Logistic Regression.ipynb

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  {
   "cell_type": "markdown",
   "id": "b98bdc1a",
   "metadata": {},
   "source": [
    "# Table of Content\n",
    "<hr style=\"height: 5px\">"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4592de8",
   "metadata": {},
   "source": [
    "# [1. WorkFlow](#wf)\n",
    "\n",
    "# [2. What is Logistic Regression](#lg)\n",
    "\n",
    "# [3. Importing Library](#ib)\n",
    "\n",
    "# [4. Loading Dataset](#ld)\n",
    "\n",
    "# [5. Preprocing Dataset](#pd)\n",
    "\n",
    "# [6. Preparing Dataset](#ppd)\n",
    "\n",
    "# [7. Visualization of Training Data](#vt)\n",
    "\n",
    "# [8. Model Building](#md)\n",
    "\n",
    "# [9. Model Evaluation](#me)\n",
    "\n",
    "# [10. Building Predictive System](#bp)\n",
    "\n",
    "# [11. Conclusion](#c)\n",
    "<hr style=\"height: 5px\">"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7bce59b3",
   "metadata": {},
   "source": [
    "<a name='wf'></a>\n",
    " # 1. Work Flow"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4167237f",
   "metadata": {},
   "source": [
    "### Heart Data --->  Data Preprocessing ---> Train Test Split ---> Model Fiting ---> Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97219dcc",
   "metadata": {},
   "source": [
    "<a name='lg'></a>\n",
    "# 2. What is Logistic Regression?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f989f87",
   "metadata": {},
   "source": [
    "### Logistic regression is a supervised learning classification algorithm used to predict the probability of a target variable. \n",
    "### It is one of the simplest ML algorithms that can be used for various classification problems such as spam detection, Diabetes prediction, cancer detection etc. \n",
    "### Logistic regression predicts the output of a categorical dependent variable. Therefore the outcome must be a categorical or discrete value. It can be either Yes or No, 0 or 1, true or False, etc. but instead of giving the exact value as 0 and 1, it gives the probabilistic values which lie between 0 and 1.\n",
    "### In Logistic regression, instead of fitting a regression line, we fit an \"S\" shaped logistic function, which predicts two maximum values (0 or 1).\n",
    "### The curve from the logistic function indicates the likelihood of something such as whether the cells are cancerous or not, a mouse is obese or not based on its weight, etc.\n",
    "### Logistic Regression is a significant machine learning algorithm because it has the ability to provide probabilities and classify new data using continuous and discrete datasets.\n",
    "\n",
    "### Here is quick comparision between Linear Regreesion  and Logistic Regression\n"
   ]
  },
  {
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    }
   },
   "cell_type": "markdown",
   "id": "c1e18899",
   "metadata": {},
   "source": [
    " </img>![Logistic%20Regression.jpeg](attachment:Logistic%20Regression.jpeg)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c15091c",
   "metadata": {},
   "source": [
    "<a name='ib'></a>\n",
    "# Importing Library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "b618f4ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd  #Pandas is used to analyze data\n",
    "import numpy as np   #This library contains a large number of mathematical, algebraic, and transformation functions\n",
    "import matplotlib.pyplot as plt   # Used for Visualization of data\n",
    "import seaborn as sns     #Seaborn is a Python data visualization library based on matplotlib\n",
    "from sklearn.model_selection import train_test_split  # Used for splitting data into train data and test data\n",
    "from sklearn.linear_model import LogisticRegression   # Importing Logistic Regression model\n",
    "from sklearn.metrics import accuracy_score # To Calculate Accuracy classification score\n",
    "import pickle # to save the model for later use\n",
    "#matplotlib inline sets the backend of matplotlib to the 'inline' backend\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0915d42",
   "metadata": {},
   "source": [
    "<a name='ld'></a>\n",
    "#  Loading dataset"
   ]
  },
  {
   "cell_type": "raw",
   "id": "73952e51",
   "metadata": {},
   "source": [
    "# About DataSet\n",
    "\n",
    "There are 13 attributes:\n",
    "\n",
    "1. Age: Age (in years)\n",
    "\n",
    "2. Sex: gender (1 = male; 0 = female)\n",
    "\n",
    "3.ChestPain: Chest Pain type\n",
    "\n",
    "-- 1: typical angina (all criteria present)\n",
    "-- 2: atypical angina (two of three criteria satisfied)\n",
    "-- 3: non-anginal pain (less than one criteria satisfied)\n",
    "-- 4: asymptomatic (none of the criteria are satisfied)\n",
    "\n",
    "4. Restbps: Resting Blood pressure (in mmHg, upon admission to the hospital)\n",
    "\n",
    "5. Chol: serum cholesterol in mg/dL\n",
    "\n",
    "6. Fbs: fasting blood sugar > 120 mg/dL (likely to be diabetic) 1 = true; 0 = false\n",
    "\n",
    "7. RestECG: Resting electrocardiogram 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",
    "\n",
    "8. MaxHR: Greatest number of beats per minute your heart can possibly reach during all-out strenuous exercise.\n",
    "\n",
    "9.Exang: exercise induced angina (1 = yes; 0 = no)\n",
    "\n",
    "10. Oldpeak: ST depression induced by exercise relative to rest (in mm, achieved by subtracting the lowest ST segment points during exercise and rest)\n",
    "\n",
    "11. Slope: the slope of the peak exercise ST segment, ST-T abnormalities are considered to be a crucial indicator for identifying presence of ischaemia \n",
    "\n",
    "-- Value 1: upsloping\n",
    "\n",
    "-- Value 2: flat\n",
    "\n",
    "-- Value 3: downsloping\n",
    "\n",
    "12.Ca: number of major vessels (0-3) colored by fluoroscopy. Major cardial vessels are as goes: aorta, superior vena cava, inferior vena cava, pulmonary artery (oxygen-poor blood --> lungs), pulmonary veins (oxygen-rich blood --> heart), and coronary arteries (supplies blood to heart tissue).\n",
    "\n",
    "\n",
    "13. AHD: 0 = normal; 1 = fixed defect (heart tissue can't absorb thallium both under stress and in rest); 2 = reversible defect (heart tissue is unable to absorb thallium only under the exercise portion of the test)\n",
    "\n",
    "14.AHD: 0 = no disease, 1 = disease\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "af7c2730",
   "metadata": {},
   "outputs": [],
   "source": [
    "# loading dataset which is in csv format using pd.read_csv(filename_with_extension)\n",
    "\n",
    "df=pd.read_csv('Data/heart_disease.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2a984995",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(303, 14)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Printing number of rows and column using shape\n",
    "\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "130483f8",
   "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>ChestPain</th>\n",
       "      <th>RestBP</th>\n",
       "      <th>Chol</th>\n",
       "      <th>Fbs</th>\n",
       "      <th>RestECG</th>\n",
       "      <th>MaxHR</th>\n",
       "      <th>ExAng</th>\n",
       "      <th>Oldpeak</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Ca</th>\n",
       "      <th>Thal</th>\n",
       "      <th>AHD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>typical</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>fixed</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>asymptomatic</td>\n",
       "      <td>160</td>\n",
       "      <td>286</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>108</td>\n",
       "      <td>1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>normal</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>asymptomatic</td>\n",
       "      <td>120</td>\n",
       "      <td>229</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>129</td>\n",
       "      <td>1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>reversable</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>nonanginal</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>nontypical</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Sex     ChestPain  RestBP  Chol  Fbs  RestECG  MaxHR  ExAng  Oldpeak  \\\n",
       "1   63    1       typical     145   233    1        2    150      0      2.3   \n",
       "2   67    1  asymptomatic     160   286    0        2    108      1      1.5   \n",
       "3   67    1  asymptomatic     120   229    0        2    129      1      2.6   \n",
       "4   37    1    nonanginal     130   250    0        0    187      0      3.5   \n",
       "5   41    0    nontypical     130   204    0        2    172      0      1.4   \n",
       "\n",
       "   Slope   Ca        Thal  AHD  \n",
       "1      3  0.0       fixed   No  \n",
       "2      2  3.0      normal  Yes  \n",
       "3      2  2.0  reversable  Yes  \n",
       "4      3  0.0      normal   No  \n",
       "5      1  0.0      normal   No  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Printing First 5 rows of the dataset using head()\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "df8c8cfc",
   "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>ChestPain</th>\n",
       "      <th>RestBP</th>\n",
       "      <th>Chol</th>\n",
       "      <th>Fbs</th>\n",
       "      <th>RestECG</th>\n",
       "      <th>MaxHR</th>\n",
       "      <th>ExAng</th>\n",
       "      <th>Oldpeak</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Ca</th>\n",
       "      <th>Thal</th>\n",
       "      <th>AHD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>typical</td>\n",
       "      <td>110</td>\n",
       "      <td>264</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>132</td>\n",
       "      <td>0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>reversable</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>68</td>\n",
       "      <td>1</td>\n",
       "      <td>asymptomatic</td>\n",
       "      <td>144</td>\n",
       "      <td>193</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>141</td>\n",
       "      <td>0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>reversable</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>asymptomatic</td>\n",
       "      <td>130</td>\n",
       "      <td>131</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>115</td>\n",
       "      <td>1</td>\n",
       "      <td>1.2</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>reversable</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>nontypical</td>\n",
       "      <td>130</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>174</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>normal</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>303</th>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>nonanginal</td>\n",
       "      <td>138</td>\n",
       "      <td>175</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>173</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>normal</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age  Sex     ChestPain  RestBP  Chol  Fbs  RestECG  MaxHR  ExAng  \\\n",
       "299   45    1       typical     110   264    0        0    132      0   \n",
       "300   68    1  asymptomatic     144   193    1        0    141      0   \n",
       "301   57    1  asymptomatic     130   131    0        0    115      1   \n",
       "302   57    0    nontypical     130   236    0        2    174      0   \n",
       "303   38    1    nonanginal     138   175    0        0    173      0   \n",
       "\n",
       "     Oldpeak  Slope   Ca        Thal  AHD  \n",
       "299      1.2      2  0.0  reversable  Yes  \n",
       "300      3.4      2  2.0  reversable  Yes  \n",
       "301      1.2      2  1.0  reversable  Yes  \n",
       "302      0.0      2  1.0      normal  Yes  \n",
       "303      0.0      1  NaN      normal   No  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Printing Last 5 rows of the dataset using tail()\n",
    "\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0c947ea0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 303 entries, 1 to 303\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   ChestPain  303 non-null    object \n",
      " 3   RestBP     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   MaxHR      303 non-null    int64  \n",
      " 8   ExAng      303 non-null    int64  \n",
      " 9   Oldpeak    303 non-null    float64\n",
      " 10  Slope      303 non-null    int64  \n",
      " 11  Ca         299 non-null    float64\n",
      " 12  Thal       301 non-null    object \n",
      " 13  AHD        303 non-null    object \n",
      "dtypes: float64(2), int64(9), object(3)\n",
      "memory usage: 35.5+ KB\n"
     ]
    }
   ],
   "source": [
    "# Getting more Information of dataset using info\n",
    "\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8136d1c4",
   "metadata": {},
   "source": [
    "<a name='pd'></a>\n",
    "# Preprocessing of Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e0efafd",
   "metadata": {},
   "source": [
    "### 1. Dealing with Missing Value \n",
    "### 2.Converting cateogerical value to numeric value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c91ac772",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age          0\n",
       "Sex          0\n",
       "ChestPain    0\n",
       "RestBP       0\n",
       "Chol         0\n",
       "Fbs          0\n",
       "RestECG      0\n",
       "MaxHR        0\n",
       "ExAng        0\n",
       "Oldpeak      0\n",
       "Slope        0\n",
       "Ca           4\n",
       "Thal         2\n",
       "AHD          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for Missing Value in whole dataset using \n",
    "# isnull(): Returns a Boolean value that indicates whether an expression contains no valid data (Null)\n",
    "\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "155beb58",
   "metadata": {},
   "outputs": [],
   "source": [
    "# AS we can see we have 2 colimn which have missing value (Ca and Thal)\n",
    "# Dealing with Missing Value: we will replace missing value with 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ccacbed5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Replcaing missing value in colimn Ca with 0 using fillna():\n",
    "#fiilna():\n",
    "\n",
    "df['Ca'].fillna(0,inplace=True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d9f3b776",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checkling if Ca is having null value since we fill null value it should return 0\n",
    "\n",
    "df['Ca'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "634bc4c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age          0\n",
       "Sex          0\n",
       "ChestPain    0\n",
       "RestBP       0\n",
       "Chol         0\n",
       "Fbs          0\n",
       "RestECG      0\n",
       "MaxHR        0\n",
       "ExAng        0\n",
       "Oldpeak      0\n",
       "Slope        0\n",
       "Ca           0\n",
       "Thal         2\n",
       "AHD          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for Missing Value again if any leftover\n",
    "\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c322b506",
   "metadata": {},
   "source": [
    "###### Note we still have one column Thal having missing value, we will deal with in a bit "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "725ac87d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting Careogorical data to numeric data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e4afe981",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Columns that needs to be changed\n",
    "# 1. ChestPain\n",
    "# 2. Thal\n",
    "# 3. AHD"
   ]
  },
  {
   "cell_type": "raw",
   "id": "d131e3cf",
   "metadata": {},
   "source": [
    "# Here we will change categorical value to numerical value of the column ChestPain \n",
    "'''\n",
    "1. ChestPain replacing value by :\n",
    "    Value 1 - typical \n",
    "    Value 2 - atypical \n",
    "    Value 3 - non-anginal \n",
    "    Value 4 - asymptomatic\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "39f447fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1           typical\n",
       "2      asymptomatic\n",
       "3      asymptomatic\n",
       "4        nonanginal\n",
       "5        nontypical\n",
       "           ...     \n",
       "299         typical\n",
       "300    asymptomatic\n",
       "301    asymptomatic\n",
       "302      nontypical\n",
       "303      nonanginal\n",
       "Name: ChestPain, Length: 303, dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Printing ChestPain column without processing \n",
    "\n",
    "df['ChestPain']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "41c22cb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "asymptomatic    144\n",
       "nonanginal       86\n",
       "nontypical       50\n",
       "typical          23\n",
       "Name: ChestPain, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#value_count():return a Series containing counts of unique values\n",
    "\n",
    "df['ChestPain'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bcd90817",
   "metadata": {},
   "outputs": [],
   "source": [
    "# replacing the value using replace()\n",
    "\n",
    "df['ChestPain'].replace(['typical','nontypical','nonanginal','asymptomatic'],[1,2,3,4],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4d9a240e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1      1\n",
       "2      4\n",
       "3      4\n",
       "4      3\n",
       "5      2\n",
       "      ..\n",
       "299    1\n",
       "300    4\n",
       "301    4\n",
       "302    2\n",
       "303    3\n",
       "Name: ChestPain, Length: 303, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Printing to check whether value have changed or not\n",
    "\n",
    "df['ChestPain']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "83da7ebe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4    144\n",
       "3     86\n",
       "2     50\n",
       "1     23\n",
       "Name: ChestPain, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#verifying the values are replaced properly using value_count()\n",
    "\n",
    "df['ChestPain'].value_counts()"
   ]
  },
  {
   "cell_type": "raw",
   "id": "6d5dd5ea",
   "metadata": {},
   "source": [
    "# Now lets move to second \n",
    "# Here we will change categorical value to numerical value of the column Thal \n",
    "\n",
    "'''\n",
    "2. Thal replacing value by :\n",
    "    Value 1 - normal\n",
    "    Value 2 - fixed\n",
    "    Value 3 - reversable\n",
    "    Value 4 - asymptomatic\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6b3b926f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replacing value using replace()\n",
    "\n",
    "df['Thal'].replace(['normal','fixed','reversable'],[1,2,3],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6fc5124b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0    166\n",
       "3.0    117\n",
       "2.0     18\n",
       "Name: Thal, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#verifying the values are replaced properly using value_count()\n",
    "\n",
    "df['Thal'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1e9a5cb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "301"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "166+117+18"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94562756",
   "metadata": {},
   "source": [
    "#### As we  have 303 rows we have 2 rows missing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1d77748",
   "metadata": {},
   "source": [
    "#### We now deal with missing value "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "fca9eacd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We are filling 0 in place of null value\n",
    "\n",
    "df['Thal'].fillna(0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ee6ad3b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0    166\n",
       "3.0    117\n",
       "2.0     18\n",
       "0.0      2\n",
       "Name: Thal, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verifying the values are replaced properly using value_count()\n",
    "\n",
    "df['Thal'].value_counts()"
   ]
  },
  {
   "cell_type": "raw",
   "id": "888ff78c",
   "metadata": {},
   "source": [
    "# Now lets move to third \n",
    "# Here we will change categorical value to numerical value of the column AHD(note :AHD is our target variable) \n",
    "'''\n",
    "3. AHD replacing value by :\n",
    "    Value 0 - No\n",
    "    Value 1 - True\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d9b6a94f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Replacing the value using replace()\n",
    "\n",
    "df['AHD'].replace(['No','Yes'],[0,1],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e3d3f061",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1      0\n",
       "2      1\n",
       "3      1\n",
       "4      0\n",
       "5      0\n",
       "      ..\n",
       "299    1\n",
       "300    1\n",
       "301    1\n",
       "302    1\n",
       "303    0\n",
       "Name: AHD, Length: 303, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking if changes are done\n",
    "\n",
    "df['AHD']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b101f540",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    164\n",
       "1    139\n",
       "Name: AHD, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verifying the values are replaced properly using value_count()\n",
    "\n",
    "df['AHD'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b41a8366",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "303"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "164+139"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ce5160a6",
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
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       "</table>\n",
       "<p>303 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age  Sex  ChestPain  RestBP  Chol  Fbs  RestECG  MaxHR  ExAng  Oldpeak  \\\n",
       "1     63    1          1     145   233    1        2    150      0      2.3   \n",
       "2     67    1          4     160   286    0        2    108      1      1.5   \n",
       "3     67    1          4     120   229    0        2    129      1      2.6   \n",
       "4     37    1          3     130   250    0        0    187      0      3.5   \n",
       "5     41    0          2     130   204    0        2    172      0      1.4   \n",
       "..   ...  ...        ...     ...   ...  ...      ...    ...    ...      ...   \n",
       "299   45    1          1     110   264    0        0    132      0      1.2   \n",
       "300   68    1          4     144   193    1        0    141      0      3.4   \n",
       "301   57    1          4     130   131    0        0    115      1      1.2   \n",
       "302   57    0          2     130   236    0        2    174      0      0.0   \n",
       "303   38    1          3     138   175    0        0    173      0      0.0   \n",
       "\n",
       "     Slope   Ca  Thal  AHD  \n",
       "1        3  0.0   2.0    0  \n",
       "2        2  3.0   1.0    1  \n",
       "3        2  2.0   3.0    1  \n",
       "4        3  0.0   1.0    0  \n",
       "5        1  0.0   1.0    0  \n",
       "..     ...  ...   ...  ...  \n",
       "299      2  0.0   3.0    1  \n",
       "300      2  2.0   3.0    1  \n",
       "301      2  1.0   3.0    1  \n",
       "302      2  1.0   1.0    1  \n",
       "303      1  0.0   1.0    0  \n",
       "\n",
       "[303 rows x 14 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Final look at the dataset\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f6a948f",
   "metadata": {},
   "source": [
    "<a name='ppd'></a>\n",
    "#  Preparing Data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6bff10df",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We are removing AHD which is target and store it in other varibale \n",
    "\n",
    "X=df.drop(columns='AHD',axis=1)\n",
    "\n",
    "y=df['AHD']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "2ee8ba1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1.0</td>\n",
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       "      <th>...</th>\n",
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       "      <td>0.0</td>\n",
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       "      <td>68</td>\n",
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       "      <td>144</td>\n",
       "      <td>193</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>141</td>\n",
       "      <td>0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
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       "      <th>301</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>130</td>\n",
       "      <td>131</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>115</td>\n",
       "      <td>1</td>\n",
       "      <td>1.2</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>174</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>303</th>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>138</td>\n",
       "      <td>175</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>173</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>303 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age  Sex  ChestPain  RestBP  Chol  Fbs  RestECG  MaxHR  ExAng  Oldpeak  \\\n",
       "1     63    1          1     145   233    1        2    150      0      2.3   \n",
       "2     67    1          4     160   286    0        2    108      1      1.5   \n",
       "3     67    1          4     120   229    0        2    129      1      2.6   \n",
       "4     37    1          3     130   250    0        0    187      0      3.5   \n",
       "5     41    0          2     130   204    0        2    172      0      1.4   \n",
       "..   ...  ...        ...     ...   ...  ...      ...    ...    ...      ...   \n",
       "299   45    1          1     110   264    0        0    132      0      1.2   \n",
       "300   68    1          4     144   193    1        0    141      0      3.4   \n",
       "301   57    1          4     130   131    0        0    115      1      1.2   \n",
       "302   57    0          2     130   236    0        2    174      0      0.0   \n",
       "303   38    1          3     138   175    0        0    173      0      0.0   \n",
       "\n",
       "     Slope   Ca  Thal  \n",
       "1        3  0.0   2.0  \n",
       "2        2  3.0   1.0  \n",
       "3        2  2.0   3.0  \n",
       "4        3  0.0   1.0  \n",
       "5        1  0.0   1.0  \n",
       "..     ...  ...   ...  \n",
       "299      2  0.0   3.0  \n",
       "300      2  2.0   3.0  \n",
       "301      2  1.0   3.0  \n",
       "302      2  1.0   1.0  \n",
       "303      1  0.0   1.0  \n",
       "\n",
       "[303 rows x 13 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Printing X where we have removed traget variable\n",
    "\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c24ab8bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1      0\n",
       "2      1\n",
       "3      1\n",
       "4      0\n",
       "5      0\n",
       "      ..\n",
       "299    1\n",
       "300    1\n",
       "301    1\n",
       "302    1\n",
       "303    0\n",
       "Name: AHD, Length: 303, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Printing Y which as only traget variable\n",
    "\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d5bd8632",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Spliting data into train data and test data\n",
    "\n",
    "X_train,X_test,y_train,y_test=train=train_test_split(X,y,test_size=0.2,stratify=y,random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "13a330fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(242, 13)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Printing the size of X training dataset\n",
    "\n",
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "f27f388a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(61, 13)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Printing the size of X testing dataset\n",
    "\n",
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "585e738e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(242,)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Printing the size of Y training dataset\n",
    "\n",
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "05bd674c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(61,)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Printing the size of Y testing dataset\n",
    "\n",
    "y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e142ee1",
   "metadata": {},
   "source": [
    "<a name='vt'></a>\n",
    "# Visualizing Training Data"
   ]
  },
  {
   "cell_type": "raw",
   "id": "cdb79385",
   "metadata": {},
   "source": [
    "# Here are just plotting histogram of X Training data (Note we will not touch testing data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "2760112c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We are just plotting histogram of Age\n",
    "\n",
    "X_train['Age'].hist(figsize=(12,8),bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "eabf89d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We are just plotting histogram of Sex\n",
    "\n",
    "X_train['Sex'].hist(figsize=(12,8),bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "b96ca47b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We are just plotting histogram of ChestPain\n",
    "\n",
    "X_train['ChestPain'].hist(figsize=(12,8),bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "3affab9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We are just plotting histogram of RestBP\n",
    "\n",
    "X_train['RestBP'].hist(figsize=(12,8),bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "e84471df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We are just plotting histogram of Chol\n",
    "\n",
    "X_train['Chol'].hist(figsize=(12,8),bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "a431b89a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We are just plotting histogram of Fbs\n",
    "\n",
    "X_train['Fbs'].hist(figsize=(12,8),bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "8aa4d04e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We are just plotting histogram of RestECG\n",
    "\n",
    "X_train['RestECG'].hist(figsize=(12,8),bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "88b43e7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We are just plotting histogram of MaxHR\n",
    "\n",
    "X_train['MaxHR'].hist(figsize=(12,8),bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "ad266688",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We are just plotting histogram of ExAng\n",
    "\n",
    "X_train['ExAng'].hist(figsize=(12,8),bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "c2467aa4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We are just plotting histogram of Oldpeak\n",
    "\n",
    "X_train['Oldpeak'].hist(figsize=(12,8),bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "3d34cfe4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We are just plotting histogram of Slope\n",
    "\n",
    "X_train['Slope'].hist(figsize=(12,8),bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "7c9fff68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We are just plotting histogram of Ca\n",
    "\n",
    "X_train['Ca'].hist(figsize=(12,8),bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "0cc4d135",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We are just plotting histogram of Thal\n",
    "\n",
    "X_train['Thal'].hist(figsize=(12,8),bins=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "383ea7ae",
   "metadata": {},
   "source": [
    "### Note we have not added AHD because its out targat variable "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "5501700b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'Age'}>,\n",
       "        <AxesSubplot:title={'center':'Sex'}>,\n",
       "        <AxesSubplot:title={'center':'ChestPain'}>,\n",
       "        <AxesSubplot:title={'center':'RestBP'}>],\n",
       "       [<AxesSubplot:title={'center':'Chol'}>,\n",
       "        <AxesSubplot:title={'center':'Fbs'}>,\n",
       "        <AxesSubplot:title={'center':'RestECG'}>,\n",
       "        <AxesSubplot:title={'center':'MaxHR'}>],\n",
       "       [<AxesSubplot:title={'center':'ExAng'}>,\n",
       "        <AxesSubplot:title={'center':'Oldpeak'}>,\n",
       "        <AxesSubplot:title={'center':'Slope'}>,\n",
       "        <AxesSubplot:title={'center':'Ca'}>],\n",
       "       [<AxesSubplot:title={'center':'Thal'}>, <AxesSubplot:>,\n",
       "        <AxesSubplot:>, <AxesSubplot:>]], dtype=object)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Instead of Writing all the histogram seprately we can use\n",
    "\n",
    "X_train.hist(figsize=(12,8),bins=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74f843eb",
   "metadata": {},
   "source": [
    "<a name='md'></a>\n",
    "# Model Building "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "3cfb7f11",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating instance of LogisticRegression\n",
    "\n",
    "model=LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "9c9a0b0f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Project\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: 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": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting the data in the logistic Regression (Ignore Waring)\n",
    "\n",
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e7e4dcf",
   "metadata": {},
   "source": [
    "#### We got above Warning beacuse our dataset is small it has only 303 rows  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbaee281",
   "metadata": {},
   "source": [
    "<a name='me'></a>\n",
    "# Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "fb5f2364",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "acc score= 0.8636363636363636\n"
     ]
    }
   ],
   "source": [
    "# As we Created out model Lets test it\n",
    "#We will fing Accuracy on training data\n",
    "\n",
    "X_train_predict=model.predict(X_train)\n",
    "\n",
    "training_data_acc=accuracy_score(X_train_predict,y_train)\n",
    "\n",
    "print('acc score=',training_data_acc)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "869584bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "acc score= 0.8524590163934426\n"
     ]
    }
   ],
   "source": [
    "# Accuracy on testing data\n",
    "\n",
    "X_test_predict=model.predict(X_test)\n",
    "\n",
    "testing_data_acc=accuracy_score(X_test_predict,y_test)\n",
    "\n",
    "print('acc score=',testing_data_acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2521820a",
   "metadata": {},
   "source": [
    "<a name='bp'></a>\n",
    "# Building Predictive System (optional)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "1e290d5b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Person have Heart Disease\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Project\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# Here we take one custom or from testing set input and test whether the person has heart disease or not \n",
    "\n",
    "#input_data=(41,0,2,130,204,0,2,172,0,1.4,1,0.0,1.0)\n",
    "\n",
    "input_data=(68,1,4,144,193,1,0,141,0,3.4,2,2.0,3.0)\n",
    "\n",
    "\n",
    "#changing input tuple to numpy array\n",
    "\n",
    "data=np.asarray(input_data)\n",
    "\n",
    "\n",
    "#reshaping the numpy array as we are predicting for only  on instance\n",
    "\n",
    "new_data=data.reshape(1,-1)\n",
    "\n",
    "\n",
    "# predicting on new data\n",
    "\n",
    "new_predict=model.predict(new_data)\n",
    "\n",
    "# Printing predicting value\n",
    "\n",
    "#print(new_predict)\n",
    "\n",
    "\n",
    "if(new_predict[0]==0):\n",
    "    \n",
    "    print('The Person does not have Heart Disease')\n",
    "\n",
    "else:\n",
    "    \n",
    "    print(\"The Person have Heart Disease\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "27d02feb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Lets save the model using pickle\n",
    "# # save the model to disk\n",
    "# filename = 'lg_heart.sav'\n",
    "# pickle.dump(model, open(filename, 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "f19f10d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # load the model from disk\n",
    "# loaded_model = pickle.load(open(filename, 'rb'))\n",
    "# result = loaded_model.score(X_test, Y_test)\n",
    "# print(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5de1f8b6",
   "metadata": {},
   "source": [
    "<a name='c'></a>\n",
    "# Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da706a50",
   "metadata": {},
   "source": [
    "### This Algorithm performed well,\n",
    "\n",
    "### Here we had only 303 rows, it very less data to train a model on  then too our model performed uite well we achieved above 85% accuracy on trainig data and 82% in testing data without getting overfitted\n",
    "\n",
    "### Logistic regression provides a useful means for modelling the dependence of a binary response variable on one or more explanatory variables, where the latter can be either categorical or continuous. The fit of the resulting model can be assessed using a number of methods.\n",
    "\n",
    "### We created custom input and tested it, in Building Predictive System section it also worked properly.\n",
    "\n",
    "### Thats it we are done with Logistic Regression Algorithm.....!!!"
   ]
  }
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