Untitled1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "da863bfe-7bf1-48f8-9d7c-683a12e50516",
   "metadata": {},
   "source": [
    "### HEART DISEASE PREDICTION USING DIFFERENT MACHINE LEARNING MODELS"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da902fb6-e63c-44b9-8a21-de8d37554c22",
   "metadata": {},
   "source": [
    "#### Data set feature explanation\n",
    "###### Sex              |  gender of the patient \n",
    "###### Age              |  age of the patient\n",
    "###### cp               |chest pain 1-Typical Angina 2-Atypical Angina 3-Non-typical Angina\n",
    "###### trestbps         | Resting blood pressure (in mm Hg on admission to the hospital) \n",
    "###### chol             |    serum  cholesterol in mg/dl \n",
    "###### fbs              |  (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false) \n",
    "###### restecg          |  resting ECG results (0: 'normal', 1 and 2: 'abnormal')\n",
    "###### thalach          |  maximum heart rate achieved\n",
    "###### thalach          |  maximum heart rate achieved \n",
    "###### exang            |  exercise induced angina (1 = yes; 0 = no)\n",
    "###### oldpeak          |  ST depression induced by exercise relative to rest\n",
    "###### slope            |  the slope of the peak exercise ST segment (1: 'upsloping', 2: 'flat', 3: 'down sloping)\n",
    "###### ca               |  number of major vessels (0-3) colored by fluoroscopy \n",
    "###### thal             |  thalassemia (3: 'normal', 6: 'fixed defect' , 7: 'reversible defect')\n",
    "###### num              |  angiographic disease status (0: no heart disease, > 0: heart disease)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33f9ccef-03dc-49b6-b493-4a472336e5f8",
   "metadata": {},
   "source": [
    "#### Importing Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1a4f2980-dc02-4b30-9d75-4beba53f3db3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np # for Array Manipulation and Preprocessing\n",
    "import pandas as pd # for Data wrangling and preprocessing\n",
    "import matplotlib.pyplot as plt #for data visualization\n",
    "import seaborn as sns #for data visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99be4f48-e861-404a-aebf-20cc1d7bbf61",
   "metadata": {},
   "source": [
    "#### Import Machine Learning Classification Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5c196c32-a0fd-4810-8eb5-f9f8f956e15f",
   "metadata": {},
   "outputs": [],
   "source": [
    "## No-Free lunch Theorem \n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1e213a4-ab69-4867-b0e2-d3f323020073",
   "metadata": {},
   "source": [
    "#### Importing Evaluation Matrics and Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fbdfdba7-07d0-44c0-ae35-67008e39569d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import f1_score,accuracy_score,precision_score,recall_score\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fbf05da9-faed-4691-8576-8bc5959cb2ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading Heart Disease Dataset with contains all the information for the Heart Disease Patient\n",
    "Data_set = pd.read_csv(\"heart-disease.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1e96ec90-6b2c-41b1-8984-ebde909226a2",
   "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>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>192</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>148</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>140</td>\n",
       "      <td>294</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>153</td>\n",
       "      <td>0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>263</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>173</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>172</td>\n",
       "      <td>199</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>162</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>150</td>\n",
       "      <td>168</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>174</td>\n",
       "      <td>0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "5   57    1   0       140   192    0        1      148      0      0.4      1   \n",
       "6   56    0   1       140   294    0        0      153      0      1.3      1   \n",
       "7   44    1   1       120   263    0        1      173      0      0.0      2   \n",
       "8   52    1   2       172   199    1        1      162      0      0.5      2   \n",
       "9   57    1   2       150   168    0        1      174      0      1.6      2   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   0     1       1  \n",
       "1   0     2       1  \n",
       "2   0     2       1  \n",
       "3   0     2       1  \n",
       "4   0     2       1  \n",
       "5   0     1       1  \n",
       "6   0     2       1  \n",
       "7   0     3       1  \n",
       "8   0     3       1  \n",
       "9   0     2       1  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking First 10 Rows of the dataset\n",
    "Data_set.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "14e0bc3c-6460-451b-887d-74cd4b5d8095",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age         0\n",
       "sex         0\n",
       "cp          0\n",
       "trestbps    0\n",
       "chol        0\n",
       "fbs         0\n",
       "restecg     0\n",
       "thalach     0\n",
       "exang       0\n",
       "oldpeak     0\n",
       "slope       0\n",
       "ca          0\n",
       "thal        0\n",
       "target      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking for null values\n",
    "Data_set.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "04479c9e-acb8-46fa-a222-5d450974a885",
   "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>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>54.366337</td>\n",
       "      <td>0.683168</td>\n",
       "      <td>0.966997</td>\n",
       "      <td>131.623762</td>\n",
       "      <td>246.264026</td>\n",
       "      <td>0.148515</td>\n",
       "      <td>0.528053</td>\n",
       "      <td>149.646865</td>\n",
       "      <td>0.326733</td>\n",
       "      <td>1.039604</td>\n",
       "      <td>1.399340</td>\n",
       "      <td>0.729373</td>\n",
       "      <td>2.313531</td>\n",
       "      <td>0.544554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.082101</td>\n",
       "      <td>0.466011</td>\n",
       "      <td>1.032052</td>\n",
       "      <td>17.538143</td>\n",
       "      <td>51.830751</td>\n",
       "      <td>0.356198</td>\n",
       "      <td>0.525860</td>\n",
       "      <td>22.905161</td>\n",
       "      <td>0.469794</td>\n",
       "      <td>1.161075</td>\n",
       "      <td>0.616226</td>\n",
       "      <td>1.022606</td>\n",
       "      <td>0.612277</td>\n",
       "      <td>0.498835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>47.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>211.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>133.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>55.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>153.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>61.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>274.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>77.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>564.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              age         sex          cp    trestbps        chol         fbs  \\\n",
       "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
       "mean    54.366337    0.683168    0.966997  131.623762  246.264026    0.148515   \n",
       "std      9.082101    0.466011    1.032052   17.538143   51.830751    0.356198   \n",
       "min     29.000000    0.000000    0.000000   94.000000  126.000000    0.000000   \n",
       "25%     47.500000    0.000000    0.000000  120.000000  211.000000    0.000000   \n",
       "50%     55.000000    1.000000    1.000000  130.000000  240.000000    0.000000   \n",
       "75%     61.000000    1.000000    2.000000  140.000000  274.500000    0.000000   \n",
       "max     77.000000    1.000000    3.000000  200.000000  564.000000    1.000000   \n",
       "\n",
       "          restecg     thalach       exang     oldpeak       slope          ca  \\\n",
       "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
       "mean     0.528053  149.646865    0.326733    1.039604    1.399340    0.729373   \n",
       "std      0.525860   22.905161    0.469794    1.161075    0.616226    1.022606   \n",
       "min      0.000000   71.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      0.000000  133.500000    0.000000    0.000000    1.000000    0.000000   \n",
       "50%      1.000000  153.000000    0.000000    0.800000    1.000000    0.000000   \n",
       "75%      1.000000  166.000000    1.000000    1.600000    2.000000    1.000000   \n",
       "max      2.000000  202.000000    1.000000    6.200000    2.000000    4.000000   \n",
       "\n",
       "             thal      target  \n",
       "count  303.000000  303.000000  \n",
       "mean     2.313531    0.544554  \n",
       "std      0.612277    0.498835  \n",
       "min      0.000000    0.000000  \n",
       "25%      2.000000    0.000000  \n",
       "50%      2.000000    1.000000  \n",
       "75%      3.000000    1.000000  \n",
       "max      3.000000    1.000000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking statistical relationship between numerical features\n",
    "Data_set.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "466d3b31-dc21-41bd-a5b2-8cbc72594ec8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.098447</td>\n",
       "      <td>-0.068653</td>\n",
       "      <td>0.279351</td>\n",
       "      <td>0.213678</td>\n",
       "      <td>0.121308</td>\n",
       "      <td>-0.116211</td>\n",
       "      <td>-0.398522</td>\n",
       "      <td>0.096801</td>\n",
       "      <td>0.210013</td>\n",
       "      <td>-0.168814</td>\n",
       "      <td>0.276326</td>\n",
       "      <td>0.068001</td>\n",
       "      <td>-0.225439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <td>-0.098447</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.049353</td>\n",
       "      <td>-0.056769</td>\n",
       "      <td>-0.197912</td>\n",
       "      <td>0.045032</td>\n",
       "      <td>-0.058196</td>\n",
       "      <td>-0.044020</td>\n",
       "      <td>0.141664</td>\n",
       "      <td>0.096093</td>\n",
       "      <td>-0.030711</td>\n",
       "      <td>0.118261</td>\n",
       "      <td>0.210041</td>\n",
       "      <td>-0.280937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cp</th>\n",
       "      <td>-0.068653</td>\n",
       "      <td>-0.049353</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.047608</td>\n",
       "      <td>-0.076904</td>\n",
       "      <td>0.094444</td>\n",
       "      <td>0.044421</td>\n",
       "      <td>0.295762</td>\n",
       "      <td>-0.394280</td>\n",
       "      <td>-0.149230</td>\n",
       "      <td>0.119717</td>\n",
       "      <td>-0.181053</td>\n",
       "      <td>-0.161736</td>\n",
       "      <td>0.433798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trestbps</th>\n",
       "      <td>0.279351</td>\n",
       "      <td>-0.056769</td>\n",
       "      <td>0.047608</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.123174</td>\n",
       "      <td>0.177531</td>\n",
       "      <td>-0.114103</td>\n",
       "      <td>-0.046698</td>\n",
       "      <td>0.067616</td>\n",
       "      <td>0.193216</td>\n",
       "      <td>-0.121475</td>\n",
       "      <td>0.101389</td>\n",
       "      <td>0.062210</td>\n",
       "      <td>-0.144931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chol</th>\n",
       "      <td>0.213678</td>\n",
       "      <td>-0.197912</td>\n",
       "      <td>-0.076904</td>\n",
       "      <td>0.123174</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.013294</td>\n",
       "      <td>-0.151040</td>\n",
       "      <td>-0.009940</td>\n",
       "      <td>0.067023</td>\n",
       "      <td>0.053952</td>\n",
       "      <td>-0.004038</td>\n",
       "      <td>0.070511</td>\n",
       "      <td>0.098803</td>\n",
       "      <td>-0.085239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fbs</th>\n",
       "      <td>0.121308</td>\n",
       "      <td>0.045032</td>\n",
       "      <td>0.094444</td>\n",
       "      <td>0.177531</td>\n",
       "      <td>0.013294</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.084189</td>\n",
       "      <td>-0.008567</td>\n",
       "      <td>0.025665</td>\n",
       "      <td>0.005747</td>\n",
       "      <td>-0.059894</td>\n",
       "      <td>0.137979</td>\n",
       "      <td>-0.032019</td>\n",
       "      <td>-0.028046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>restecg</th>\n",
       "      <td>-0.116211</td>\n",
       "      <td>-0.058196</td>\n",
       "      <td>0.044421</td>\n",
       "      <td>-0.114103</td>\n",
       "      <td>-0.151040</td>\n",
       "      <td>-0.084189</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.044123</td>\n",
       "      <td>-0.070733</td>\n",
       "      <td>-0.058770</td>\n",
       "      <td>0.093045</td>\n",
       "      <td>-0.072042</td>\n",
       "      <td>-0.011981</td>\n",
       "      <td>0.137230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thalach</th>\n",
       "      <td>-0.398522</td>\n",
       "      <td>-0.044020</td>\n",
       "      <td>0.295762</td>\n",
       "      <td>-0.046698</td>\n",
       "      <td>-0.009940</td>\n",
       "      <td>-0.008567</td>\n",
       "      <td>0.044123</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.378812</td>\n",
       "      <td>-0.344187</td>\n",
       "      <td>0.386784</td>\n",
       "      <td>-0.213177</td>\n",
       "      <td>-0.096439</td>\n",
       "      <td>0.421741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>exang</th>\n",
       "      <td>0.096801</td>\n",
       "      <td>0.141664</td>\n",
       "      <td>-0.394280</td>\n",
       "      <td>0.067616</td>\n",
       "      <td>0.067023</td>\n",
       "      <td>0.025665</td>\n",
       "      <td>-0.070733</td>\n",
       "      <td>-0.378812</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.288223</td>\n",
       "      <td>-0.257748</td>\n",
       "      <td>0.115739</td>\n",
       "      <td>0.206754</td>\n",
       "      <td>-0.436757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oldpeak</th>\n",
       "      <td>0.210013</td>\n",
       "      <td>0.096093</td>\n",
       "      <td>-0.149230</td>\n",
       "      <td>0.193216</td>\n",
       "      <td>0.053952</td>\n",
       "      <td>0.005747</td>\n",
       "      <td>-0.058770</td>\n",
       "      <td>-0.344187</td>\n",
       "      <td>0.288223</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.577537</td>\n",
       "      <td>0.222682</td>\n",
       "      <td>0.210244</td>\n",
       "      <td>-0.430696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>slope</th>\n",
       "      <td>-0.168814</td>\n",
       "      <td>-0.030711</td>\n",
       "      <td>0.119717</td>\n",
       "      <td>-0.121475</td>\n",
       "      <td>-0.004038</td>\n",
       "      <td>-0.059894</td>\n",
       "      <td>0.093045</td>\n",
       "      <td>0.386784</td>\n",
       "      <td>-0.257748</td>\n",
       "      <td>-0.577537</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.080155</td>\n",
       "      <td>-0.104764</td>\n",
       "      <td>0.345877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ca</th>\n",
       "      <td>0.276326</td>\n",
       "      <td>0.118261</td>\n",
       "      <td>-0.181053</td>\n",
       "      <td>0.101389</td>\n",
       "      <td>0.070511</td>\n",
       "      <td>0.137979</td>\n",
       "      <td>-0.072042</td>\n",
       "      <td>-0.213177</td>\n",
       "      <td>0.115739</td>\n",
       "      <td>0.222682</td>\n",
       "      <td>-0.080155</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.151832</td>\n",
       "      <td>-0.391724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thal</th>\n",
       "      <td>0.068001</td>\n",
       "      <td>0.210041</td>\n",
       "      <td>-0.161736</td>\n",
       "      <td>0.062210</td>\n",
       "      <td>0.098803</td>\n",
       "      <td>-0.032019</td>\n",
       "      <td>-0.011981</td>\n",
       "      <td>-0.096439</td>\n",
       "      <td>0.206754</td>\n",
       "      <td>0.210244</td>\n",
       "      <td>-0.104764</td>\n",
       "      <td>0.151832</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.344029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <td>-0.225439</td>\n",
       "      <td>-0.280937</td>\n",
       "      <td>0.433798</td>\n",
       "      <td>-0.144931</td>\n",
       "      <td>-0.085239</td>\n",
       "      <td>-0.028046</td>\n",
       "      <td>0.137230</td>\n",
       "      <td>0.421741</td>\n",
       "      <td>-0.436757</td>\n",
       "      <td>-0.430696</td>\n",
       "      <td>0.345877</td>\n",
       "      <td>-0.391724</td>\n",
       "      <td>-0.344029</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               age       sex        cp  trestbps      chol       fbs  \\\n",
       "age       1.000000 -0.098447 -0.068653  0.279351  0.213678  0.121308   \n",
       "sex      -0.098447  1.000000 -0.049353 -0.056769 -0.197912  0.045032   \n",
       "cp       -0.068653 -0.049353  1.000000  0.047608 -0.076904  0.094444   \n",
       "trestbps  0.279351 -0.056769  0.047608  1.000000  0.123174  0.177531   \n",
       "chol      0.213678 -0.197912 -0.076904  0.123174  1.000000  0.013294   \n",
       "fbs       0.121308  0.045032  0.094444  0.177531  0.013294  1.000000   \n",
       "restecg  -0.116211 -0.058196  0.044421 -0.114103 -0.151040 -0.084189   \n",
       "thalach  -0.398522 -0.044020  0.295762 -0.046698 -0.009940 -0.008567   \n",
       "exang     0.096801  0.141664 -0.394280  0.067616  0.067023  0.025665   \n",
       "oldpeak   0.210013  0.096093 -0.149230  0.193216  0.053952  0.005747   \n",
       "slope    -0.168814 -0.030711  0.119717 -0.121475 -0.004038 -0.059894   \n",
       "ca        0.276326  0.118261 -0.181053  0.101389  0.070511  0.137979   \n",
       "thal      0.068001  0.210041 -0.161736  0.062210  0.098803 -0.032019   \n",
       "target   -0.225439 -0.280937  0.433798 -0.144931 -0.085239 -0.028046   \n",
       "\n",
       "           restecg   thalach     exang   oldpeak     slope        ca  \\\n",
       "age      -0.116211 -0.398522  0.096801  0.210013 -0.168814  0.276326   \n",
       "sex      -0.058196 -0.044020  0.141664  0.096093 -0.030711  0.118261   \n",
       "cp        0.044421  0.295762 -0.394280 -0.149230  0.119717 -0.181053   \n",
       "trestbps -0.114103 -0.046698  0.067616  0.193216 -0.121475  0.101389   \n",
       "chol     -0.151040 -0.009940  0.067023  0.053952 -0.004038  0.070511   \n",
       "fbs      -0.084189 -0.008567  0.025665  0.005747 -0.059894  0.137979   \n",
       "restecg   1.000000  0.044123 -0.070733 -0.058770  0.093045 -0.072042   \n",
       "thalach   0.044123  1.000000 -0.378812 -0.344187  0.386784 -0.213177   \n",
       "exang    -0.070733 -0.378812  1.000000  0.288223 -0.257748  0.115739   \n",
       "oldpeak  -0.058770 -0.344187  0.288223  1.000000 -0.577537  0.222682   \n",
       "slope     0.093045  0.386784 -0.257748 -0.577537  1.000000 -0.080155   \n",
       "ca       -0.072042 -0.213177  0.115739  0.222682 -0.080155  1.000000   \n",
       "thal     -0.011981 -0.096439  0.206754  0.210244 -0.104764  0.151832   \n",
       "target    0.137230  0.421741 -0.436757 -0.430696  0.345877 -0.391724   \n",
       "\n",
       "              thal    target  \n",
       "age       0.068001 -0.225439  \n",
       "sex       0.210041 -0.280937  \n",
       "cp       -0.161736  0.433798  \n",
       "trestbps  0.062210 -0.144931  \n",
       "chol      0.098803 -0.085239  \n",
       "fbs      -0.032019 -0.028046  \n",
       "restecg  -0.011981  0.137230  \n",
       "thalach  -0.096439  0.421741  \n",
       "exang     0.206754 -0.436757  \n",
       "oldpeak   0.210244 -0.430696  \n",
       "slope    -0.104764  0.345877  \n",
       "ca        0.151832 -0.391724  \n",
       "thal      1.000000 -0.344029  \n",
       "target   -0.344029  1.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking Correlation between the features\n",
    "Data_set.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2f474d18-37d7-4ba4-9a26-689f779e4d30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualizing Correlation in heatmap Graph\n",
    "fig,ax = plt.subplots(figsize=(30,15))\n",
    "ax = sns.heatmap(Data_set.corr(),annot=True,linewidths=.5,fmt=\".2f\",cmap=\"YlGnBu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1bb66172-4913-4403-b523-343d729b362b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dividing the data into target and features \n",
    "X = Data_set.drop(columns=\"target\",axis=1)\n",
    "Y = Data_set[\"target\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "81cbc6db-93c1-493e-99f5-ea06a6ed6ebf",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Spliting the data into train and test \n",
    "x_train,x_test,y_train,y_test = train_test_split(X,Y,test_size=0.25,stratify=Y,random_state=1234)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7aaa0a1b-91c3-4496-a83e-bf716bd56b6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((227, 13), (227,), (76, 13), (76,))"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking the shape of the train and test data\n",
    "x_train.shape,y_train.shape,x_test.shape,y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "dee2db8d-7610-4a2a-9642-6ffd3863d851",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Loading the models into a single list name models\n",
    "models = [KNeighborsClassifier(),SVC(),GaussianNB(),LogisticRegression(),RandomForestClassifier(),DecisionTreeClassifier()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "12884870-4ded-49bf-9216-03e0a37def6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def tr_ts_ac_comp():\n",
    "    for model in models:\n",
    "        model.fit(x_train,y_train)\n",
    "        predicted = model.predict(x_test)\n",
    "        acc_sco = accuracy_score(y_test,predicted)\n",
    "        print(\"The Acuuracy Score for the \",model, \" = \" ,acc_sco,\"For Train Test Split\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7d7db9a3-4889-47d3-910f-67d842bc4ee1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Acuuracy Score for the  KNeighborsClassifier()  =  0.6578947368421053 For Train Test Split\n",
      "The Acuuracy Score for the  SVC()  =  0.631578947368421 For Train Test Split\n",
      "The Acuuracy Score for the  GaussianNB()  =  0.8289473684210527 For Train Test Split\n",
      "The Acuuracy Score for the  LogisticRegression()  =  0.868421052631579 For Train Test Split\n",
      "The Acuuracy Score for the  RandomForestClassifier()  =  0.8421052631578947 For Train Test Split\n",
      "The Acuuracy Score for the  DecisionTreeClassifier()  =  0.7894736842105263 For Train Test Split\n"
     ]
    }
   ],
   "source": [
    "#Calling the Fuction to see the scores for the different Algorithm   \n",
    "tr_ts_ac_comp()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d0c634b-68a3-4e9d-a9bc-17cd26e773e8",
   "metadata": {},
   "source": [
    "##### As we see the Logistics Regression has high score and the svc has very low but let split the data into k fold to see the result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "415f9d10-930c-48ec-8472-36d2ad87c82d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def k_fold_split():\n",
    "    for model in models:\n",
    "        cv_score = cross_val_score(model,X,Y,cv=5)\n",
    "        mean_cv_score = sum(cv_score)/ len(cv_score)\n",
    "        mean_cv_score = mean_cv_score * 100\n",
    "        mean_cv_score = round(mean_cv_score,2)\n",
    "        print(\"The Cross Validation Score for the \",model, \"=\", cv_score, \"and the mean score is \", mean_cv_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2bbceec0-1738-4c24-b5ea-71081c4ed754",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Cross Validation Score for the  KNeighborsClassifier() = [0.60655738 0.6557377  0.57377049 0.73333333 0.65      ] and the mean score is  64.39\n",
      "The Cross Validation Score for the  SVC() = [0.60655738 0.60655738 0.73770492 0.68333333 0.58333333] and the mean score is  64.35\n",
      "The Cross Validation Score for the  GaussianNB() = [0.80327869 0.8852459  0.78688525 0.83333333 0.71666667] and the mean score is  80.51\n",
      "The Cross Validation Score for the  LogisticRegression() = [0.81967213 0.86885246 0.85245902 0.83333333 0.75      ] and the mean score is  82.49\n",
      "The Cross Validation Score for the  RandomForestClassifier() = [0.80327869 0.85245902 0.78688525 0.83333333 0.75      ] and the mean score is  80.52\n",
      "The Cross Validation Score for the  DecisionTreeClassifier() = [0.75409836 0.78688525 0.75409836 0.73333333 0.71666667] and the mean score is  74.9\n"
     ]
    }
   ],
   "source": [
    "#Calling the kfold function\n",
    "k_fold_split()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e011b4d-578b-4ab4-a729-5c63b6db5a4f",
   "metadata": {},
   "source": [
    "##### Again even with the kfold split the Logistic Regression has best mean Score So let try Hyperparameter Tunning to find the best Parameter for the Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a707f624-112e-45b6-b160-0ba5deb228b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Loading the Logictics Regression Parameter into a Dictionary\n",
    "params = {\n",
    "    \"penalty\":[\"none\",\"l1\",\"l2\"],\n",
    "    \"solver\":[\"lbfgs\",\"liblinear\",\"newton-cg\",\"sag\",\"saga\"],\n",
    "    \"C\":[1,2,3,4,5]\n",
    "        \n",
    "\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4fdbf603-7638-4635-9aca-9e48f7382081",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Start with Grid Search\n",
    "GRD = GridSearchCV(LogisticRegression(),params,cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "15030a0e-f3ed-404d-9a27-c62e1f9150fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=LogisticRegression(),\n",
       "             param_grid={'C': [1, 2, 3, 4, 5], 'penalty': ['none', 'l1', 'l2'],\n",
       "                         'solver': ['lbfgs', 'liblinear', 'newton-cg', 'sag',\n",
       "                                    'saga']})"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#fitting the data\n",
    "GRD.fit(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "05ac4f34-bc58-4b7f-bdd2-dce9561680d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8348633879781421"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking for best score\n",
    "GRD.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0daa87e7-7f98-4e27-b52a-6993abe38201",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 3, 'penalty': 'l2', 'solver': 'newton-cg'}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking for best parameters\n",
    "GRD.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "dad64dc4-d46c-4b73-a64b-9d062f31a651",
   "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>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_C</th>\n",
       "      <th>param_penalty</th>\n",
       "      <th>param_solver</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>split3_test_score</th>\n",
       "      <th>split4_test_score</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.049753</td>\n",
       "      <td>0.017429</td>\n",
       "      <td>0.011469</td>\n",
       "      <td>0.005879</td>\n",
       "      <td>1</td>\n",
       "      <td>none</td>\n",
       "      <td>lbfgs</td>\n",
       "      <td>{'C': 1, 'penalty': 'none', 'solver': 'lbfgs'}</td>\n",
       "      <td>0.836066</td>\n",
       "      <td>0.868852</td>\n",
       "      <td>0.819672</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.821585</td>\n",
       "      <td>0.039268</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.001399</td>\n",
       "      <td>0.001020</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>none</td>\n",
       "      <td>liblinear</td>\n",
       "      <td>{'C': 1, 'penalty': 'none', 'solver': 'libline...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.071876</td>\n",
       "      <td>0.007654</td>\n",
       "      <td>0.003125</td>\n",
       "      <td>0.006250</td>\n",
       "      <td>1</td>\n",
       "      <td>none</td>\n",
       "      <td>newton-cg</td>\n",
       "      <td>{'C': 1, 'penalty': 'none', 'solver': 'newton-...</td>\n",
       "      <td>0.803279</td>\n",
       "      <td>0.885246</td>\n",
       "      <td>0.819672</td>\n",
       "      <td>0.866667</td>\n",
       "      <td>0.766667</td>\n",
       "      <td>0.828306</td>\n",
       "      <td>0.042928</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.034375</td>\n",
       "      <td>0.037499</td>\n",
       "      <td>0.003125</td>\n",
       "      <td>0.006250</td>\n",
       "      <td>1</td>\n",
       "      <td>none</td>\n",
       "      <td>sag</td>\n",
       "      <td>{'C': 1, 'penalty': 'none', 'solver': 'sag'}</td>\n",
       "      <td>0.672131</td>\n",
       "      <td>0.704918</td>\n",
       "      <td>0.754098</td>\n",
       "      <td>0.766667</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.712896</td>\n",
       "      <td>0.041113</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.015625</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>none</td>\n",
       "      <td>saga</td>\n",
       "      <td>{'C': 1, 'penalty': 'none', 'solver': 'saga'}</td>\n",
       "      <td>0.672131</td>\n",
       "      <td>0.672131</td>\n",
       "      <td>0.770492</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.683333</td>\n",
       "      <td>0.709617</td>\n",
       "      <td>0.042042</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_fit_time  std_fit_time  mean_score_time  std_score_time param_C  \\\n",
       "0       0.049753      0.017429         0.011469        0.005879       1   \n",
       "1       0.001399      0.001020         0.000000        0.000000       1   \n",
       "2       0.071876      0.007654         0.003125        0.006250       1   \n",
       "3       0.034375      0.037499         0.003125        0.006250       1   \n",
       "4       0.015625      0.000002         0.000000        0.000000       1   \n",
       "\n",
       "  param_penalty param_solver  \\\n",
       "0          none        lbfgs   \n",
       "1          none    liblinear   \n",
       "2          none    newton-cg   \n",
       "3          none          sag   \n",
       "4          none         saga   \n",
       "\n",
       "                                              params  split0_test_score  \\\n",
       "0     {'C': 1, 'penalty': 'none', 'solver': 'lbfgs'}           0.836066   \n",
       "1  {'C': 1, 'penalty': 'none', 'solver': 'libline...                NaN   \n",
       "2  {'C': 1, 'penalty': 'none', 'solver': 'newton-...           0.803279   \n",
       "3       {'C': 1, 'penalty': 'none', 'solver': 'sag'}           0.672131   \n",
       "4      {'C': 1, 'penalty': 'none', 'solver': 'saga'}           0.672131   \n",
       "\n",
       "   split1_test_score  split2_test_score  split3_test_score  split4_test_score  \\\n",
       "0           0.868852           0.819672           0.833333           0.750000   \n",
       "1                NaN                NaN                NaN                NaN   \n",
       "2           0.885246           0.819672           0.866667           0.766667   \n",
       "3           0.704918           0.754098           0.766667           0.666667   \n",
       "4           0.672131           0.770492           0.750000           0.683333   \n",
       "\n",
       "   mean_test_score  std_test_score  rank_test_score  \n",
       "0         0.821585        0.039268               19  \n",
       "1              NaN             NaN               56  \n",
       "2         0.828306        0.042928                7  \n",
       "3         0.712896        0.041113               40  \n",
       "4         0.709617        0.042042               41  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Complete cross validation score and parameters in Dataframe\n",
    "CV_Results= pd.DataFrame(GRD.cv_results_)\n",
    "CV_Results.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "269079d2-66bb-4c24-80ff-ef26f5102601",
   "metadata": {},
   "outputs": [],
   "source": [
    "GridSerch = CV_Results[[\"param_C\",\"param_penalty\",\"param_solver\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5c9a2159-0798-44ea-8b9e-cbb07868c417",
   "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>param_C</th>\n",
       "      <th>param_penalty</th>\n",
       "      <th>param_solver</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>none</td>\n",
       "      <td>lbfgs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>none</td>\n",
       "      <td>liblinear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>none</td>\n",
       "      <td>newton-cg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>none</td>\n",
       "      <td>sag</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>none</td>\n",
       "      <td>saga</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>5</td>\n",
       "      <td>l2</td>\n",
       "      <td>lbfgs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>5</td>\n",
       "      <td>l2</td>\n",
       "      <td>liblinear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>5</td>\n",
       "      <td>l2</td>\n",
       "      <td>newton-cg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>5</td>\n",
       "      <td>l2</td>\n",
       "      <td>sag</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>5</td>\n",
       "      <td>l2</td>\n",
       "      <td>saga</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>75 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   param_C param_penalty param_solver\n",
       "0        1          none        lbfgs\n",
       "1        1          none    liblinear\n",
       "2        1          none    newton-cg\n",
       "3        1          none          sag\n",
       "4        1          none         saga\n",
       "..     ...           ...          ...\n",
       "70       5            l2        lbfgs\n",
       "71       5            l2    liblinear\n",
       "72       5            l2    newton-cg\n",
       "73       5            l2          sag\n",
       "74       5            l2         saga\n",
       "\n",
       "[75 rows x 3 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GridSerch"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1046623-736c-4982-aa9f-471cca5b030d",
   "metadata": {},
   "source": [
    "##### So now we now our best parameter let predict our case study "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "37a269dc-2c8e-4142-9e38-c60cedcf7d85",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = GRD.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "94d005af-d475-4fb7-812a-f0f403dbfa25",
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction = GRD.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ce6cb378-7979-4a4d-ad86-c02f8549daab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8552631578947368"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test,prediction)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f70a17d-00e5-4035-bcb6-ceb702f1f1ed",
   "metadata": {},
   "source": [
    "##### Making Predictive System"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "37ca78b2-2b93-405c-9d2e-5861ee8e1603",
   "metadata": {},
   "outputs": [],
   "source": [
    "Features = np.array([[60,1,0,145,282,0,0,142,1,2.8,1,2,3]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "1c754b18-dc99-4092-b61c-e05bdd3ad835",
   "metadata": {},
   "outputs": [],
   "source": [
    "pree = model.predict(Features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "d28c1374-e7a1-428e-8145-ec9cb7c1e090",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No Heart Disease\n"
     ]
    }
   ],
   "source": [
    "if pree[0] == 0:\n",
    "    print(\"No Heart Disease\")\n",
    "else:\n",
    "    print(\"You Have Heart Disease\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "be91ac7f-2c1c-412f-ab45-808b4875d77d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "c7696fd1-7763-476c-90cf-67868a1fe276",
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = \"trained_pmodel.sav\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "36c7c595-cfe5-4a2f-9c60-2edf2a5671d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(model,open(filename,\"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "2504a9fd-b993-45d2-8a33-a923ee4d40ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "loaded_model = pickle.load(open(\"trained_pmodel.sav\",\"rb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "81b1ad3b-e79d-42b7-8293-d07de7bdda12",
   "metadata": {},
   "outputs": [],
   "source": [
    "mod = loaded_model.predict(Features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "03f65e50-a785-4b53-b152-1a6ed1508c06",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No Heart Disease\n"
     ]
    }
   ],
   "source": [
    "if mod[0] == 0:\n",
    "    print(\"No Heart Disease\")\n",
    "else:\n",
    "    print(\"You Have Heart Disease\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5d0d75de-e3a3-4ce0-8511-ffd4184d409f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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