixQ7fccku1+i+++EJ+v1+vv/66Zs6cqbKyMmVlZemmm25Sx44dG9SL2x29jOdymUFfoy1wESjTNGTX42Jlbrcpywr9GcVIxw9c7KtOh9xs1PNia2GNLx1y0aw4q6/vxeWqVpSMOj4AHbfb21T1gVVqmae47z9kfWVNuPuXQF1NF5eraa4ifXyGuz8BAuI+tBzuP//5j6ZPn64RI0bo1FNPrXb71q1bJUnJycl68MEHtW/fPt1///0aP368li9frqSkyK5cappGvS6cFKm0tOSo38ehEpM8shT6Am3mwQtJ1ffiZuGO70mofCi63KbcHlfY4yckuGUmN/745sHrophmfNXXe3tdgV8Odd9HvG5vU9fXNk9O6b82gRdc9d2/uOt4vhw6V/V9fEa6PwEcFVreeust3XjjjRo4cKDmzp1bY83o0aM1fPhwtW3btmpZz549NXz4cL3zzjsaNWpURPdtWbYKC0siWjccLpeptLRkFRZ65fdboVdoINM0lJ6eorLSCvm95SHrXQeDx/79JWEfaanP+CqvvM6D32fJV1HbNTgPOuSFXHm5T+WNPb4ky7KqvsZTfb2311/5s/L5/FIdP7Z43d4mqzcqfwnXNk9x338IPl9lfbj7l8D+yFfT86WGuarv47O++xOnaIoXti2dY0LLc889p5kzZ2rkyJG65557lJBQe5o/NLBIUseOHdWmTRvt3LmzQT0EnvjR5PdbTXI/gcOzlmWHtdMwrMAvP6tebw+FO36gJpy/R3Lo4Xs7CuMH6uKyvp7b+9OKdd9P3G5vE9VXPaZqWT/e+w9dX1lT3/2LbdvVxq9prur7+Kzv/gQIiPsTcSXp+eef15/+9CddeOGFuv/+++sMLA888IDOPPPMoCfad999p/z8fPXo0aMp2gUAAFEQ96Fl+/btmjVrln71q1/p8ssv1969e7Vnzx7t2bNHRUVFKi8v1549e1ReXnlI8le/+pW+//573XXXXdq+fbs2bNigyZMna+DAgRo2bFiMtwYAAEQq7t8eev3111VRUaE333xTb775ZtBtY8aM0ZgxYzR+/Hg9++yzGjJkiPr166fHH39cDz74oM4991wlJCTo9NNP17Rp08I6yx4AAMSnuA8tV1xxha644oo6aw6/PP/QoUM1dOjQaLYFAACaWNy/PQQAACA54EgLosuVFN51EsKtQ3SZiclyJbcKXZcQ2fWI0LzV9+JyQLwhtLRQhlH5sdiOJ48Mex3LssVpQbGRkOCRZdlqd8Lw+q1ohn+hPjRfiUlJsiy73heXM3j8IM4QWloow6i83PbKle+pqGB/yPrWbdL1m98MO3gyM9dVaGoej0umaWjV6rXav29fyPouXX+m7OyBMsK4pDqav4REj0zT0Op/rNf+vPyQ9Z2P6KzsXw6QYXLEBfGF0NLCFReXqKjwQMg6w+1pgm4QSvEBb1g/rzRvaRN0A6cpKixWYV5ByLrW6a2j3wwQAWI0AABwBEILAABwBEILAABwBEILAABwBEILAABwBEILAABwBEILAABwBEILAABwBEILAABwBEILAABwBEILAABwBEILAABwBEILAABwBEILAABwBEILAABwBHesG2hpDMOQYVRfbppG1dfA/yXJtiXbtpuqvUbnSkoJq85MSIpofDMxWa7kVlEbH0Ds1bbfrL2+cp1wWZYty3LufrYlIbQ0IcMw1DYjWabLVWtNenrwL3nL71devtdxwcUwKncEHU8eWb8VzfAO/iUkeGRZttqdMDwq4wOID+HsNw9nWXbQi79w6vPzDxBcHIDQ0oQMQzJdLu3JeV0+74Gg20zTUGKSR2WlFVVPHHdyqjoMOVOGUXnExUkMo/KI0cqV76moYH/I+i5df6bs7IEywgwVHo9Lpmlo1eq12r9vX6OPDyA+1LXfrElSx67K6DtYq15bq8KCwpD1aelpOvusoTJNg9DiAISWGPB5D8jvLQ5aZpuGLCXI7y1vVk+c4uISFRWG3tGkeUsjG/+AN6rjA4gPNe03a+Iv80qSigqKVJhXEOWu0NR42QkAAByB0AIAAByB0AIAAByB0AIAAByB0AIAAByB0AIAAByB0AIAAByB0AIAAByB0AIAAByB0AIAAByB0AIAABzBEaHFsiw99NBDGjZsmE444QT94Q9/0LfffltrfX5+vm644QZlZWVp8ODBmjFjhrxebxN2DAAAGpsjQssjjzyi559/Xn/605/04osvyrIsXXbZZSovL6+xfsqUKdqxY4eefvppPfjgg3r33Xd11113NW3TAACgUcV9aCkvL9dTTz2lKVOm6NRTT1WfPn30wAMPaOfOnXrjjTeq1X/00Udav3697rnnHh133HEaOnSo7r77bq1YsUK7du2KwRYAAIDGEPehZcuWLTpw4ICGDh1atSwtLU19+/bVhg0bqtVv3LhRHTp0UGZmZtWywYMHyzAMffjhh03SMwAAaHyGbdt2rJuoyxtvvKHJkyfr448/VlJSUtXya665RqWlpVq0aFFQ/Z///Gd9/PHHeumll4KWDx06VJdddpkmTpwYUR+2bcuyGjZVhiGZpil/mVeqYdoNw1DQj8Mw5EpMlmVZDbrfWrqRaRoqKSkNa3zTNJWSknRwDsKZh/qN73K5lJycGH6926XkpHrU13d86qmnvv77h/JS2eHsr+q5b6trv1ltaJdLpieC3i0rnOHr5HLF/XEAx3PHuoFQAifQJiQkBC1PTEzU/v37a6w/vDZQX1ZWFnEfhmHI5TIiXv9QrsTketWbZvSeCCkpSaGLDmGahqTw56G+41NPPfXOrTcT6rs/CX/fVt/9Zv33bQQOJ4j7n1Lg6MrhJ92WlZUpObn6gzgpKanGE3TLysqUkpISnSYBAEDUxX1o6dKliyRp9+7dQct3796tTp06Vavv3Llztdry8nIVFBSoY8eO0WsUAABEVdyHlj59+qhVq1bKycmpWlZYWKhNmzYpKyurWn1WVpZ27typHTt2VC1bv369JOmkk06KfsMAACAq4v6cloSEBF100UWaO3eu2rZtqyOOOEL33XefOnfurBEjRsjv9ysvL0+tW7dWUlKSBgwYoIEDB+q6667TXXfdpZKSEt1xxx0aPXp0jUdmAACAM8T9p4ckye/36/7779eyZctUWlqqrKws3XHHHeratau+++47nX766Zo9e7bOPfdcSdK+ffs0Y8YMvffee0pMTNTIkSM1ffp0JSYmxnhLAABApBwRWgAAAOL+nBYAAACJ0AIAAByC0AIAAByB0AIAAByB0AIAAByB0AIAAByB0NLE9u3bp5tuukknn3yyTjzxRE2aNElffvll1e2bN2/WRRddpBNOOEHZ2dl69tlnY9ht/Ni+fbtOPPFELVu2rGoZc/WTXbt2qXfv3tX+BeaLufrJ8uXLNWrUKB1//PE666yz9Pe//73qtu+++06XX365Bg4cqFNOOUXz58+X3++PYbexkZOTU+PjqXfv3jr99NMlMVeIjbi/Im5z88c//lGWZemxxx5TamqqHnzwQV1yySV64403VFpaqgkTJig7O1szZszQf//7X82YMUOpqakaO3ZsrFuPmYqKCt14440qKSmpWpafn89cHWLLli1KTEzUW2+9JcP46a9wt27dmrk6xIoVK3Trrbfqlltu0bBhw7R69Wpdf/316ty5s/r166eJEyeqe/fuevHFF/XNN9/o1ltvlWmamjJlSqxbb1Innnii3n///aBl//3vfzV58mRdddVVqqioYK4QGzaaTEFBgX399dfbubm5Vcs2b95s9+rVy/7444/thQsX2qeccopdUVFRdfu8efPsESNGxKLduDFv3jx7/Pjxdq9eveyXX37Ztm2buTrMY489Zp9zzjk13sZcVbIsyz7ttNPsOXPmBC2/9NJL7YULF9qvvvqq3a9fP7ugoKDqthdffNEeOHCgXVZW1tTtxpUDBw7Yp512mn3zzTfbtm0zV4gZ3h5qQunp6Zo3b5569eolScrLy9PTTz+tzp07q0ePHtq4caMGDx4st/unA2Ann3yyvv76a+3duzdWbcfUhg0b9Je//EVz5swJWs5cBcvNzVVmZmaNtzFXlbZv367vv/9e55xzTtDyJ598Updffrk2btyo4447Tunp6VW3nXzyySouLtbmzZubut24snDhQnm9Xk2bNk2SmCvEDKElRm6//XYNHTpUq1ev1syZM5WSkqKdO3eqc+fOQXUdO3aUJP3444+xaDOmCgsLNXXqVN12223q0qVL0G3MVbCtW7cqLy9PF154oX7+85/r/PPP15o1ayQxVwHbt2+XJJWUlGjixIkaOnSofvvb3+qdd96RxDzVJvDi6oorrlCbNm0kMVeIHUJLjFx88cV6+eWXdfbZZ+uPf/yjPv/8c5WWliohISGoLvBHHsvKymLRZkzdddddOvHEE6u9MpbEXB3C5/Ppq6++0v79+zV58mQ99thjOuGEEzRp0iStXbuWuTqouLhYkjRt2jSdffbZeuqpp/SLX/xCV111FfNUh+eff16tW7fW7373u6plzBVihRNxY6RHjx6SpJkzZ+rjjz/Wc889p6SkJJWXlwfVBXYAKSkpTd5jLC1fvlwbN27Uq6++WuPtzNVP3G63cnJy5HK5lJSUJEnq16+ftm3bpieffJK5Osjj8UiSJk6cqDFjxkiSjj32WG3atEmLFy9mnmqxfPlyjR49uuqxJfH8Q+xwpKUJ5eXlafXq1fL5fFXLTNNUjx49tHv3bnXu3Fm7d+8OWifwfadOnZq011h7+eWXtW/fPp166qk68cQTdeKJJ0qS7rzzTl122WXM1WFSU1ODfqlIUs+ePbVr1y7m6qDAtgbOKQvo0aOHvvvuO+apBlu2bNG3335b7Wgnc4VYIbQ0ob179+r666/X2rVrq5ZVVFRo06ZNyszMVFZWlj788MOgax2sW7dORx99tNq1axeLlmNm7ty5eu2117R8+fKqf5I0ZcoUzZw5k7k6xLZt2zRw4EDl5OQELf/ss8/Uo0cP5uqg4447Tqmpqfr444+Dlm/dulXdunVTVlaWNm3aVPU2klQ5T6mpqerTp09TtxsXNm7cqHbt2lXbfuYKMRPrjy+1NJdddpk9YsQIe/369XZubq59/fXX21lZWfb3339v7927187KyrKnTZtmb9u2zX755Zft448/3l62bFms244Lh37kmbn6id/vt8eOHWuPGjXK3rBhg/3FF1/Ys2bNsvv162fn5uYyV4f4v//7P/vEE0+0X331VXvHjh32I488Yvfp08det26dXVpaap9xxhn2xIkT7c2bN9tvvvmmPXjwYPvhhx+OddsxM336dPuSSy6ptpy5QqwYtm3bsQ5OLUlRUZHmzZunt956S0VFRRo0aJBuvvlm9ezZU5L0ySefaObMmdq0aZM6dOigSy+9VBdddFGMu44PvXv31uzZs3XuuedKYq4OtXfvXs2bN0/vvfeeCgsL1bdvX914440aNGiQJObqUIsXL9Zzzz2nXbt2KTMzU5MnT9YZZ5whSdqxY4dmzJihjRs3Kj09Xeedd54mT54s02yZB6X/8Ic/qFWrVnrggQeq3cZcIRYILQAAwBGIxAAAwBEILQAAwBEILQAAwBEILQAAwBEILQAAwBEILQAAwBEILQAAwBEILQAAwBEILQAAwBEILQAAwBEILQAAwBEILQAkSaWlpZo3b55GjBihfv36aeDAgZowYYI2b95cVfPKK69o1KhROv744/Wb3/xGa9euVd++fbVs2bKqmh9++EHXX3+9Bg8erAEDBujiiy/Wpk2bYrFJAJoZQgsASdLUqVP18ssva9KkSXrqqac0ffp0bdu2TTfccINs29by5ct18803a+DAgXrkkUd05pln6qqrrpLf768aIy8vT7///e/1+eef6/bbb9e8efNkWZYuvPBCffnllzHcOgDNgTvWDQCIvfLych04cEC33XabRo0aJUkaPHiwiouLNWfOHO3du1cPPvigTjvtNP35z3+WJA0bNkwej0fz5s2rGueZZ55RQUGBXnjhBR1xxBGSpOHDh2vUqFF68MEH9dBDDzX9xgFoNjjSAkAJCQl68sknNWrUKO3atUvr1q3Tiy++qH/+85+SpO3bt+uHH37QyJEjg9Y766yzgr5fu3atjj32WHXq1Ek+n08+n0+maWr48OH697//3WTbA6B54kgLAEnSe++9p1mzZumrr75Samqq+vTpo5SUFEmSx+ORJLVr1y5onfbt2wd9X1BQoB07dui4446r8T68Xq+Sk5Oj0D2AloDQAkDffPON/vjHP+qMM87QokWLdOSRR8owDC1dulTvvfde1Xkr+/btC1rv8O9bt26twYMHa+rUqTXeT0JCQnQ2AECLwNtDAPTZZ5+prKxMkyZNUrdu3WQYhqTKoy+S1LFjR3Xr1k1vvvlm0HpvvPFG0PeDBw/W9u3bdfTRR+v444+v+rdixQr97W9/k8vlapoNAtAsEVoA6LjjjpPb7dZ9992nDz74QP/85z81efJk/etf/5JU+bbOlClT9NZbb+nOO+/U+++/ryeeeEIPPvigJMk0K3cll1xyiSzL0iWXXKLXXntNa9eu1e23364lS5bo6KOPjtXmAWgmDNu27Vg3ASD2/vGPf2jBggX65ptvlJ6erhNOOEHjx4/XuHHjdPvtt+vCCy/UX/7yFz355JP64Ycf1LNnT1144YW69dZb9fDDD2vEiBGSKt9qmjdvntauXauysjJ1795d48aN03nnnRfjLQTgdIQWAGFZtWqV+vbtq2OOOaZq2b/+9S9dfvnlWrFihfr06RPD7gC0BIQWAGGZNGmSvvzyS1177bXq0qWLduzYoYceekjdunXTkiVLYt0egBaA0AIgLPn5+Zo3b57WrFmjvLw8tW/fXmeeeaamTJmi1NTUWLcHoAUgtAAAAEfg00MAAMARCC0AAMARCC0AAMARCC0AAMARCC0AAMARCC0AAMARCC0AAMARCC0AAMARCC0AAMAR/h/OnASRXprtQAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 563.5x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's visualize the same distribution in seaborn\n",
    "plt.figure(figsize=(8, 8))\n",
    "sns.displot(x=\"age\", data=data, hue=\"output\", bins=30, color=\"BlGr\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "587403c9",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x2af3bcd9890>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1250x1250 with 30 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(data[[\"age\", \"cp\", \"trtbps\", 'chol', 'output']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1952bc7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>98.6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>98.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>98.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>98.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>98.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>97.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   98.6\n",
       "0  98.6\n",
       "1  98.6\n",
       "2  98.6\n",
       "3  98.1\n",
       "4  97.5"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "o2_saturation.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "333e4c28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trtbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalachh</th>\n",
       "      <th>exng</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slp</th>\n",
       "      <th>caa</th>\n",
       "      <th>thall</th>\n",
       "      <th>output</th>\n",
       "      <th>o2Saturation</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",
       "      <td>98.6</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",
       "      <td>98.6</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",
       "      <td>98.6</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",
       "      <td>98.1</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",
       "      <td>97.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trtbps  chol  fbs  restecg  thalachh  exng  oldpeak  slp  \\\n",
       "0   63    1   3     145   233    1        0       150     0      2.3    0   \n",
       "1   37    1   2     130   250    0        1       187     0      3.5    0   \n",
       "2   41    0   1     130   204    0        0       172     0      1.4    2   \n",
       "3   56    1   1     120   236    0        1       178     0      0.8    2   \n",
       "4   57    0   0     120   354    0        1       163     1      0.6    2   \n",
       "\n",
       "   caa  thall  output  o2Saturation  \n",
       "0    0      1       1          98.6  \n",
       "1    0      2       1          98.6  \n",
       "2    0      2       1          98.6  \n",
       "3    0      2       1          98.1  \n",
       "4    0      2       1          97.5  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"o2Saturation\"] = o2_saturation[\"98.6\"]\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2efb8da6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 8))\n",
    "plt.bar(data.oldpeak, data.thalachh, color=[\"red\", \"blue\"]);\n",
    "plt.xlabel(\"Oldpeak\");\n",
    "plt.ylabel(\"Thalach\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "67c930d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2af3f0937d0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data[\"age\"], data[\"o2Saturation\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "75f4a1b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trtbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalachh</th>\n",
       "      <th>exng</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slp</th>\n",
       "      <th>caa</th>\n",
       "      <th>thall</th>\n",
       "      <th>output</th>\n",
       "      <th>o2Saturation</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",
       "      <td>0.019479</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",
       "      <td>-0.133972</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",
       "      <td>0.136100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trtbps</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",
       "      <td>0.045749</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",
       "      <td>-0.030027</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",
       "      <td>-0.054655</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",
       "      <td>0.070147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thalachh</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",
       "      <td>0.149697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>exng</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",
       "      <td>-0.081410</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",
       "      <td>0.017763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>slp</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",
       "      <td>-0.044691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>caa</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",
       "      <td>-0.133258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thall</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",
       "      <td>-0.010630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>output</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",
       "      <td>0.309337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>o2Saturation</th>\n",
       "      <td>0.019479</td>\n",
       "      <td>-0.133972</td>\n",
       "      <td>0.136100</td>\n",
       "      <td>0.045749</td>\n",
       "      <td>-0.030027</td>\n",
       "      <td>-0.054655</td>\n",
       "      <td>0.070147</td>\n",
       "      <td>0.149697</td>\n",
       "      <td>-0.081410</td>\n",
       "      <td>0.017763</td>\n",
       "      <td>-0.044691</td>\n",
       "      <td>-0.133258</td>\n",
       "      <td>-0.010630</td>\n",
       "      <td>0.309337</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   age       sex        cp    trtbps      chol       fbs  \\\n",
       "age           1.000000 -0.098447 -0.068653  0.279351  0.213678  0.121308   \n",
       "sex          -0.098447  1.000000 -0.049353 -0.056769 -0.197912  0.045032   \n",
       "cp           -0.068653 -0.049353  1.000000  0.047608 -0.076904  0.094444   \n",
       "trtbps        0.279351 -0.056769  0.047608  1.000000  0.123174  0.177531   \n",
       "chol          0.213678 -0.197912 -0.076904  0.123174  1.000000  0.013294   \n",
       "fbs           0.121308  0.045032  0.094444  0.177531  0.013294  1.000000   \n",
       "restecg      -0.116211 -0.058196  0.044421 -0.114103 -0.151040 -0.084189   \n",
       "thalachh     -0.398522 -0.044020  0.295762 -0.046698 -0.009940 -0.008567   \n",
       "exng          0.096801  0.141664 -0.394280  0.067616  0.067023  0.025665   \n",
       "oldpeak       0.210013  0.096093 -0.149230  0.193216  0.053952  0.005747   \n",
       "slp          -0.168814 -0.030711  0.119717 -0.121475 -0.004038 -0.059894   \n",
       "caa           0.276326  0.118261 -0.181053  0.101389  0.070511  0.137979   \n",
       "thall         0.068001  0.210041 -0.161736  0.062210  0.098803 -0.032019   \n",
       "output       -0.225439 -0.280937  0.433798 -0.144931 -0.085239 -0.028046   \n",
       "o2Saturation  0.019479 -0.133972  0.136100  0.045749 -0.030027 -0.054655   \n",
       "\n",
       "               restecg  thalachh      exng   oldpeak       slp       caa  \\\n",
       "age          -0.116211 -0.398522  0.096801  0.210013 -0.168814  0.276326   \n",
       "sex          -0.058196 -0.044020  0.141664  0.096093 -0.030711  0.118261   \n",
       "cp            0.044421  0.295762 -0.394280 -0.149230  0.119717 -0.181053   \n",
       "trtbps       -0.114103 -0.046698  0.067616  0.193216 -0.121475  0.101389   \n",
       "chol         -0.151040 -0.009940  0.067023  0.053952 -0.004038  0.070511   \n",
       "fbs          -0.084189 -0.008567  0.025665  0.005747 -0.059894  0.137979   \n",
       "restecg       1.000000  0.044123 -0.070733 -0.058770  0.093045 -0.072042   \n",
       "thalachh      0.044123  1.000000 -0.378812 -0.344187  0.386784 -0.213177   \n",
       "exng         -0.070733 -0.378812  1.000000  0.288223 -0.257748  0.115739   \n",
       "oldpeak      -0.058770 -0.344187  0.288223  1.000000 -0.577537  0.222682   \n",
       "slp           0.093045  0.386784 -0.257748 -0.577537  1.000000 -0.080155   \n",
       "caa          -0.072042 -0.213177  0.115739  0.222682 -0.080155  1.000000   \n",
       "thall        -0.011981 -0.096439  0.206754  0.210244 -0.104764  0.151832   \n",
       "output        0.137230  0.421741 -0.436757 -0.430696  0.345877 -0.391724   \n",
       "o2Saturation  0.070147  0.149697 -0.081410  0.017763 -0.044691 -0.133258   \n",
       "\n",
       "                 thall    output  o2Saturation  \n",
       "age           0.068001 -0.225439      0.019479  \n",
       "sex           0.210041 -0.280937     -0.133972  \n",
       "cp           -0.161736  0.433798      0.136100  \n",
       "trtbps        0.062210 -0.144931      0.045749  \n",
       "chol          0.098803 -0.085239     -0.030027  \n",
       "fbs          -0.032019 -0.028046     -0.054655  \n",
       "restecg      -0.011981  0.137230      0.070147  \n",
       "thalachh     -0.096439  0.421741      0.149697  \n",
       "exng          0.206754 -0.436757     -0.081410  \n",
       "oldpeak       0.210244 -0.430696      0.017763  \n",
       "slp          -0.104764  0.345877     -0.044691  \n",
       "caa           0.151832 -0.391724     -0.133258  \n",
       "thall         1.000000 -0.344029     -0.010630  \n",
       "output       -0.344029  1.000000      0.309337  \n",
       "o2Saturation -0.010630  0.309337      1.000000  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr = data.corr()\n",
    "corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "195ae311",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 10))\n",
    "sns.heatmap(corr,\n",
    "            annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f17d927",
   "metadata": {},
   "source": [
    "Now, we can see in the heat map that the o2Saturation, thalach anf the cp columns are good correlated with the output we are going to predict"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12f6d55a",
   "metadata": {},
   "source": [
    "## Buliding the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0fe76502",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the dataset\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(data.drop(\"output\", axis=1),\n",
    "                                                    data[\"output\"],\n",
    "                                                    test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "183fd155",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((212, 14), (91, 14), (212,), (91,))"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "076fb645",
   "metadata": {},
   "source": [
    "## Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7b927375",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We have our data ready. Let's train the model\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "077d86fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8351648351648352"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest = RandomForestClassifier()\n",
    "forest.fit(X_train, y_train)\n",
    "forest.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c02fcbf",
   "metadata": {},
   "source": [
    "We have a accuracy of 83%. Let's predict and evaluate the output "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d982aed4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0,\n",
       "       1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1,\n",
       "       1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1,\n",
       "       0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0,\n",
       "       1, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest_preds = forest.predict(X_test)\n",
    "forest_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a27263f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[35 10]\n",
      " [ 5 41]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Confusion Matrix')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Evaluate the prediced labels with the original labels\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "\n",
    "print(confusion_matrix(y_test, forest_preds))\n",
    "sns.heatmap(confusion_matrix(y_test, forest_preds),\n",
    "            annot=True)\n",
    "plt.xlabel(\"Actual Labels\")\n",
    "plt.ylabel(\"Predicted Labels\")\n",
    "plt.title(\"Confusion Matrix\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9861b8c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.78      0.82        45\n",
      "           1       0.80      0.89      0.85        46\n",
      "\n",
      "    accuracy                           0.84        91\n",
      "   macro avg       0.84      0.83      0.83        91\n",
      "weighted avg       0.84      0.84      0.83        91\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, forest_preds))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45d0fcc7",
   "metadata": {},
   "source": [
    "### Using RandomizedSearchCV for hyperparameter turning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7c1501e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "estimator = RandomForestClassifier()\n",
    "param_dists = {\"n_estimators\": [80, 90, 100, 110, 120],\n",
    "               \"max_depth\": [5, 10, 15],\n",
    "               \"max_features\" : [\"auto\", \"sqrt\", \"log2\"],\n",
    "               \"min_samples_split\": [2, 1, 3, 4],\n",
    "               \"min_samples_leaf\": [1, 2, 3, 4]}\n",
    "\n",
    "randomized_model = RandomizedSearchCV(estimator=estimator,\n",
    "                                      param_distributions=param_dists,\n",
    "                                      verbose=True,\n",
    "                                      n_iter=10,\n",
    "                                      n_jobs=-1,\n",
    "                                      cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4e1e3ae3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vikra\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\model_selection\\_validation.py:547: FitFailedWarning: \n",
      "15 fits failed out of a total of 50.\n",
      "The score on these train-test partitions for these parameters will be set to nan.\n",
      "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
      "\n",
      "Below are more details about the failures:\n",
      "--------------------------------------------------------------------------------\n",
      "5 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"C:\\Users\\vikra\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\model_selection\\_validation.py\", line 895, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"C:\\Users\\vikra\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\base.py\", line 1467, in wrapper\n",
      "    estimator._validate_params()\n",
      "  File \"C:\\Users\\vikra\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\base.py\", line 666, in _validate_params\n",
      "    validate_parameter_constraints(\n",
      "  File \"C:\\Users\\vikra\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\utils\\_param_validation.py\", line 95, in validate_parameter_constraints\n",
      "    raise InvalidParameterError(\n",
      "sklearn.utils._param_validation.InvalidParameterError: The 'min_samples_split' parameter of RandomForestClassifier must be an int in the range [2, inf) or a float in the range (0.0, 1.0]. Got 1 instead.\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "8 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"C:\\Users\\vikra\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\model_selection\\_validation.py\", line 895, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"C:\\Users\\vikra\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\base.py\", line 1467, in wrapper\n",
      "    estimator._validate_params()\n",
      "  File \"C:\\Users\\vikra\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\base.py\", line 666, in _validate_params\n",
      "    validate_parameter_constraints(\n",
      "  File \"C:\\Users\\vikra\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\utils\\_param_validation.py\", line 95, in validate_parameter_constraints\n",
      "    raise InvalidParameterError(\n",
      "sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of RandomForestClassifier must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'log2', 'sqrt'} or None. Got 'auto' instead.\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "2 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"C:\\Users\\vikra\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\model_selection\\_validation.py\", line 895, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"C:\\Users\\vikra\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\base.py\", line 1467, in wrapper\n",
      "    estimator._validate_params()\n",
      "  File \"C:\\Users\\vikra\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\base.py\", line 666, in _validate_params\n",
      "    validate_parameter_constraints(\n",
      "  File \"C:\\Users\\vikra\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\utils\\_param_validation.py\", line 95, in validate_parameter_constraints\n",
      "    raise InvalidParameterError(\n",
      "sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of RandomForestClassifier must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'sqrt', 'log2'} or None. Got 'auto' instead.\n",
      "\n",
      "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
      "C:\\Users\\vikra\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\model_selection\\_search.py:1051: UserWarning: One or more of the test scores are non-finite: [0.83111849        nan 0.82635659 0.83565891 0.82159468 0.84019934\n",
      "        nan 0.81683278        nan 0.82624585]\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=5, estimator=RandomForestClassifier(), n_jobs=-1,\n",
       "                   param_distributions={&#x27;max_depth&#x27;: [5, 10, 15],\n",
       "                                        &#x27;max_features&#x27;: [&#x27;auto&#x27;, &#x27;sqrt&#x27;,\n",
       "                                                         &#x27;log2&#x27;],\n",
       "                                        &#x27;min_samples_leaf&#x27;: [1, 2, 3, 4],\n",
       "                                        &#x27;min_samples_split&#x27;: [2, 1, 3, 4],\n",
       "                                        &#x27;n_estimators&#x27;: [80, 90, 100, 110,\n",
       "                                                         120]},\n",
       "                   verbose=True)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomizedSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.model_selection.RandomizedSearchCV.html\">?<span>Documentation for RandomizedSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomizedSearchCV(cv=5, estimator=RandomForestClassifier(), n_jobs=-1,\n",
       "                   param_distributions={&#x27;max_depth&#x27;: [5, 10, 15],\n",
       "                                        &#x27;max_features&#x27;: [&#x27;auto&#x27;, &#x27;sqrt&#x27;,\n",
       "                                                         &#x27;log2&#x27;],\n",
       "                                        &#x27;min_samples_leaf&#x27;: [1, 2, 3, 4],\n",
       "                                        &#x27;min_samples_split&#x27;: [2, 1, 3, 4],\n",
       "                                        &#x27;n_estimators&#x27;: [80, 90, 100, 110,\n",
       "                                                         120]},\n",
       "                   verbose=True)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: RandomForestClassifier</label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier()</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier()</pre></div> </div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=RandomForestClassifier(), n_jobs=-1,\n",
       "                   param_distributions={'max_depth': [5, 10, 15],\n",
       "                                        'max_features': ['auto', 'sqrt',\n",
       "                                                         'log2'],\n",
       "                                        'min_samples_leaf': [1, 2, 3, 4],\n",
       "                                        'min_samples_split': [2, 1, 3, 4],\n",
       "                                        'n_estimators': [80, 90, 100, 110,\n",
       "                                                         120]},\n",
       "                   verbose=True)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's fit the model with our Randomized Model \n",
    "randomized_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "facbfba0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 120,\n",
       " 'min_samples_split': 4,\n",
       " 'min_samples_leaf': 2,\n",
       " 'max_features': 'sqrt',\n",
       " 'max_depth': 10}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "randomized_model.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "fd8fc68b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8131868131868132"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "randomized_model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7151ec2e",
   "metadata": {},
   "source": [
    "Yes!! I got a few improvement in the RandomForestClassifier Model. Let's evaluate the predictions on X_test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a9188e30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0,\n",
       "       1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1,\n",
       "       1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1,\n",
       "       0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0,\n",
       "       1, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "randomized_preds = randomized_model.predict(X_test)\n",
    "randomized_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c694ebfc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[35 10]\n",
      " [ 5 41]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Confusion Matrix')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(confusion_matrix(y_test, forest_preds))\n",
    "sns.heatmap(confusion_matrix(y_test, randomized_preds),\n",
    "            annot=True)\n",
    "plt.xlabel(\"Actual Labels\")\n",
    "plt.ylabel(\"Predicted Labels\")\n",
    "plt.title(\"Confusion Matrix\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "10b109c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      0.76      0.80        45\n",
      "           1       0.78      0.87      0.82        46\n",
      "\n",
      "    accuracy                           0.81        91\n",
      "   macro avg       0.82      0.81      0.81        91\n",
      "weighted avg       0.82      0.81      0.81        91\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, randomized_preds))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b084c123",
   "metadata": {},
   "source": [
    "## Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "61c379bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vikra\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\linear_model\\_logistic.py:469: 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": [
       "0.8241758241758241"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "log_reg = LogisticRegression(max_iter=90)\n",
    "log_reg.fit(X_train, y_train)\n",
    "log_reg.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "dc52a6e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0,\n",
       "       1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1,\n",
       "       1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1,\n",
       "       0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1,\n",
       "       1, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_reg_preds = log_reg.predict(X_test)\n",
    "log_reg_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "56e73f3d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[33 12]\n",
      " [ 4 42]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Confusion Matrix')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Evaluate the prediced labels with the original labels\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "\n",
    "print(confusion_matrix(y_test, log_reg_preds))\n",
    "sns.heatmap(confusion_matrix(y_test, log_reg_preds),\n",
    "            annot=True)\n",
    "plt.xlabel(\"Actual Labels\")\n",
    "plt.ylabel(\"Predicted Labels\")\n",
    "plt.title(\"Confusion Matrix\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "1a184781",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.73      0.80        45\n",
      "           1       0.78      0.91      0.84        46\n",
      "\n",
      "    accuracy                           0.82        91\n",
      "   macro avg       0.83      0.82      0.82        91\n",
      "weighted avg       0.83      0.82      0.82        91\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, log_reg_preds))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e5bf1e4",
   "metadata": {},
   "source": [
    "### Let's improve our Logistic Regression Model using the GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "1a1367d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "estimator = LogisticRegression()\n",
    "params_grid = {\"penalty\": ['l1', 'l2', 'elasticnet'],\n",
    "               \"multi_class\": ['auto', 'ovr', 'multinomial'],\n",
    "               \"max_iter\": [100, 120, 80],\n",
    "               \"solver\": ['newton-cg', 'lbfgs', 'liblinear']}\n",
    "\n",
    "grid_model = GridSearchCV(estimator= estimator,\n",
    "                          param_grid= params_grid,\n",
    "                          verbose=True,\n",
    "                          cv= 5,\n",
    "                          n_jobs= -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "2e160c25",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 81 candidates, totalling 405 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vikra\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\model_selection\\_validation.py:547: FitFailedWarning: \n",
      "255 fits failed out of a total of 405.\n",
      "The score on these train-test partitions for these parameters will be set to nan.\n",
      "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
      "\n",
      "Below are more details about the failures:\n",
      "--------------------------------------------------------------------------------\n",
      "45 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"C:\\Users\\vikra\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\model_selection\\_validation.py\", line 895, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"C:\\Users\\vikra\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\base.py\", line 1474, in wrapper\n",
      "    return fit_method(estimator, *args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"C:\\Users\\vikra\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 1172, in fit\n",
      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"C:\\Users\\vikra\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 67, in _check_solver\n",
      "    raise ValueError(\n",
      "ValueError: Solver newton-cg supports only 'l2' or None penalties, got l1 penalty.\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "45 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"C:\\Users\\vikra\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\model_selection\\_validation.py\", line 895, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"C:\\Users\\vikra\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\base.py\", line 1474, in wrapper\n",
      "    return fit_method(estimator, *args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"C:\\Users\\vikra\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 1172, in fit\n",
      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"C:\\Users\\vikra\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 67, in _check_solver\n",
      "    raise ValueError(\n",
      "ValueError: Solver lbfgs supports only 'l2' or None penalties, got l1 penalty.\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "45 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"C:\\Users\\vikra\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\model_selection\\_validation.py\", line 895, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"C:\\Users\\vikra\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\base.py\", line 1474, in wrapper\n",
      "    return fit_method(estimator, *args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"C:\\Users\\vikra\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 1172, in fit\n",
      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"C:\\Users\\vikra\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 67, in _check_solver\n",
      "    raise ValueError(\n",
      "ValueError: Solver newton-cg supports only 'l2' or None penalties, got elasticnet penalty.\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "45 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"C:\\Users\\vikra\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\model_selection\\_validation.py\", line 895, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"C:\\Users\\vikra\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\base.py\", line 1474, in wrapper\n",
      "    return fit_method(estimator, *args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"C:\\Users\\vikra\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 1172, in fit\n",
      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"C:\\Users\\vikra\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 67, in _check_solver\n",
      "    raise ValueError(\n",
      "ValueError: Solver lbfgs supports only 'l2' or None penalties, got elasticnet penalty.\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "45 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"C:\\Users\\vikra\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\model_selection\\_validation.py\", line 895, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"C:\\Users\\vikra\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\base.py\", line 1474, in wrapper\n",
      "    return fit_method(estimator, *args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"C:\\Users\\vikra\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 1172, in fit\n",
      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"C:\\Users\\vikra\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 75, in _check_solver\n",
      "    raise ValueError(\n",
      "ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "30 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"C:\\Users\\vikra\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\model_selection\\_validation.py\", line 895, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"C:\\Users\\vikra\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\base.py\", line 1474, in wrapper\n",
      "    return fit_method(estimator, *args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"C:\\Users\\vikra\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 1212, in fit\n",
      "    multi_class = _check_multi_class(self.multi_class, solver, len(self.classes_))\n",
      "                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"C:\\Users\\vikra\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 99, in _check_multi_class\n",
      "    raise ValueError(\"Solver %s does not support a multinomial backend.\" % solver)\n",
      "ValueError: Solver liblinear does not support a multinomial backend.\n",
      "\n",
      "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
      "C:\\Users\\vikra\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\model_selection\\_search.py:1051: UserWarning: One or more of the test scores are non-finite: [       nan        nan 0.83543743 0.8448505  0.83532669 0.82126246\n",
      "        nan        nan        nan        nan        nan 0.83543743\n",
      " 0.8448505  0.83532669 0.82126246        nan        nan        nan\n",
      "        nan        nan        nan 0.85448505 0.83067553        nan\n",
      "        nan        nan        nan        nan        nan 0.83543743\n",
      " 0.8448505  0.83067553 0.82126246        nan        nan        nan\n",
      "        nan        nan 0.83543743 0.8448505  0.83067553 0.82126246\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.85448505 0.82126246        nan        nan        nan        nan\n",
      "        nan        nan 0.83543743 0.8448505  0.83056478 0.82126246\n",
      "        nan        nan        nan        nan        nan 0.83543743\n",
      " 0.8448505  0.83056478 0.82126246        nan        nan        nan\n",
      "        nan        nan        nan 0.85448505 0.82591362        nan\n",
      "        nan        nan        nan]\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=LogisticRegression(), n_jobs=-1,\n",
       "             param_grid={&#x27;max_iter&#x27;: [100, 120, 80],\n",
       "                         &#x27;multi_class&#x27;: [&#x27;auto&#x27;, &#x27;ovr&#x27;, &#x27;multinomial&#x27;],\n",
       "                         &#x27;penalty&#x27;: [&#x27;l1&#x27;, &#x27;l2&#x27;, &#x27;elasticnet&#x27;],\n",
       "                         &#x27;solver&#x27;: [&#x27;newton-cg&#x27;, &#x27;lbfgs&#x27;, &#x27;liblinear&#x27;]},\n",
       "             verbose=True)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=5, estimator=LogisticRegression(), n_jobs=-1,\n",
       "             param_grid={&#x27;max_iter&#x27;: [100, 120, 80],\n",
       "                         &#x27;multi_class&#x27;: [&#x27;auto&#x27;, &#x27;ovr&#x27;, &#x27;multinomial&#x27;],\n",
       "                         &#x27;penalty&#x27;: [&#x27;l1&#x27;, &#x27;l2&#x27;, &#x27;elasticnet&#x27;],\n",
       "                         &#x27;solver&#x27;: [&#x27;newton-cg&#x27;, &#x27;lbfgs&#x27;, &#x27;liblinear&#x27;]},\n",
       "             verbose=True)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: LogisticRegression</label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression()</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression()</pre></div> </div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5, estimator=LogisticRegression(), n_jobs=-1,\n",
       "             param_grid={'max_iter': [100, 120, 80],\n",
       "                         'multi_class': ['auto', 'ovr', 'multinomial'],\n",
       "                         'penalty': ['l1', 'l2', 'elasticnet'],\n",
       "                         'solver': ['newton-cg', 'lbfgs', 'liblinear']},\n",
       "             verbose=True)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e21ed149",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_iter': 100,\n",
       " 'multi_class': 'multinomial',\n",
       " 'penalty': 'l2',\n",
       " 'solver': 'newton-cg'}"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_model.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "4b938dab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9009433962264151"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_model.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5e14e73",
   "metadata": {},
   "source": [
    "Ohh!! We got an improvement using the gridsearchCv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "bf566eae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0,\n",
       "       1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0,\n",
       "       1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1,\n",
       "       0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1,\n",
       "       1, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_preds = grid_model.predict(X_test)\n",
    "grid_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "e1f9ee43",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[33 12]\n",
      " [ 4 42]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Confusion Matrix')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(confusion_matrix(y_test, log_reg_preds))\n",
    "sns.heatmap(confusion_matrix(y_test, grid_preds),\n",
    "            annot=True)\n",
    "plt.xlabel(\"Actual Labels\")\n",
    "plt.ylabel(\"Predicted Labels\")\n",
    "plt.title(\"Confusion Matrix\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "742adee0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.80      0.84        45\n",
      "           1       0.82      0.89      0.85        46\n",
      "\n",
      "    accuracy                           0.85        91\n",
      "   macro avg       0.85      0.85      0.85        91\n",
      "weighted avg       0.85      0.85      0.85        91\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, grid_preds))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c083dd4e",
   "metadata": {},
   "source": [
    "We have better predictions when we go with the Logistic regression"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "327dac8c",
   "metadata": {},
   "source": [
    "## Decision Tree Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "38da8beb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7692307692307693"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "dtree = DecisionTreeClassifier()\n",
    "dtree.fit(X_train, y_train)\n",
    "dtree.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "4f6374f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0,\n",
       "       1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1,\n",
       "       1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1,\n",
       "       0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0,\n",
       "       1, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtree_preds = dtree.predict(X_test)\n",
    "dtree_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "b520a4e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[32 13]\n",
      " [ 8 38]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Confusion Matrix')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9PfXyyy9rwIABVt3DZDabzcaEn7E8WDjE0SEAGU7zsUccHQKQIW279K2h4z/4foHNxnqhfk+bjWUEKhoAANhbBqlo2ANzNAAAgGGoaAAAYG8Z5KFq9kCiAQCAvdE6AQAASD8qGgAA2ButEwAAYBhaJwAAAOlHRQMAAHtzoooGiQYAAPbmRHM0aJ0AAADDUNEAAMDeaJ0AAADDOFHrhEQDAAB7c6KKBnM0AACAYahoAABgb7ROAACAYWidAAAApB8VDQAA7M2JKhokGgAA2JvZ7OgI7IbWCQAAMAwVDQAA7I3WCQAAMIwTJRq0TgAAgGGoaAAAYG9s2AUAAAzjRK0TEg0AAOyN5a0AAADpR0UDAAB7o3UCAAAM40SJBq0TAABgGCoaAADYG8tbAQCAUcxJrDoBAABINyoaAADYmxNNBiXRAADA3pxojgatEwAAYBgqGgAA2JsTTQYl0QAAwN6YowEAAAzjRIkGczQAAIBhqGjA5pLMZi07cFZrjpzX9XsPVNQ7h7pVL6mW5QpZrvnm18tavP+0frtxT57Z3BRU1Ff96wXIJ3tWB0YO2I9v/jz69NsFGtVzrI7s+clyvEaDIHUf9LqK+hfR7Zt39PXqzVo+Y4UeJj50YLSwOR4TDzy7OT+c0Mwdx9WmYmHNaFtdQUXzaOSGH7Xp2O+SpK+P/66h6w6pbL5cmtq6qvrUKaMDF2L01ue7Ff/wkYOjB4znm99X70e+pxw5cyQ7XrXuS5r06Xid/fWcRr05VivnrtJrb7VT/4l9HRQpDJOUZLtXBkdFAzb1IPGhIg+eVaeXSujNGi9KkoKK+urYtdv67PA5NS9bUAv3nlLtEnk1qmlFy+eK5c6hrst/0I4z19S4dAFHhQ8YymQyqWm7xgob3UsymVKc79yno04ePaUpQ6ZKkg79cFg5c+dU136dNXvcx4p7EGfvkIF0I9GATblnyaIlnWsrt4d7suNuWUy6F/9ISWazahT1VZXCPsnOF/P58ze7S7H37RYrYG8lA0po0DsD9OXSdTr0w2G9t3RysvNThkyTq2uWZMcSExJlcjHJ1S2L9MCe0cJQLG8Fnk0WF5P883pJksxms27eT9BXRy9o328xGtW0olxMJg1uUC7F574/dVWSVNLH067xAvZ07fJ1da7zuqKvxKhSzcAU569cuGL52SOHh16qU0X/DG2vrV99r3t3/rBnqDCaE+0M6tBE4+HDh/rmm2904MABXblyRQkJCXrhhReUL18+VatWTU2aNFGWLFmePhAypK+PX9bwfx+WJNUpkVctyxZK9bqLt/7Qh98fU+m8XqpdMq89QwTs6m7sXd2NvfvU63Lnza21h1dJkn7/7bIWvrfI6NAAwzhsMuilS5fUsmVLjRgxQidOnFC2bNnk6+srNzc3/frrrxo+fLhefvllXb582VEhIp3K58+lhR3/oYhG5XXk91sKX71X5r/MtD53467e+ny3sriYNPWVqnJJpW8NOJuEuHgNfG2IxvYar8SERM1ZN1N5/Hye/kE8P5LMtntlcA6raIwfP16FChXSmjVr5OmZslx+584dDRw4UOPHj9fcuXMdECHSq7B3dhX2zq6XCvsou7urRm88osOXbuql/87POHAhRoO/PCgPtyz6pENNFfbO7uCIgYzh3p0/9OPuI5KkEz+d0Irdy9SiQ3Mtnb7csYHBZszPwWoRW3FYRePAgQMaOnRoqkmGJHl5eentt9/WgQMH7BwZ0uPm/Xit//mibv4Rn+x4QL6ckqToe3/Omt907HeFrdqrfJ7ZtKRLbRVnbgacnIuLi4Jb1VOpcqWSHb966Zruxt5VnnxUNDKVDFLRuHHjht5++23VqFFDlStXVkhIiM6cOWM5f/z4cXXp0kWVKlVSgwYNtHTpUqvv4bBEw9PTU9euXXviNZcvX1a2bNnsFBFsIf7hI43eeERfHL2Q7Pie36IlSS/6emnnmWsateFHBRbMrU871VI+zxccESqQoSQlJSlkeE+FDO+Z7PiL5UspZ+6cOnP8nIMiQ2YWHh6u8+fPa/78+VqzZo2yZcum7t2768GDB7p165beeOMNFSlSRFFRUQoPD9fUqVMVFRVl1T0c1jpp166dIiIi1L9/f9WoUUP58+eXu7u7EhISdO3aNe3fv19Tp05Vu3btHBUinkF+Lw+1rlBY83eflKuLi8rk89Lhizf16b7TalOhiArl8lDoyj3ycHdVz5ov6uyNe8k+n88zG4kHnNbiD5ZqxEfDNHByf23fsEP5i+bXG4Ne19lfz2nTqq8dHR5sKQOsOrl9+7YKFiyoXr16yd/fX5LUu3dvvfLKKzp16pT27NkjNzc3jR8/Xq6uripZsqQlKWnbtm2a7+OwRKNv375ycXHRlClTdP9+yr0TsmfPrs6dO6t///4OiA7pMbJJRRXM5aGon87ryp0H8vPMprDapdWtekkdvHBD0f9tq4St2pvis73+4a+w2qXtHTKQIXwTtUXxcXHq1LuDmrRrpAd/xOmHr3/Q/HcXKiEuwdHhwZYywCTOnDlzatq0aZb3N2/e1OLFi+Xn56dSpUpp5syZql69ulxd/3+qUKNGDc2bN08xMTHKkydPmu5jMv91GYCdJSYm6vjx47p27ZoePHigbNmyyc/PT2XKlJG7u/vTB0ijBwuH2GwsILNoPvaIo0MAMqRtl741dPw/xne22Vj/t/3qE89v3br1qWOMHj1aq1atkru7uz7++GPVrl1bL7/8surWrau3337bct3p06fVsmVLrVmzRhUqVEhTfA7fsMvNzU0VK1Z8+oUAAGQWGWzVSbdu3fTPf/5TkZGRCg8P14oVKxQXF5fiF/6sWf988GV8fHxqw6TK4YkGAABOx4atk7RULJ6mVKk/VztNmjRJP/30k5YvX65s2bIpISF5y+5xguHh4ZHmsXl6KwAATujmzZvasGGDHj58aDnm4uKiUqVK6fr16/Lz89P169eTfebx+3z58qX5PiQaAADYmznJdq9nFBMTo0GDBmnPnj2WY4mJiTp27JhKliypatWq6dChQ3r06JHl/N69e1W8eHH5+KR9XxcSDQAA7C0DbNjl7++vunXrauLEiTpw4IBOnjypiIgI3blzR927d1fbtm117949jRw5UqdPn9batWu1ePFi9erVy6r7kGgAAOCkPvjgA9WsWVMDBw5U+/btFRsbq8jISBUoUEA+Pj5asGCBzp07pzZt2mjWrFkaOnSo2rRpY9U9HL681V5Y3gqkxPJWIHVGL2+9NzztG149TY53rNup095YdQIAgL1lgA277IVEAwAAe3OiRIM5GgAAwDBUNAAAsLcM8FA1eyHRAADA3midAAAApB8VDQAA7MzsRBUNEg0AAOzNiRINWicAAMAwVDQAALC3JFadAAAAo9A6AQAASD8qGgAA2JsTVTRINAAAsDMneXC6JBINAADsz4kqGszRAAAAhqGiAQCAvTlRRYNEAwAAO3OmLchpnQAAAMM8c0Xj/v378vDwkCRt3rxZly9fVv369VWsWDFbxQYAQOZERePvnT17Vo0bN9b8+fMlSdOnT9eAAQP03nvv6ZVXXtGhQ4dsHiQAAJlKkg1fGZzVicbUqVPl6uqqhg0bKiEhQStWrFDz5s118OBB1alTR9OnTzcgTAAA8DyyOtE4ePCgBg8erAoVKmj//v26e/eu/vnPfypHjhzq0KGDfv75ZyPiBAAg0zAnmW32yuisnqORmJgoLy8vSdKOHTv0wgsv6KWXXpIkPXr0SK6uLGQBAOCJnoMEwVasrmj4+/vrm2++UXR0tL7++mvVrl1brq6uSkxMVGRkpPz9/Y2IEwAAPIesLj/069dP4eHhioyMlLu7u9566y1JUtOmTRUTE6O5c+faPEgAADKV52ASp61YnWjUqlVL69ev19GjRxUYGKiCBQtKkrp166YaNWqodOnSNg8SAIDM5HmYW2ErzzShonDhwipcuHCyY926dbNJQAAAZHpUNJJ7/fXX0zygyWTSkiVLnjkgAACQeaQp0TCb017iseZaAACcEa2Tv1i2bJnRcQAA4DxonTzdmTNntGvXLkVHR6tLly66ePGiypQpoxw5ctgyPgAA8ByzOtFISkrSmDFjFBUVJbPZLJPJpGbNmmnOnDk6f/68IiMj5efnZ0SsAABkCmYnqmhYvWHXnDlztH79ek2cOFG7du2yzMl4++23ZTab9eGHH9o8SAAAMhUeqvb3oqKi1K9fP7Vt21a5cuWyHA8ICFC/fv20a9cuW8YHAACeY1a3TmJiYhQQEJDquXz58unOnTvpDgoAgMyM1skTFC1aVNu3b0/13P79+1W0aNF0BwUAQKbmRK0Tqysa3bp105gxY5SYmKj69evLZDLp/Pnz2rdvnxYtWqSIiAgj4gQAAM8hqxON9u3b6+bNm/r444/12WefyWw2a9CgQXJzc1PPnj3VsWNHI+IEACDTcKbWyTPto9GrVy917txZP/74o2JjY+Xl5aXAwMBkk0MBAEDqSDTSICkpybKPhru7u9zc3GwZFwAAmRaJxhMkJSVpypQpWrFihRITEy37aLzwwgsKCwtTSEiIzYMEAADPJ6sTjdmzZ2vZsmXq0qWLGjduLB8fH8XExOjf//63pk+fruzZs6tz585GxAoAQOZgNjk6AruxOtGIiopSWFiY+vTpYzlWvHhxVatWTTly5NCnn35KogEAwBM4U+vE6n00bt26pcqVK6d6rk6dOoqOjk53UAAAIHOwOtGoWbOmNm3alOq53bt3q0qVKukOCgCAzMycZLLZK6NLU+vkyy+/tPxcqVIlzZo1Szdu3FDz5s3l6+ur2NhYbd++XZs3b9bIkSONihUAgEzBmVonJvPjZSNPUKZMmbQPaDLp+PHj6QrKCA8WDnF0CECG03zsEUeHAGRI2y59a+j4l/9R32ZjFdj9vc3GMkKaKhpbt241Og4AAJyGmVUnyRUsWDDNA6ahQAIAgFNzptbJM+0MunHjRu3fv18JCQmWxMJsNuv+/fs6cuSIduzYYdMgAQCAbcXGxuqDDz7Qtm3bdO/ePZUuXVqDBw9W1apVJUlvvPGGdu/enewz1atX17Jly6y6j9WJxqxZszRr1ix5enrq4cOHcnNzk6urq27evCkXFxe1b9/e2iEBAHAqGWG1yKBBgxQdHa0PPvhAPj4+WrZsmXr06KEvvvhCJUqU0IkTJzRu3Dg1atTI8plnedyI1ctbv/jiC7Vu3Vr79+9X9+7dVb9+fe3evVtr1qxRrly59OKLL1odBAAAzsRstt3rWZw/f167du3SuHHjVLVqVRUvXlyjR49W3rx5tX79et24cUM3btxQYGCgfH19La9neXiq1YnGtWvX9PLLL8tkMikgIEA//vijJKl8+fIKDQ3V6tWrrQ4CAABn4uh9NLy9vTV//nxVqFDBcsxkMslkMunOnTs6ceKETCaTihcvnu7vanXrxMPDQybTn1+saNGiunTpkuLi4pQtWzYFBATo0qVL6Q4KAACkTcOGDZ94PrWVo15eXqpXr16yY5s3b9b58+c1YsQInTx5Up6enho/frx27dolDw8PNWvWTL1795a7u7tV8Vld0ahQoYJlA6/ixYsrS5Ys2rNnjyTpzJkzVgcAAICzcXRF468OHz6s4cOHq0mTJgoODtbJkycVHx+vihUrasGCBQoLC9Pq1as1atQoq8e2uqIRGhqqN954Q3fu3NHcuXP1f//3fxo2bJiCgoL0ww8/JJs0AgAAUrLlThDp3evq22+/1ZAhQ1SlShVNnTpVkjR+/HgNGzZMOXPmlCT5+/vLzc1NAwcO1NChQ5UnT540j291olGtWjWtWbNGJ06ckCSNGTNGLi4uOnz4sJo1a6aIiAhrhwQAAA6wfPlyTZo0Sc2aNdN7771n6Uq4urpakozHHi/2uHr1qrGJhvTnluSPtyXPmjWrJkyY8CzDAADglDLC8tYVK1ZowoQJ6tq1q0aOHGmZfylJXbt2VaFChfTOO+9Yjh09elRubm4qVqyYVfexeo7Gk3zxxRdq2rSpLYcEACDTMZtNNns9i3Pnzmny5Mlq3LixevXqpZiYGEVHRys6Olp3795V06ZN9dVXX+mzzz7TxYsXtXHjRk2ZMkU9evRQjhw5rLrXM1U0/s6dO3d04cIFWw4JAABsbPPmzUpMTNSWLVu0ZcuWZOfatGmjd999VyaTScuWLdPkyZPl6+ur7t27KyQkxOp72TTRAAAAT+foZ52EhoYqNDT0idd07txZnTt3Tve9SDQAALCzJCd6eqtN52gAAAD8LyoaAADY2bNO4nwepSnRKFOmTLJlL3/HbDan6ToAAJxZRljeai9pSjTCw8NJIAAAsBFb7gya0aUp0ejbt6/RcQAAgEyIORoAANgZrRMAAGAYlrcCAADYABUNAADsjOWtAADAMKw6+Ysvv/zSqkFbt279DKEAAIDMJk2JRkRERLL3j/fUMP9PSva/+2yQaAAA8PecaTJomhKNrVu3Wn4+fvy43n77bfXu3VvNmzdX3rx5devWLX333XeaOXOm3nnnHcOCBQAgM2COxl8ULFjQ8nPfvn3Vu3dvvfXWW5Zj+fLlU8eOHZWQkKD3339f9erVs32kAADguWP1ZNAzZ86obNmyqZ4rUaKELl26lO6gAADIzJxpMqjV+2gUK1ZM69evT/XcypUr5e/vn+6gAADIzJLMJpu9MjqrKxrh4eHq37+/fvvtN9WvX1/e3t6KiYnRN998o9OnT+uTTz4xIs508wz7zNEhABnOg8s7HR0C4JSYo/EETZo00ezZszV79mxNnz5dZrNZLi4uqly5shYvXqyqVasaEScAAHgOPdOGXQ0aNFCDBg0UHx+v27dvK1euXHJ3d7d1bAAAZErPQ8vDVp55Z9AzZ85o165dio6OVpcuXXTx4kWVKVNGOXLksGV8AABkOk40F9T6RCMpKUljxoxRVFSUzGazTCaTmjVrpjlz5ujChQtavny5/Pz8jIgVAAA8Z6xedTJnzhytX79eEydO1K5duyy7g7799ttKSkrShx9+aPMgAQDITJxp1YnViUZUVJT69euntm3bKleuXJbjAQEB6tevn3bt2mXL+AAAyHTMZpPNXhmd1YlGTEyMAgICUj2XL18+3blzJ91BAQCAzMHqRKNo0aLavn17quf279+vokWLpjsoAAAysyQbvjI6qyeDduvWTWPGjFFiYqLq168vk8mk8+fPa9++fVq0aFGKJ70CAIDkzMr4LQ9bsTrRaN++vW7evKmPP/5Yn332mcxmswYNGiQ3Nzf17NlTHTt2NCJOAADwHDKZzc/2aJd79+7pxx9/VGxsrLy8vBQYGJhscmhG4+pe8OkXAU6GLciB1LnlKWHo+NvytbfZWMHXVttsLCNYPUdj+PDhunjxonLkyKE6dero5ZdfVr169ZQrVy6dPXtWoaGhRsQJAECmkSSTzV4ZXZpaJ5cvX7b8/OWXX6pRo0bKkiVLiut27Nih3bt32y46AAAyIeZo/MW//vUv7dixw/K+T58+qV5nNptVq1Yt20QGAACee2lKNMaPH6/du3fLbDZrxIgRCgsLU5EiRZJd4+LiIi8vLwUFBRkSKAAAmcXzsCzVVtKUaOTLl09t2rSRJJlMJgUHB8vLy8vSPomLi1NiYqI8PT2NixQAgEzCmVonVk8GbdWqlaZPn67XXnvNcuzw4cOqWbOm3nvvPSUlOVOeBgAAnsTqRGPmzJlat26dWrVqZTlWtmxZDRkyRKtWrdKCBQtsGiAAAJkNO4M+wfr16zVs2DB16NDBcixXrlzq3r27XF1dtXTpUoWEhNg0SAAAMpPnIUGwFasrGrdu3VLhwoVTPVeiRAldvXo13UEBAIDMwepEo0SJEtq8eXOq57777jseqgYAwFOYZbLZK6OzunXy+uuvKyIiQrGxsWrUqJF8fHx08+ZNff/999q0aZPeeecdI+IEACDTSMr4+YHNWJ1otG7dWn/88YfmzJmjb775xnLc29tbo0ePVuvWrW0ZHwAAeI5ZnWhIUufOndWpUyedO3fO8lC1EiVKyMXF6k4MAABO53l4RomtPFOiIf25cVeJEsY+3Q4AgMzomR6b/pxKU6IREBCglStXqmLFiipTpoxMpr/PxEwmk44dO2azAAEAyGycaXlrmhKN8PBw5cuXz/LzkxINAACAx0xms9kpKjiu7gUdHQKQ4Ty4vNPRIQAZklseY6cGrMnf2WZjtbsSabOxjJCmisbly5etGrRAgQLPFAwAAM7AKX7D/680JRoNGjSwql1y/PjxZw4IAABkHmlKNCZPnmxJNG7fvq2pU6eqZs2aat68uXx9fRUbG6vvvvtO27ZtU0REhKEBAwDwvHOmyaBWz9EIDw+Xt7e3Jk6cmOLcpEmTdOrUKS1evNhW8dkMczSAlJijAaTO6DkanxWw3RyNjpcz9hwNq3fY2rVrl5o3b57queDgYP3444/pDgoAABgrNjZWY8aMUd26dVWlShV17NhRBw8etJzfs2ePXn31VQUGBqpZs2basGHDM93H6kTD29tb//nPf1I9t3fvXssyWAAAkLokmWz2elaDBg3Sjz/+qA8++EBRUVEKCAhQjx49dPbsWZ05c0a9evVSnTp1tHbtWrVv315Dhw7Vnj17rL6P1TuDtm/fXrNnz1ZcXJyCg4Pl7e2tmJgYff311/rss880YsQIq4MAAMCZOHrVyfnz57Vr1y6tWLFCL730kiRp9OjR2rlzp9avX68bN26odOnSGjhwoCSpZMmSOnbsmBYsWKCaNWtadS+rE42wsDDdvXtXCxcu1Pz58yVJZrNZ2bJlU//+/dW5s+36TgAA4MkaNmz4xPNbt25Ncczb21vz589XhQoVLMdMJpNMJpPu3LmjgwcPqlGjRsk+U6NGDU2aNElms9mqlahWJxomk0nDhg1T7969deTIEd2+fVve3t6qXLmyPDw8rB0OAACn4+jHxHt5ealevXrJjm3evFnnz5/XiBEj9MUXX8jPzy/Z+bx58+rBgwe6deuWcufOneZ7PfND1bJnzy5fX1+ZzWYFBgYqISGBRAMAgDSw5fLW1CoW1jp8+LCGDx+uJk2aKDg4WHFxcXJ3d092zeP3CQkJVo39TInGV199pWnTpik6Olomk0mrV6/WzJkz5ebmpmnTpqUIDgAA/H+OnqPxv7799lsNGTJEVapU0dSpUyVJWbNmTZFQPH7/wgsvWDW+1atONm7cqGHDhqlGjRr64IMPlJT0Z17WuHFjbd++XXPmzLF2SAAA4ADLly9X3759Vb9+fc2dO1dZs2aVJOXPn1/Xr19Pdu3169fl4eEhT09Pq+5hdUVj7ty56tChg8aNG6dHjx5Zjrdt21Y3b97UqlWrNGDAAGuHBQDAaTh6joYkrVixQhMmTFDXrl01cuTIZBM8q1atqv379ye7fu/evapSpYpcXKyrUVhd0Th37pwaN26c6rnAwEBdu3bN2iEBAHAqSTZ8PYtz585p8uTJaty4sXr16qWYmBhFR0crOjpad+/eVdeuXfWf//xHU6dO1ZkzZ7Ro0SJ9/fXX6tmzp9X3srqi4ePjozNnzqhWrVopzp05c0Y+Pj5WBwEAAOxn8+bNSkxM1JYtW7Rly5Zk59q0aaN3331Xc+bM0fvvv68lS5aoUKFCev/9963eQ0N6hkSjRYsWmjFjhvLmzWtZGmMymfTzzz9rzpw5atWqldVBAADgTBz9ULXQ0FCFhoY+8Zq6deuqbt266b6X1YnGgAEDdPLkSQ0YMMDSp+natavu37+vqlWrqn///ukOCgCAzMycAeZo2IvViYa7u7sWLFigXbt2ae/evYqNjZWnp6eqV6+uevXqWbVbGAAAyNysTjR69Oihnj17qlatWqnO0wAAAE/m6NaJPVmdaBw+fJiqBQAA6eBMiYbVy1vr1KmjdevWKTEx0Yh4AABAJmJ1RSNr1qxat26dNm3apJIlS6Z4vonJZNKSJUtsFiAAAJlNRtqC3GhWJxpXr15V5cqVLe/N5uR/XH99DwAAkssIO4Pai9WJxrJly4yIAwAAp+FMczSsSjT+85//6Pfff1fRokVVtmxZo2ICAACZRJoSjTt37qhXr146cuSIzGazTCaTKleurGnTpil//vxGxwgAQKbiTBWNNK06mT59uo4dO6a+fftq/vz5GjZsmM6ePasxY8YYHR8AAJmO2YavjC5NFY3vv/9egwYNUrdu3ST9uf95vnz5NGTIEN2/fz/FyhMAAAApjRWN6OholStXLtmxoKAgPXr0SFeuXDEkMAAAMqskk+1eGV2aKhoPHz6Uu7t7smM5c+aUJMXHx9s+KgAAMjHmaFiBfTMAAMDfsXofjb/iuScAAFjHmX5FT3OiMW7cOOXIkcPy/nElY/To0cqePbvlOFuQAwDwZElOlGqkKdGoVq2apJRtktSO00oBAACPpSnRYNtxAABsx5kmg6Z7jgYAALCOM9X+STQAALAzZ6popHt5KwAAwN+hogEAgJ09Dzt62gqJBgAAduZMy1tpnQAAAMNQ0QAAwM6cp55BogEAgN2x6gQAAMAGqGgAAGBnzjQZlEQDAAA7c540g9YJAAAwEBUNAADszJkmg5JoAABgZ8zRAAAAhnGeNIM5GgAAwEBUNAAAsDPmaAAAAMOYnah5QusEAAAYhooGAAB2RusEAAAYxpmWt9I6AQAAhiHRgN30eLOTfjrynW7fOqWj/9mmsNBujg4JsKukpCR9umKNWvyzh16q/4pe7dZb/978XbJrtu7Yrdfe7Ktqjdqo+Wtvas6iSCUmJjooYhjFbMNXRkfrBHbx5hsdNW/u+5o5a6HWr/9GtWtX10fTJypbtmz6cPo8R4cH2MWsBcu0KHKN+vTsqvIBL2rnnoOKGP++XFxc1KJxsHbvP6wBIyaqWcO6GhD6hs6cO6/pcxcrNva2Rgzq7ejwYUPO1Doxmc1mp/i2ru4FHR2CU9u5/Ss9evRIwQ1etRxbvmy2gqpX0YulazowMuf24PJOR4fgNB7Exaluq476Z+uWGtKnp+V49z5DlZiQqMj5H2rouPd05OgxbVq1SFmyZJEkffjxp1q6cq32f/uF3Fz53dBe3PKUMHT8XsXa22yseb+tttlYRuB/tbCLrNmy6uqV68mO3bhxSz4+3g6KCLAvdzc3LZ/7gXy8cyY77ubqqnv3/pAkxccn6IVs2SxJhiTlyumpxMSHun//gXJ6edo1ZhjHmVadMEcDdjFz1kI1aVJPnTq9Ki8vTzVpXE+vd22v5ZFRjg4NsIssWbKodKniyuOTW2azWTE3b2nBslXae/CIOrzaSpLUsW0rnb90WZ+uWKM7d+/pp5+Pa/mqL1WnZjWSjEzGbMP/ZHRUNGAXn3/+perVramli2dajm3e/L0GDR7rwKgAx9j07XYNHfeeJKnuP6qrVdMGkqSglyrpzc7tNG32Qk2bvVCSFOBfUlPGDXNYrDAGFQ3Axr6IWqS2r7bUsIgJatCwrfr1H6mXXgrUys+ZCArnUz7AX4tnT9GIgWE6cvSYQgeNktls1vj3Z2lR5Br16tZRi2a+p4kjBun2nbsKHTRKD+LiHB028EyoaMBwNWtUVbNmDRTSa4gWffqZJGnHzr06d+6C1q9bppYtGmnDxm8dHCVgP0UKFVCRQgVUtVIFZc/uoZETp+ngkaNas26T3nr9n+ob8rrl2gpl/fVKl1B98e9v1Knd/zkwatjS89DysBWHJhpdu3aVyWRK07VLly41OBoYpUjRP1f87N5zINnxHTv3SpLKlvUn0UCmd/NWrHbuPajaNarKxzuX5XjZ0qUkSUeOHpfZbFblCmWTfa5k8aLKldNLp8+dt2e4MFhGbJ3MmzdPP/zwg5YtW2Y5NmrUKK1enXxVS8GCBfXdd9/99eN/y6Gtk9q1a+vgwYO6ceOGChYs+MQXnl8nTpyWJNWuFZTseK1/VJMknT13we4xAfYWH5+gkROnae36zcmO795/WJJUqUKAsmRx0aGffkl2/tz5S4q9fUeFCuS3W6xwPpGRkZo+fXqK4ydOnFBoaKh++OEHy2vNmjVWje3QikavXr2UI0cOTZs2TfPmzVOhQoUcGQ4McuTIL4pau0FT3x8rb++c2r//R5Ut668xowfr4KGf9OWXmxwdImC4/H551aZVE839dIVcXbMowL+kDv30ixYuX6VXWzVVtcoV1eW11lq84s9/if+jWmVdvnpdH38aqQJ+edXu/5o5+BvAlpIyyBZW165d09ixY7Vv3z4VK1Ys2Tmz2azTp08rJCREvr6+z3wPh8/R6Ny5s3bu3KkpU6ZoxowZjg4HBunSNVwjR/RXyFtdNW7sEF24cFlLlq7UhIkf6tGjR44OD7CLMUP6qFABP61Z97UuX70mv7y+6tOzq7p3bCtJGhLeU36+ebTqy41a8nmUfH1y6x/Vq6hfSDd5eeZwcPSwpYyRZki//PKL3NzctG7dOs2ePVu///675dyFCxd0//59lSiRvs3LMsTOoNevX9cvv/yi+vXrG3YPdgYFUmJnUCB1Ru8M2qXoq0+/KI2ulLr9xPNbt25N0zgRERH6/fffLXM0tmzZoj59+qhjx47asWOHXFxcVLduXQ0cOFCenmnf18XhFQ1Jyps3r/LmzevoMAAAsIvn4VknJ0+elIuLi/Lmzau5c+fqwoULmjJlik6dOqUlS5bIxSVt0zwzRKIBAIAzseXy1rRWLKwVFhamTp06ydv7z0dF+Pv7y9fXV6+99pqOHj2qwMDANI3Dhl0AACAFFxcXS5Lx2IsvvihJunr1atrHsWlUAADgqZJs+DLK0KFD1b1792THjh49KkkqVapUmsch0QAAwM6SZLbZyyhNmzbVnj17NGvWLF24cEHbt2/XiBEj1KpVK5UsWTLN4zBHAwAAO3setiBv2LChpk+frvnz5+uTTz6Rp6enXn75ZQ0YMMCqcTLE8lZ7YHkrkBLLW4HUGb28tV1R2z23Zs35dTYbywhUNAAAsLOM+KwTo5BoAABgZ07STJDEZFAAAGAgKhoAANjZ87AzqK2QaAAAYGfONEeD1gkAADAMFQ0AAOzsedhHw1ZINAAAsDNnmqNB6wQAABiGigYAAHbmTPtokGgAAGBnzrTqhEQDAAA7c6bJoMzRAAAAhqGiAQCAnTnTqhMSDQAA7MyZJoPSOgEAAIahogEAgJ3ROgEAAIZh1QkAAIANUNEAAMDOkpxoMiiJBgAAduY8aQatEwAAYCAqGgAA2BmrTgAAgGFINAAAgGHYGRQAAMAGqGgAAGBntE4AAIBh2BkUAADABqhoAABgZ840GZREAwAAO3OmORq0TgAAgGGoaAAAYGe0TgAAgGGcqXVCogEAgJ2xvBUAAMAGqGgAAGBnSczRAAAARqF1AgAAYANUNAAAsDNaJwAAwDC0TgAAAGyAigYAAHZG6wQAABiG1gkAAIANUNEAAMDOaJ0AAADDOFPrhEQDAAA7M5uTHB2C3TBHAwAAGIaKBgAAdpbkRK0TKhoAANiZ2Wy22ctW5s2bp65duyY7dvz4cXXp0kWVKlVSgwYNtHTpUqvHJdEAAMDJRUZGavr06cmO3bp1S2+88YaKFCmiqKgohYeHa+rUqYqKirJqbFonAADYWUZpnVy7dk1jx47Vvn37VKxYsWTnVq1aJTc3N40fP16urq4qWbKkzp8/r/nz56tt27ZpvgcVDQAA7CyjtE5++eUXubm5ad26dQoMDEx27uDBg6pevbpcXf9/TaJGjRr67bffFBMTk+Z7UNEAAOA51rBhwyee37p169+ea9CggRo0aJDquatXr8rf3z/Zsbx580qSrly5ojx58qQpPhINAADs7HnYGTQuLk7u7u7JjmXNmlWSFB8fn+ZxSDQAALAzW+4M+qSKRXpky5ZNCQkJyY49TjA8PDzSPA5zNAAAQAp+fn66fv16smOP3+fLly/N45BoAABgZxllMuiTVKtWTYcOHdKjR48sx/bu3avixYvLx8cnzeOQaAAAYGdJMtvsZZS2bdvq3r17GjlypE6fPq21a9dq8eLF6tWrl1XjMEcDAAA7M7ISYSs+Pj5asGCBJk2apDZt2sjX11dDhw5VmzZtrBrHZH4evq0NuLoXdHQIQIbz4PJOR4cAZEhueUoYOn4eL/+nX5RGMXdO2mwsI1DRAADAzp6H5a22QqIBAICdOUkzQRKTQQEAgIGoaAAAYGcZ5aFq9kCiAQCAndE6AQAAsAEqGgAA2BmrTgAAgGFs+VC1jI7WCQAAMAwVDQAA7IzWCQAAMIwzrToh0QAAwM6YowEAAGADVDQAALAzWicAAMAwzpRo0DoBAACGoaIBAICdOU89QzKZnal+AwAA7IrWCQAAMAyJBgAAMAyJBgAAMAyJBgAAMAyJBgAAMAyJBgAAMAyJBgAAMAyJBgAAMAyJBgAAMAyJBgAAMAyJBgAAMAyJBgAAMAyJBgAAMAyJBuwiKSlJM2bMUJ06dVSpUiW99dZbunjxoqPDAjKUefPmqWvXro4OA7ApEg3YxZw5c7RixQpNmDBBn3/+uZKSktSzZ08lJCQ4OjQgQ4iMjNT06dMdHQZgcyQaMFxCQoIWLVqkfv36KTg4WGXKlNGHH36oq1ev6ptvvnF0eIBDXbt2TaGhoZo6daqKFSvm6HAAmyPRgOF+/fVX/fHHH6pZs6blmJeXl8qWLasDBw44MDLA8X755Re5ublp3bp1CgwMdHQ4gM25OjoAZH5Xr16VJOXPnz/Z8bx581rOAc6qQYMGatCggaPDAAxDRQOGe/DggSTJ3d092fGsWbMqPj7eESEBAOyERAOGy5YtmySlmPgZHx+vF154wREhAQDshEQDhnvcMrl+/Xqy49evX1e+fPkcERIAwE5INGC4MmXKKEeOHNq3b5/l2J07d3Ts2DFVq1bNgZEBAIzGZFAYzt3dXV26dNHUqVOVO3duFSxYUO+//778/PzUpEkTR4cHADAQiQbsol+/fnr48KFGjRqluLg4VatWTQsXLpSbm5ujQwMAGMhkNpvNjg4CAABkTszRAAAAhiHRAAAAhiHRAAAAhiHRAAAAhiHRAAAAhiHRAAAAhiHRAAAAhiHRADIQtrUBkNmQaAA2MnjwYJUuXVqLFi2y+rNXr15VSEiIfv/9d5vHNXPmTJUuXfqJ15QuXVozZ85M970iIiLUoEGDdI+zb98+lS5dOtnzcQA8n0g0ABu4e/euvv32W/n7+2vlypVWVyZ2796t7du3GxQdADgOiQZgA//+978lSSNHjtRvv/2mvXv3OjgiAMgYSDQAG4iKilLNmjVVo0YNFS1aVJ9//nmKa7788ku1adNGgYGBCg4O1rRp05SQkKC1a9dq+PDhkqSGDRsqIiJCUurtjNTaIKtXr9arr76qSpUqqWLFinrllVe0adMmm3/HuLg4TZs2TU2aNFH58uVVpUoVvfHGGzp+/HiKa1euXKng4GBVrFhR3bp107Fjx5Kdv3z5sgYNGqTq1asrMDAw1Wv+eu9x48apbt26Kl++vJo1a6aFCxfa/DsCsD0SDSCdTp06paNHj6p169aSpNatW2vr1q2KiYmxXBMZGalhw4apXLlymjVrlkJCQrRs2TJNnDhRwcHBCgsLkyTNmjVLvXv3TvO9IyMjNWbMGDVq1Ejz5s3T1KlT5e7uriFDhujq1as2/Z5Dhw5VVFSUQkJCtGjRIg0fPlynTp3S4MGDk7WKrl69qlmzZmnAgAH64IMPdPv2bXXt2lWXL1+WJN28eVMdOnTQL7/8otGjR2vatGlKSkpS586ddebMmVTvPXnyZO3YsUPDhg3TwoUL1bBhQ02ZMkVRUVE2/Y4AbI/HxAPpFBUVpVy5clkmQbZp00YzZ87UmjVrFBoaqqSkJM2ePVuNGjXSxIkTLZ978OCBNmzYIE9PTxUpUkSSFBAQoEKFCqX53hcvXlSPHj2SJScFCxbUq6++qkOHDqlly5Y2+Y4JCQn6448/NGrUKLVo0UKSVL16dd27d0/vvvuuYmJi5OvrK0l69OiRZs+erYoVK0qSAgMD1ahRIy1btkzDhg3TkiVLFBsbq88++0wFCxaUJNWtW1ctWrTQRx99pBkzZqS4//79+1WrVi3L9wkKCpKHh4d8fHxs8v0AGIdEA0iHxMRErVu3To0aNVJcXJzi4uKUPXt2vfTSS1q1apVCQkJ07tw53bhxQ40bN0722R49eqhHjx7puv/jNsudO3d09uxZnT9/3rJSIyEhIV1j/y93d3dLq+LatWs6d+6cfvvtN33//fcp7lW4cGFLkiFJvr6+qlSpkg4cOCBJ2rNnjwICApQvXz49fPhQkuTi4qK6detq3bp1qd4/KChIn3/+ua5evap69eqpXr16Cg8Pt9n3A2AcEg0gHbZt26YbN25ozZo1WrNmTYrzO3fuVI4cOSTJkN++L1y4oDFjxmjPnj1yc3NTiRIlVKZMGUm235Nj586dmjx5ss6ePavs2bOrTJky8vDwSHGvPHnypPisj4+Prly5IkmKjY3V+fPnVa5cuVTv8+DBgxTHRo4cKT8/P61bt04TJkzQhAkTVLlyZY0bN87yfQFkTCQaQDpERUWpcOHCmjRpUrLjZrNZffr00eeff65BgwZJ+nNuwv+6deuWjh07psqVK//t+I8ePUr2/v79+5afk5KSFBISIjc3N61Zs0YBAQFydXXV6dOn9dVXX6X3qyVz4cIFhYeHW+aCFC5cWCaTSZGRkdq5c2eya2/fvp3i89HR0cqdO7ckydPTU9WrV9fQoUNTvZe7u3uqx8LCwhQWFqbLly/r+++/15w5czR48GBt2LDBBt8QgFGYDAo8o+joaO3cuVMtW7ZUUFBQsleNGjXUrFkzbd++XV5eXvL29ra0GR776quvFBISosTERLm4pPy/Yo4cOXTt2rVkxw4fPmz5+datWzp37pzatWunChUqyNX1z98bduzYIenPRMRWfv75Z8XHxyskJERFihSRyWSSJEuS8b8VjXPnzunChQuW91euXNGPP/6ooKAgSX/O7Th37pyKFy+uChUqWF5fffWV1qxZoyxZsiS7d1xcnJo2bWrZCK1AgQLq3LmzWrZsaZlgCiDjoqIBPKMvv/xSDx8+/NsJl61bt9bq1au1atUq9e3bV+PHj5ePj48aNGigc+fOacaMGercubNy5swpLy8vSdKWLVtUt25dlSxZUsHBwdqwYYMCAwNVtGhRrV27VufPn7eM7+Pjo4IFCyoyMlJ+fn7y8vLSzp07tXTpUkmptyCe5MiRI1q8eHGK43Xq1FG5cuXk6uqq999/X2+++aZlWe62bdskJa+0ZM2aVWFhYRo4cKAePXqkjz76SLly5VK3bt0kSd27d9dXX32l7t27680335S3t7c2btyoVatWWZb5/q9s2bJZVuu4ubmpdOnSOnfunL744gs1bdrUqu8IwP5MZh6uADyT5s2bK0uWLJbNuv7KbDarUaNGSkxM1Pfff69169Zp4cKF+u233+Tn56e2bdvqrbfekqurq/744w/16dNHBw4c0D/+8Q/Nnz9fMTExmjBhgnbs2CFXV1e1aNFC5cuX16hRo3TixAlJ0q+//qpJkybp559/lru7u0qVKqXQ0FBNnjxZ/v7++uijjzRz5kzNmjXL8pnUPGmL8ilTpuiVV17R119/rVmzZunChQvKmTOnKlWqpNdff11du3bV6NGj1blzZ0VEROj8+fNq2rSpFixYoLt376pmzZoaMWKEZWWN9GcrZtq0adqzZ4/i4+NVrFgxde3aVe3atZP05xbkr7/+upYuXaqgoCDdu3dP06dP19atWxUdHS0fHx+1aNFC/fv3V7Zs2Z7lHx8AOyHRAAAAhmGOBgAAMAyJBgAAMAyJBgAAMAyJBgAAMAyJBgAAMAyJBgAAMAyJBgAAMAyJBgAAMAyJBgAAMAyJBgAAMAyJBgAAMMz/A+7IMGPHFHJ9AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Evaluate the prediced labels with the original labels\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "\n",
    "print(confusion_matrix(y_test, dtree_preds))\n",
    "sns.heatmap(confusion_matrix(y_test, dtree_preds),\n",
    "            annot=True)\n",
    "plt.xlabel(\"Actual Labels\")\n",
    "plt.ylabel(\"Predicted Labels\")\n",
    "plt.title(\"Confusion Matrix\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "e50ef748",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.80      0.71      0.75        45\n",
      "           1       0.75      0.83      0.78        46\n",
      "\n",
      "    accuracy                           0.77        91\n",
      "   macro avg       0.77      0.77      0.77        91\n",
      "weighted avg       0.77      0.77      0.77        91\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, dtree_preds))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad4fe1cc",
   "metadata": {},
   "source": [
    "Decsion Tree Classifier is predicting the labels at the worst rate as compared to other two models. I think we can use the StandardScaler method to scale the data and retrain the model. May be it predicts good 🤔"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "406879f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "\n",
    "X_scaler_train = scaler.fit_transform(X_train)\n",
    "X_scaler_test = scaler.fit_transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "22f7257b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7912087912087912"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now, fit the scaled data to the DecsionTreeClassifier\n",
    "\n",
    "dtree.fit(X_scaler_train, y_train)\n",
    "dtree.score(X_scaler_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "407a347c",
   "metadata": {},
   "source": [
    "Infact, we have the slight decrement by using the standardscaler for this data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "8499e3bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0,\n",
       "       1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1,\n",
       "       1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1,\n",
       "       0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0,\n",
       "       1, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler_preds = dtree.predict(X_scaler_test)\n",
    "scaler_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "a5282e31",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[32 13]\n",
      " [ 8 38]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Confusion Matrix')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(confusion_matrix(y_test, dtree_preds))\n",
    "sns.heatmap(confusion_matrix(y_test, scaler_preds),\n",
    "            annot=True)\n",
    "plt.xlabel(\"Actual Labels\")\n",
    "plt.ylabel(\"Predicted Labels\")\n",
    "plt.title(\"Confusion Matrix\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "454633bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.84      0.71      0.77        45\n",
      "           1       0.75      0.87      0.81        46\n",
      "\n",
      "    accuracy                           0.79        91\n",
      "   macro avg       0.80      0.79      0.79        91\n",
      "weighted avg       0.80      0.79      0.79        91\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, scaler_preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "59c7d0e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vikra\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\base.py:486: UserWarning: X has feature names, but DecisionTreeClassifier was fitted without feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "model_scores = {\"Forest\": forest.score(X_test, y_test),\n",
    "                \"Randomized Forest\": randomized_model.score(X_test, y_test),\n",
    "                \"Logistic\": log_reg.score(X_test, y_test),\n",
    "                \"Grid Logistic\": grid_model.score(X_train, y_train),\n",
    "                \"DTree\": dtree.score(X_test, y_test),\n",
    "                \"DTree Scaler\": dtree.score(X_scaler_test, y_test)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "ca93d30b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Forest': 0.8351648351648352,\n",
       " 'Randomized Forest': 0.8131868131868132,\n",
       " 'Logistic': 0.8241758241758241,\n",
       " 'Grid Logistic': 0.9009433962264151,\n",
       " 'DTree': 0.6813186813186813,\n",
       " 'DTree Scaler': 0.7912087912087912}"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "3a49bcd4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 5))\n",
    "plt.bar(*zip(*model_scores.items()), color=[\"blue\", \"red\", \"green\", \"yellow\", \"black\", \"purple\"])\n",
    "plt.xlabel(\"Models\");\n",
    "plt.ylabel(\"Acuurary\");"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}