EDA_Heart_Attack_I.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "isolated-complement",
   "metadata": {},
   "source": [
    "## 1. Importing the required libraries for EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "adequate-winning",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np                     # For mathematical calculations \n",
    "import seaborn as sns                  # For data visualization \n",
    "import matplotlib.pyplot as plt        # For plotting graphs \n",
    "%matplotlib inline \n",
    "sns.set(color_codes=True)\n",
    "import warnings  \n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fresh-clinic",
   "metadata": {},
   "source": [
    "## Reading Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "accepted-subscriber",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trtbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalachh</th>\n",
       "      <th>exng</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slp</th>\n",
       "      <th>caa</th>\n",
       "      <th>thall</th>\n",
       "      <th>output</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trtbps  chol  fbs  restecg  thalachh  exng  oldpeak  slp  \\\n",
       "0   63    1   3     145   233    1        0       150     0      2.3    0   \n",
       "1   37    1   2     130   250    0        1       187     0      3.5    0   \n",
       "2   41    0   1     130   204    0        0       172     0      1.4    2   \n",
       "3   56    1   1     120   236    0        1       178     0      0.8    2   \n",
       "4   57    0   0     120   354    0        1       163     1      0.6    2   \n",
       "\n",
       "   caa  thall  output  \n",
       "0    0      1       1  \n",
       "1    0      2       1  \n",
       "2    0      2       1  \n",
       "3    0      2       1  \n",
       "4    0      2       1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_csv(\"heart.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "knowing-personality",
   "metadata": {},
   "source": [
    "## Dataset Description\n",
    "\n",
    "age : Age of the patient\\\n",
    "sex : Sex of the patient\\\n",
    "cp : Chest Pain\\\n",
    "trtbps : resting blood pressure (in mm Hg)\\\n",
    "chol : cholestoral in mg/dl\\\n",
    "fbs : fasting blood sugar\\\n",
    "restecg : resting electrocardiographic results\\\n",
    "thalachh : Maximum heart rate\\\n",
    "exang: exercise induced angina\\\n",
    "old peak : Previous peak\\\n",
    "slp : Slope\\\n",
    "caa: number of major vessels\\\n",
    "output : 0= less chance of heart attack 1= more chance of heart attack"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "israeli-collective",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 303 entries, 0 to 302\n",
      "Data columns (total 14 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   age       303 non-null    int64  \n",
      " 1   sex       303 non-null    int64  \n",
      " 2   cp        303 non-null    int64  \n",
      " 3   trtbps    303 non-null    int64  \n",
      " 4   chol      303 non-null    int64  \n",
      " 5   fbs       303 non-null    int64  \n",
      " 6   restecg   303 non-null    int64  \n",
      " 7   thalachh  303 non-null    int64  \n",
      " 8   exng      303 non-null    int64  \n",
      " 9   oldpeak   303 non-null    float64\n",
      " 10  slp       303 non-null    int64  \n",
      " 11  caa       303 non-null    int64  \n",
      " 12  thall     303 non-null    int64  \n",
      " 13  output    303 non-null    int64  \n",
      "dtypes: float64(1), int64(13)\n",
      "memory usage: 33.3 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "attractive-trouble",
   "metadata": {},
   "source": [
    "## Univariate Analysis\n",
    "In this section, we will do univariate analysis. It is the simplest form of analyzing data where we examine each variable individually. For categorical features we can use frequency table or bar plots which will calculate the number of each category in a particular variable. For numerical features, probability density plots can be used to look at the distribution of the variable.\n",
    "\n",
    "## Target Variable( if categorical)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "shaped-acting",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.output.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "interested-ferry",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    165\n",
       "0    138\n",
       "Name: output, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We will first look at the target variable, i.e., Loan_Status. \n",
    "# As it is a categorical variable, let us look at its frequency table, percentage distribution and bar plot.\n",
    "\n",
    "data['output'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "wound-budget",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    0.544554\n",
       "0    0.455446\n",
       "Name: output, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Normalize can be set to True to print proportions instead of number \n",
    "data['output'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "recent-modeling",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'output'}>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['output'].value_counts(normalize=True).plot.bar(title= 'output')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "italic-twins",
   "metadata": {},
   "source": [
    "As 54% of the person have heart attack so data is almost balanced so no need to balance the data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "vocational-latter",
   "metadata": {},
   "source": [
    "## Independent Variable\n",
    "\n",
    "## 1. Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "stable-christopher",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/rjn/miniconda3/envs/env/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1)\n",
    "plt.subplot(121) \n",
    "sns.distplot(data['age']); \n",
    "plt.subplot(122)\n",
    "data['age'].plot.box(figsize=(16,5)) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "renewable-rates",
   "metadata": {},
   "source": [
    "## 2. Sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "offensive-metro",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1) \n",
    "data['sex'].value_counts(normalize=True).plot.bar(title= 'sex') \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cardiac-wilderness",
   "metadata": {},
   "source": [
    "## 3. Chest Pain (cp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "accurate-wiring",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1) \n",
    "\n",
    "plt.subplot(221)\n",
    "data['cp'].value_counts(normalize=True).plot.bar(figsize=(20,10), title= 'Chest Pain') \n",
    "plt.xlabel('Chest Pain')\n",
    "# plt.title ('Distribution of chest pain')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "provincial-reservation",
   "metadata": {},
   "source": [
    "## 3. trtbps : resting blood pressure (in mm Hg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "actual-center",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/rjn/miniconda3/envs/env/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1)\n",
    "plt.subplot(121) \n",
    "sns.distplot(data['trtbps'], kde= True,label='Blood Pressure')\n",
    "plt.subplot(122)\n",
    "data['trtbps'].plot.box(figsize=(16,5))\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "egyptian-genetics",
   "metadata": {},
   "source": [
    "## 4. chol : cholestoral in mg/dl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "experienced-exclusive",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1)\n",
    "plt.subplot(121) \n",
    "sns.kdeplot(data['chol'], shade=True,label='cholestoral in mg/dl')\n",
    "plt.subplot(122)\n",
    "data['chol'].plot.box(figsize=(16,5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adapted-feature",
   "metadata": {},
   "source": [
    "## 5. fbs : fasting blood sugar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "configured-massachusetts",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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47FyucM79ABMU5DjvhIPfe0Ti4uJUWlqqmpoaNTQ0qLi4WPHx8a3GGGN08OBBjRgx4tIrBgAAAcNvEImMjFR2drYyMzOVmpqqSZMmKSYmRllZWSorK5P0rz/Z7datm8LCwjq8YAAA0HU4jDHW10QCeWkm+RdbbZcBCwofS2F6GgGJpZnAc8lLMwAAAB2FIAIAAKwhiAAAAGsIIgAAwBqCCAAAsIYgAgAArCGIAAAAawgiAADAGoIIAACwhiACAACsIYgAAABrCCIAAMAagggAALCGIAIAAKwhiAAAAGsIIgAAwBqCCAAAsIYgAgAArCGIAAAAa9oURAoLC5WUlKSEhATl5+ef1f/3v/9dM2fO1OTJk/XTn/5UJ06caPdCAQBA1+M3iFRWVio3N1cFBQXasmWLNm7cqPLycl+/MUb33XefsrKytG3bNg0aNEh5eXkdWjQAAOga/AaRkpISxcbGKiIiQk6nU4mJiSoqKvL1Hzx4UE6nU/Hx8ZKke++9VxkZGR1XMQAA6DJC/A2oqqqSy+Xytd1ut/bv3+9rHzlyRNddd50eeughffjhh7rhhhu0dOnSCyqid++eFzQe6ApcrnDbJQBWcO7jTH6DiNfrlcPh8LWNMa3aLS0tevfdd/X73/9eQ4cO1eOPP641a9ZozZo1bS6iurpOXq+5wNI7Py7GwObx1NouAbjsXK5wzv0AExTkOO+Eg9+lmaioKHk8Hl/b4/HI7Xb72i6XS/3799fQoUMlSZMmTWo1YwIAAHAufoNIXFycSktLVVNTo4aGBhUXF/vuB5GkESNGqKamRh999JEkaefOnRoyZEjHVQwAALoMv0szkZGRys7OVmZmppqbmzVlyhTFxMQoKytL8+bN09ChQ/XMM89oyZIlamhoUFRUlNauXXs5agcAAJ2cwxhj/eaMQL5HJPkXW22XAQsKH0thnRwBiXtEAs8l3yMCAADQUQgiAADAGoIIAACwhiACAACsIYgAAABrCCIAAMAagggAALCGIAIAAKwhiAAAAGsIIgAAwBqCCAAAsIYgAgAArCGIAAAAawgiAADAGoIIAACwhiACAACsIYgAAABrCCIAAMCaNgWRwsJCJSUlKSEhQfn5+Wf1P/300xo/frxSUlKUkpLyrWMAAAC+KcTfgMrKSuXm5mrTpk0KDQ3VnXfeqdGjR2vAgAG+MQcOHNBvf/tbjRgxokOLBQAAXYvfGZGSkhLFxsYqIiJCTqdTiYmJKioqajXmwIEDWrdunZKTk7Vy5Uo1NjZ2WMEAAKDr8BtEqqqq5HK5fG23263Kykpfu76+XoMGDVJOTo42b96skydP6r/+6786ploAANCl+F2a8Xq9cjgcvrYxplX7qquu0nPPPedrz549Ww899JCys7PbXETv3j3bPBboKlyucNslAFZw7uNMfoNIVFSU9u7d62t7PB653W5f+4svvlBJSYmmTJki6V9BJSTE725bqa6uk9drLmibroCLMbB5PLW2SwAuO5crnHM/wAQFOc474eB3aSYuLk6lpaWqqalRQ0ODiouLFR8f7+vv3r27HnnkER09elTGGOXn5+v2229vn+oBAECX5jeIREZGKjs7W5mZmUpNTdWkSZMUExOjrKwslZWV6dprr9XKlSt13333aeLEiTLG6K677roctQMAgE7OYYyxviYSyEszyb/YarsMWFD4WArT0whILM0EnktemgEAAOgoBBEAAGANQQQAAFhDEAEAANYQRAAAgDUEEQAAYA1BBAAAWEMQAQAA1hBEAACANQQRAABgDUEEAABYQxABAADWEEQAAIA1BBEAAGANQQQAAFhDEAEAANYQRAAAgDUEEQAAYA1BBAAAWNOmIFJYWKikpCQlJCQoPz//nOPeeustTZgwod2KAwAAXVuIvwGVlZXKzc3Vpk2bFBoaqjvvvFOjR4/WgAEDWo37xz/+od/85jcdVigAAOh6/M6IlJSUKDY2VhEREXI6nUpMTFRRUdFZ45YsWaIHHnigQ4oEAABdk98gUlVVJZfL5Wu73W5VVla2GvM///M/Gjx4sIYNG9b+FQIAgC7L79KM1+uVw+HwtY0xrdoff/yxiouL9dJLL6miouKiiujdu+dFbQd0Zi5XuO0SACs493Emv0EkKipKe/fu9bU9Ho/cbrevXVRUJI/Hox/96Edqbm5WVVWVpk+froKCgjYXUV1dJ6/XXGDpnR8XY2DzeGptlwBcdi5XOOd+gAkKcpx3wsHv0kxcXJxKS0tVU1OjhoYGFRcXKz4+3tc/b9487dixQ1u3blVeXp7cbvcFhRAAABC4/AaRyMhIZWdnKzMzU6mpqZo0aZJiYmKUlZWlsrKyy1EjAADoohzGGOtrIoG8NJP8i622y4AFhY+lMD2NgMTSTOC55KUZAACAjkIQAQAA1hBEAACANQQRAABgDUEEAABYQxABAADWEEQAAIA1BBEAAGANQQQAAFhDEAEAANYQRAAAgDUEEQAAYA1BBAAAWEMQAQAA1hBEAACANQQRAABgDUEEAABYQxABAADWtCmIFBYWKikpSQkJCcrPzz+r//XXX1dycrL+4z/+QwsXLlRTU1O7FwoAALoev0GksrJSubm5Kigo0JYtW7Rx40aVl5f7+k+dOqWVK1fqxRdf1Pbt29XY2KjNmzd3aNEAAKBr8BtESkpKFBsbq4iICDmdTiUmJqqoqMjX73Q6tXPnTl133XVqaGhQdXW1evXq1aFFAwCArsFvEKmqqpLL5fK13W63KisrW43p1q2bdu3apdtuu03Hjx/X2LFj279SAADQ5YT4G+D1euVwOHxtY0yr9tfGjRund955R7/97W+1fPlyPfbYY20uonfvnm0eC3QVLle47RIAKzj3cSa/QSQqKkp79+71tT0ej9xut6/9z3/+UwcOHPDNgiQnJys7O/uCiqiurpPXay5om66AizGweTy1tksALjuXK5xzP8AEBTnOO+Hgd2kmLi5OpaWlqqmpUUNDg4qLixUfH+/rN8YoJydHX3zxhSSpqKhII0eObIfSAQBAV+d3RiQyMlLZ2dnKzMxUc3OzpkyZopiYGGVlZWnevHkaOnSoVq1apXvuuUcOh0MDBgzQihUrLkftAACgk3MYY6yviQTy0kzyL7baLgMWFD6WwvQ0AhJLM4HnkpdmAAAAOgpBBAAAWEMQAQAA1hBEAACANQQRAABgDUEEAABYQxABAADWEEQAAIA1BBEAAGANQQQAAFhDEAEAANYQRAAAgDUEEQAAYA1BBAAAWEMQAQAA1hBEAACANQQRAABgDUEEAABYQxABAADWtCmIFBYWKikpSQkJCcrPzz+r/4033lBKSoomT56sn/3sZzpx4kS7FwoAALoev0GksrJSubm5Kigo0JYtW7Rx40aVl5f7+uvq6rR8+XLl5eVp27ZtGjhwoJ566qkOLRoAAHQNfoNISUmJYmNjFRERIafTqcTERBUVFfn6m5ubtWzZMkVGRkqSBg4cqC+//LLjKgYAAF2G3yBSVVUll8vla7vdblVWVvra11xzjW6//XZJ0unTp5WXl6cf/vCHHVAqAADoakL8DfB6vXI4HL62MaZV+2u1tbW6//77ddNNNyktLe2Ciujdu+cFjQe6Apcr3HYJgBWc+ziT3yASFRWlvXv3+toej0dut7vVmKqqKv30pz9VbGysHnrooQsuorq6Tl6vueDtOjsuxsDm8dTaLgG47FyucM79ABMU5DjvhIPfpZm4uDiVlpaqpqZGDQ0NKi4uVnx8vK//q6++0r333qs77rhDixcv/tbZEgAAgG/jd0YkMjJS2dnZyszMVHNzs6ZMmaKYmBhlZWVp3rx5qqio0KFDh/TVV19px44dkqTvf//7+vWvf93hxQMAgM7NYYyxviYSyEszyb/YarsMWFD4WArT0whILM0EnktemgEAAOgoBBEAAGANQQQAAFhDEAEAANYQRAAAgDUEEQAAYA1BBAAAWEMQAQAA1hBEAACANX4f8Q4AaD/hvXqoe1hg/+gN1A/8PN3YotqTDbbLuOIE9tUAAJdZ97AQPtohQBU+liIebn82lmYAAIA1BBEAAGANQQQAAFhDEAEAANYQRAAAgDUEEQAAYA1BBAAAWEMQAQAA1rQpiBQWFiopKUkJCQnKz88/57gHH3xQmzZtarfiAABA1+Y3iFRWVio3N1cFBQXasmWLNm7cqPLy8rPG3HvvvdqxY0eHFQoAALoev0GkpKREsbGxioiIkNPpVGJiooqKilqNKSws1L//+7/rjjvu6LBCAQBA1+P3s2aqqqrkcrl8bbfbrf3797cac/fdd0uS9u3bd1FF9O7d86K2AzqzQP3gLyCQcd2fzW8Q8Xq9cjgcvrYxplW7PVRX18nrNe26z86AEzKweTx8/FUg4roPbIF43QcFOc474eB3aSYqKkoej8fX9ng8crvd7VMdAAAIaH6DSFxcnEpLS1VTU6OGhgYVFxcrPj7+ctQGAAC6OL9BJDIyUtnZ2crMzFRqaqomTZqkmJgYZWVlqays7HLUCAAAuii/94hIUnJyspKTk1v923PPPXfWuDVr1rRPVQAAICDwZFUAAGANQQQAAFhDEAEAANYQRAAAgDUEEQAAYA1BBAAAWEMQAQAA1hBEAACANQQRAABgDUEEAABYQxABAADWEEQAAIA1BBEAAGANQQQAAFhDEAEAANYQRAAAgDUEEQAAYA1BBAAAWNOmIFJYWKikpCQlJCQoPz//rP4PP/xQ6enpSkxM1OLFi9XS0tLuhQIAgK7HbxCprKxUbm6uCgoKtGXLFm3cuFHl5eWtxuTk5Og///M/tWPHDhlj9Morr3RYwQAAoOvwG0RKSkoUGxuriIgIOZ1OJSYmqqioyNd/7NgxnT59WsOHD5ckpaent+oHAAA4lxB/A6qqquRyuXxtt9ut/fv3n7Pf5XKpsrLygooICnJc0PiuxH1ND9slwJJAPu8DHdd94ArE697fe/YbRLxerxyO/9uJMaZV219/W1xzzVUXNL4rWb8kwXYJsKR37562S4AlXPeBi+v+bH6XZqKiouTxeHxtj8cjt9t9zv5//OMfrfoBAADOxW8QiYuLU2lpqWpqatTQ0KDi4mLFx8f7+r/73e8qLCxM+/btkyRt3bq1VT8AAMC5OIwxxt+gwsJCrVu3Ts3NzZoyZYqysrKUlZWlefPmaejQofroo4+0ZMkS1dXVaciQIXr44YcVGhp6OeoHAACdWJuCCAAAQEfgyaoAAMAagggAALCGIAIAAKwhiAAAAGsIIgAAwBqCCAAAsMbvI96B9vDJJ59ox44dqqioUFBQkNxut2699VYNHTrUdmkAAIuYEUGHy8/P189//nNJ0tChQzVkyBBJ0tKlS/XCCy/YLA0AYBkPNEOHS0xM1JYtW9SjR+tPHG1oaFBaWpqKioosVQagI33xxRfn7e/Tp89lqgRXMpZm0OFCQkLU0tJy1r+fPn1a3bp1s1ARgMvhnnvu0WeffSa3261v/p/X4XDoz3/+s6XKcCUhiKDD3XvvvUpNTdUtt9wil8slh8Ohqqoq7dmzR9nZ2bbLA9BBXn75ZU2fPl3Lli3TqFGjbJeDKxRLM7gsKisrVVpaqqqqKnm9XkVFRemWW25RZGSk7dIAdKD9+/frD3/4g1atWmW7FFyhCCIAAMAa/moGAABYQxABAADWEESATmTZsmWaMGGCcnNzL2r72bNnq6amRpKUlZWl8vLydqlr4cKFWr9+/bf2DRw40HfMS1VWVqYJEya0y74AXBn4qxmgE9m4caPeeustRUVFXdT2b7/9tu/1c889115lAcBFI4gAncT06dNljFFWVpaWLVum2tparVu3Tk1NTaqpqVFqaqrmz5+v+vp6LVq0SIcPH1ZQUJCGDBmilStXavHixZKkWbNmKS8vTxkZGXriiSd06tQp5ebmql+/fvrb3/6mlpYWrVixQqNGjVJNTY0WLVqkI0eOKCIiQi6XSzfeeKPmzp17Vn379u3Tjh07VFdXpzFjxmjBggUKCWn9I+aZZ57R9u3bFRwcrOuvv15Lly6Vy+VSRUWFli9frmPHjskYo9TUVN19992SpIKCAv3ud79Tz549FR0d/a1fm5aWFq1atUrvv/++unXrpr59++rhhx/W8ePHlZycrA8++ECS9Pnnn/vaX331ldauXaudO3cqPDxcMTEx+uSTT7Rhwwb99a9/1SOPPKKmpiZ5PB7FxcVp9erV+vzzz5WRkaHvfe97OnbsmDZs2CC3292e32Yg8BgAnUZ0dLSprq42Xq/XzJgxw3z66afGGGMqKirMoEGDTHV1tdm8ebOZPXu2McaYlpYWs3jxYvPZZ5+12t4YY8aPH2/2799v9uzZYwYNGmQOHTpkjDFm/fr1JiMjwxhjTHZ2tlm7dq0xxpjKykozZswY8+STT55V14IFC0xaWpqpr683jY2NZsaMGSY/P7/VMV999VUzbdo0U19fb4wx5sknn/TVmZGRYV544QVjjDEnT540ycnJ5rXXXjOHDh0yt9xyi6mqqjLGGLN06VIzfvz4s47/3nvvmYkTJxqv12uMMWbt2rVm37595ujRo2b48OG+cWe2X375ZZORkWFOnz5tGhsbzezZs82MGTN873vPnj3GGGPq6urM6NGjTVlZmTl69KiJjo4277333oV82wCcB/eIAJ2Qw+HQs88+q4MHD+rpp5/WmjVrZIxRQ0ODRo0apfLycs2cOVN5eXmaNWuW+vfvf9799enTR4MGDZIkDR48WCdOnJAk7dq1S9OmTZMkud1uTZw48Zz7SElJkdPpVGhoqCZPnqySkpJW/bt371Z6erqcTqckKTMzU3v27FFtba3ef/99ZWRkSJLCw8OVnp6u3bt3q7S0VGPGjJHL5ZIkXy3fFB0dreDgYE2dOlWPP/64EhMTNXLkyPO+5127diklJUVhYWEKDQ1tte81a9aotrZWzz77rFasWKHGxkadOnVK0r+eFDx8+PDz7htA2xFEgE7o1KlTSktL08GDBzV48GA9+OCDCgkJkTFG/fr10+uvv645c+aorq5Od911l3bu3Hne/XXv3t332uFw+B7H/fU+vxYUdO4fGcHBwb7XxpizlmW8Xq8cDker9teP/jffeJzRufrOPMaZevXqpa1bt2rBggUKDg7W/PnzlZ+f3+q9SFJzc7Pv9TfrO/O9zZgxQ7t27dINN9yg+++/v9UjykNDQ8/aFsDFI4gAndDhw4dVV1en+fPna8KECXrnnXfU1NQkr9ergoICLVq0SGPHjlVOTo7Gjh2rQ4cOSfrXL/Jv+9yfcxk3bpxeffVVSdLx48f1xhtvtAoTZ9q+fbuamprU2NiozZs3Kz4+vlX/rbfeqj/+8Y++mYUNGzbo5ptvVnh4uIYNG6b8/HxJUm1trbZs2aK4uDiNGTNGb7/9tioqKiRJmzdv/tZjv/nmm/rJT36iESNGaO7cuUpNTdWBAwfUq1cvNTc3+/46aPv27a3e27Zt29TU1KSWlhbfvk+ePKmysjL98pe/VEJCgioqKnTkyBF5vd42f90AtB2xHuiEBg4cqNtuu0133HGHQkNDFR0drQEDBujw4cNKTU3Vu+++q6SkJPXo0UPf+c53NHPmTEnSxIkTNXPmTD311FNtOs6iRYu0ZMkSJScnKyIiQn369Gk1e3Kmvn37avr06aqvr9ftt9+utLS0Vv1TpkzRl19+qalTp8rr9ap///569NFHJUmPPvqoVq5cqU2bNqmpqUnJyclKT0+Xw+FQTk6OZs2apauuukoxMTHfeuz4+Hjt3r1bkyZNktPp1NVXX61Vq1YpPDxcOTk5ysrK0rXXXttqaSk9PV2ffvqpUlNT5XQ61bdvX/Xo0UO9evXSnDlzlJaWJqfTqcjISI0cOVKHDx9Wv3792vR1A9B2POIdwDnl5+dr8ODBGjFihJqamjR9+nTNnTtX48aNs13aJfvLX/6i6upqpaSkSJJ+9atfKSwsTDk5OZYrAwILMyIAzmnAgAFatWqVvF6vmpubNXHixC4RQiTpxhtv1Pr16/X888/L6/Xqpptu0vLly22XBQQcZkQAAIA13KwKAACsIYgAAABrCCIAAMAagggAALCGIAIAAKwhiAAAAGv+P5b3GZvtc4NaAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1) \n",
    "\n",
    "plt.subplot(221)\n",
    "data['fbs'].value_counts(normalize=True).plot.bar(figsize=(20,10), title= 'fasting blood sugar') \n",
    "plt.xlabel('fasting blood sugar')\n",
    "# plt.title ('Distribution of fasting blood sugar')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dimensional-ceremony",
   "metadata": {},
   "source": [
    "## 6. restecg : resting electrocardiographic results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "italic-veteran",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1) \n",
    "plt.subplot(221)\n",
    "data['restecg'].value_counts(normalize=True).plot.bar(figsize=(20,10), title= 'resting electrocardiographic results') \n",
    "plt.xlabel('resting electrocardiographic results')\n",
    "# plt.title ('Distribution of resting electrocardiographic results')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "swiss-copper",
   "metadata": {},
   "source": [
    "## 7. thalachh : Maximum heart rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "skilled-overhead",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/rjn/miniconda3/envs/env/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1152x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1)\n",
    "plt.subplot(121) \n",
    "sns.distplot(data['thalachh'], kde=True,label='Maximum heart rate')\n",
    "plt.subplot(122)\n",
    "data['thalachh'].plot.box(figsize=(16,5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dominant-thompson",
   "metadata": {},
   "source": [
    "## 8. exng: exercise induced angina"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "covered-screening",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1) \n",
    "plt.subplot(221)\n",
    "data['exng'].value_counts(normalize=True).plot.bar(figsize=(20,10), title= 'exercise induced angina') \n",
    "plt.xlabel('exercise induced angina')\n",
    "# plt.title ('Distribution of exercise induced angina')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "presidential-negotiation",
   "metadata": {},
   "source": [
    "## 9. old peak : Previous peak"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "twenty-designer",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1)\n",
    "plt.subplot(121) \n",
    "sns.kdeplot(data['oldpeak'], shade=True,label='Previous peak')\n",
    "plt.subplot(122)\n",
    "data['oldpeak'].plot.box(figsize=(16,5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acceptable-anderson",
   "metadata": {},
   "source": [
    "## 10. slp : Slope"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "finished-spring",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1) \n",
    "\n",
    "plt.subplot(221)\n",
    "data['slp'].value_counts(normalize=True).plot.bar(figsize=(20,10), title= 'Slope') \n",
    "plt.xlabel('Slope')\n",
    "# plt.title ('Distribution of Slope')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "accepting-chester",
   "metadata": {},
   "source": [
    "## 11. caa: number of major vessels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "appointed-finder",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1) \n",
    "\n",
    "plt.subplot(221)\n",
    "data['caa'].value_counts(normalize=True).plot.bar(figsize=(20,10), title= 'number of major vessels') \n",
    "plt.xlabel('number of major vessels')\n",
    "# plt.title ('Distribution of number of major vessels'')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "convinced-spouse",
   "metadata": {},
   "source": [
    "# bivariate analysis\n",
    "\n",
    "## 1. outputs vs age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "found-coordination",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins=[20,30,40,50,60,70,80] # bin range\n",
    "\n",
    "data['extra_col']=pd.cut(data['age'],bins)\n",
    "age=pd.crosstab(data['extra_col'],data['output']) \n",
    "age.div(age.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True) \n",
    "\n",
    "plt.xlabel('age') \n",
    "P = plt.ylabel('output')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "impossible-valuation",
   "metadata": {},
   "source": [
    "## Following inferences can be made from the above bar plots:\n",
    "\n",
    "1. People having age 40-60 are more likely to have heart disease whereas people having higher heart rate are very much probable to suffer from heart attack."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ancient-aerospace",
   "metadata": {},
   "source": [
    "## 2. outputs vs sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "front-burns",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sex=pd.crosstab(data['sex'],data['output'])\n",
    "sex.div(sex.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True, figsize=(4,4))\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "hindu-rachel",
   "metadata": {},
   "source": [
    "## Following inferences can be made from the above bar plots:\n",
    "\n",
    "1. 73% of male suffers from heart attack whereas only 45% of female suffer from heart attack so males are more likely to suffer from it."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "brazilian-tenant",
   "metadata": {},
   "source": [
    "## 3. outputs vs cp (chest pain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "perfect-communication",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AwUBEAgYDEQkYDEQkYDAQkYDBQEQCBgMRCRgMRCRgMBCRgMFARAIGAxEJGAxEJGAwEJGAwUBEAgYDEQk8Cgaz2YyUlBQkJyejvLxc6D98+DAWLVqEhQsX4plnnsGlS5cUL5SI/MdtMFgsFpSUlKCiogKHDh3Cnj17cO7cOVd/R0cHXn31VZSWluKDDz7A1KlT8Yc//MGnRRORb7l9tFttbS3i4+MREREBADAajaiqqkJOTg4AwOFwoLCwEDExMQCAqVOnwmw2+65iGhQGw3MRBzO3wWC1WqHVal1tnU6HhoYGVzsyMhIPPvggAMBut6O0tBSPP/64D0qlwWQwPBdxMHMbDJIkQaVSudqyLPdof+/KlStYs2YNYmNjsWTJEq+KiIoa4dX4QNFqRwa6BJ/i/IKX0nNzGwx6vR719fWuts1mg06n6zHGarVi5cqViI+Px0svveR1Ea2tHZAk2atlAvFHttmu+G1bnJ/y/DW/YJibWq3q9Q3Z7cnHhIQE1NXVoa2tDV1dXaipqUFiYqKrv7u7G6tXr8bDDz+M/Pz8W+5NEFFwcbvHEBMTg7y8PGRlZcHhcCAjIwNxcXHIzs5Gbm4umpub8dlnn6G7uxvV1dUAgLvvvhtFRUU+L56IfMOjL5wxmUwwmUw9XisrKwMAGAwGNDY2Kl/ZjxzP2lMg8ZuoBiietadA4i3RRCRgMBCRgMFARAIGAxEJGAxEJGAwEJGAwUBEAgYDEQkYDEQkYDAQkYDBQEQCBgMRCRgMRCRgMBCRgMFARAIGAxEJGAxEJGAwEJGAwUBEAgYDEQkYDEQkYDAQkYDBQEQCBgMRCRgMRCRgMBCRgMFARAIGAxEJGAxEJGAwEJGAwUBEAgYDEQk8Cgaz2YyUlBQkJyejvLxc6D9z5gzS0tJgNBqRn58Pp9OpeKFE5D9ug8FisaCkpAQVFRU4dOgQ9uzZg3PnzvUYs3btWrzyyiuorq6GLMvYu3evzwomIt/TuBtQW1uL+Ph4REREAACMRiOqqqqQk5MDALhw4QLsdjtmzpwJAEhLS8M777yD5cuXe1yEWq3yvnIAushhfVqur/paZ19xfsry5/wG+tzcjXcbDFarFVqt1tXW6XRoaGi4bb9Wq4XFYvGqyMjI4V6N/97WguQ+LddXUVEj/Lo9zk9Z/pxfsM/N7aGEJElQqf6ZLrIs92i76yei4OM2GPR6PWw2m6tts9mg0+lu29/S0tKjn4iCj9tgSEhIQF1dHdra2tDV1YWamhokJia6+seNG4ewsDCcPHkSAFBZWdmjn4iCj0qWZdndILPZjC1btsDhcCAjIwPZ2dnIzs5Gbm4uDAYDGhsbUVBQgI6ODkyfPh0bNmxAaGioP+onIh/wKBiI6MeFdz4SkYDBQEQCBgMRCRgMRCRgMBCRwO0t0cHsiy++QHV1NZqbm6FWq6HT6fCzn/0MBoMh0KWRBw4fPoyLFy8iKSkJ48ePd72+Z88eLF26NICVKeP8+fMYNmwYYmJisG/fPnz++ee45557kJKSEujSBu8eQ3l5OZ577jkAgMFgwPTp0wEAL7/8MrZt2xbI0sgDGzduxK5du3D+/HksW7YMlZWVrr7du3cHsDJl7NixAytXrkRmZiZefPFF/OUvf8HEiRNx4MABbN68OdDlAfIglZycLF+9elV4/erVq7LRaAxARcq7cOFCr/8Fs9TUVNnhcMiyLMtffvmlfP/998sfffSRLMuyvGjRogBWpozU1FTZbrfL3377rTxz5kzZbrfLsizL165dk00mU4Crk+VBeyih0Whu+cAYu92OIUOGBKAi5a1atQrnz5+HTqeD/IP71FQqFY4cORKgyvpPvunDeHfddRe2bNmCJ554AmPGjBkUH9KTJAmhoaEYN24cnnzySYSFhbn6uru7A1jZDYP2zkez2YxNmzZh3rx50Gq1UKlUsFqt+Pjjj5GXl4dHHnkk0CX2W0dHB5YvX47CwkLce++9gS5HUe+++y5qa2uxbt06xMXFAQBOnjyJnJwcXL9+3fXZnGD19ttv48SJE9i5cydCQkIAwPXRgvvuu8/1vJNAGbTBANx4+lRdXR2sViskSYJer8e8efMQExMT6NIU09DQgH379mH9+vWBLkVxdXV10Ol0mDx5suu1ixcvYtu2bcjPzw9gZcr45JNPMGfOHFe7qakJ33zzDZKSkgJY1Q2DOhiIqG8G7VUJIuo7BgMRCRgMRCRgMBCRYNDex0DK279/P7Zv3w61Wo3IyEikpaWhvLwcd9xxB5qamjB06FAUFxf3uIpAwYlXJcgjjY2NWLFiBd5//32MHTsWO3bswO7du/HVV1/hvffew+zZs/HnP/8Z+/btw8GDBwNdLvUTDyXII3V1dZg/fz7Gjh0LAFixYgVee+01xMbGYvbs2QCA9PR0nDlzBu3t7YEslRTAYCCPhISE9LgV2W63o6mpyXXX3g/HUnBjMJBH5s6d67qLFLjxCcff//73aGxsRGNjI4AbH4eeNWsWRo0aFchSSQE8x0Aeq6ysxNatWwHc+CrCxYsX46233kJsbCwuXLiAMWPGoKioCHfeeWeAK6X+YjBQnx0/fhzr16/Hhx9+GOhSSGE8lCAiAfcYiEjAPQYiEjAYiEjAYCAiAYOBiAQMBiISMBiISPB/iNqk3MFuy+QAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "chest_pain=pd.crosstab(data['cp'],data['output'])\n",
    "chest_pain.div(chest_pain.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True, figsize=(4,4))\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "confident-embassy",
   "metadata": {},
   "source": [
    "## Following inferences can be made from the above bar plots:\n",
    "\n",
    "1. People having chest pain are highly probably to suffer from heart attack.\n",
    "\n",
    "## 4. outputs vs trtbps(resting blood pressure (in mm Hg))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "controversial-position",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'output')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins=[80,100,120,140,160,180,200,220] # bin range\n",
    "\n",
    "data['extra_col']=pd.cut(data['trtbps'],bins)\n",
    "trtbps=pd.crosstab(data['extra_col'],data['output']) \n",
    "trtbps.div(trtbps.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True) \n",
    "\n",
    "plt.xlabel('trtbps') \n",
    "plt.ylabel('output')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ready-broad",
   "metadata": {},
   "source": [
    "## Following inferences can be made from the above bar plots:\n",
    "\n",
    "1.  blood between 110 to 140 are highly likely to suffer from heart attack \n",
    "\n",
    "## 5. outputs vs chol(Cholestrol)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "generic-adobe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'output')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins=[100,200,300,400,500,600] # bin range\n",
    "\n",
    "data['extra_col']=pd.cut(data['chol'],bins)\n",
    "chol=pd.crosstab(data['extra_col'],data['output']) \n",
    "chol.div(chol.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True) \n",
    "\n",
    "plt.xlabel('chol') \n",
    "plt.ylabel('output')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "naked-wayne",
   "metadata": {},
   "source": [
    "## Following inferences can be made from the above bar plots:\n",
    "\n",
    "1. People having cholestrol 120-400 are highly probably to suffer from heart attack.\n",
    "\n",
    "## 6. outputs vs fbs(fasting blood sugar)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "italic-psychology",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQYAAAEFCAYAAAABoUmSAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAUYklEQVR4nO3de3CU9aHG8Wc3kZgQNBB2E8QOg1pBMZGKrSFiUBwTm2G5phbTIR3qRBCZ2NhyVBKNh0oRxjZWxzNjcvDWZj1BhISdziSxcOxpJ6kIjoWDMjZlrEqH3c0FJTQhl93zh2e2pD9gE9jdNyTfz1/55b3ss7zDs+91YwsGg0EBwBnsVgcAMPJQDAAMFAMAA8UAwEAxADBQDAAMFAMAQ7zVASSps/OUAoHReTtFamqy2tu7rI6BCzCat53dbtPEiePPOX1EFEMgEBy1xSBpVL+30W6sbjsOJQAYKAYABooBgIFiAGAYUjF0dXVp4cKF+uKLL4xpH3/8sZYtW6a8vDyVlZWpv78/4iEBxFbYYvjzn/+s+++/X59++ulZp69fv15PPfWUGhsbFQwGtX379khnBBBjYYth+/btqqiokNPpNKYdO3ZMPT09mj17tiRp2bJlamhoiHhIALEV9j6GTZs2nXOaz+eTw+EIjR0Oh7xe77BDpKYmD3uZCxXo75U9flzMXk+SHI4JMXstK95fLMX6/Y3VbXdRNzgFAgHZbLbQOBgMDhoPVXt7V8xuJHE4JujopuUxeS0rXFP2tvz+k1bHiJrRvP1iue3sdtt5P5Av6qpEenq6/H5/aNzW1nbWQw4Al5aLKoapU6cqISFBBw4ckCTV19crJycnIsEAWOeCiqG4uFiHDh2SJD333HPavHmz7r33Xv3jH/9QUVFRRAMCiL0hn2PYu3dv6Ofq6urQzzNnztSOHTsimwqApbjzEYCBYgBgoBgAGCgGAAaKAYCBYgBgoBgAGCgGAAaKAYCBYgBgoBgAGCgGAAaKAYCBYgBgoBgAGCgGAAaKAYCBYgBgoBgAGCgGAAaKAYCBYgBgoBgAGCgGAAaKAYCBYgBgoBgAGCgGAAaKAYCBYgBgoBgAGCgGAIYhFYPH41F+fr5yc3NVU1NjTD98+LCWL1+uRYsWafXq1frqq68iHhRA7IQtBq/Xq8rKSrndbtXV1am2tlatra2D5tm0aZNKSkq0e/duTZ8+Xdu2bYtaYADRF7YYmpublZWVpZSUFCUlJSkvL08NDQ2D5gkEAjp16pQkqbu7W5dffnl00gKIibDF4PP55HA4QmOn0ymv1ztonscff1zl5eWaN2+empubtWLFisgnBRAz8eFmCAQCstlsoXEwGBw07unpUVlZmV577TVlZmbq1Vdf1WOPPaaqqqohh0hNTR5mbJyPwzHB6gi4QCNl24UthvT0dO3fvz809vv9cjqdofEnn3yihIQEZWZmSpK+//3v61e/+tWwQrS3dykQCA5rmQs1Uv7ho8nvP2l1hKgZ7dsvVtvObred9wM57KFEdna2Wlpa1NHRoe7ubjU1NSknJyc0fdq0aTp+/LiOHj0qSdqzZ48yMjIiEB2AVcLuMaSlpam0tFRFRUXq6+tTQUGBMjMzVVxcrJKSEmVkZGjz5s368Y9/rGAwqNTUVP385z+PRXYAURK2GCTJ5XLJ5XIN+l11dXXo5/nz52v+/PmRTQbAMtz5CMBAMQAwUAwADBQDAAPFAMBAMQAwUAwADBQDAAPFAMBAMQAwUAwADBQDAAPFAMBAMQAwUAwADBQDAAPFAMBAMQAwUAwADBQDAAPFAMBAMQAwUAwADBQDAAPFAMBAMQAwUAwADBQDAAPFAMBAMQAwUAwADBQDAMOQisHj8Sg/P1+5ubmqqakxph89elQrV67UokWL9MADD+jLL7+MeFAAsRO2GLxeryorK+V2u1VXV6fa2lq1traGpgeDQT300EMqLi7W7t27dcMNN6iqqiqqoQFEV9hiaG5uVlZWllJSUpSUlKS8vDw1NDSEph8+fFhJSUnKycmRJK1Zs0Y/+MEPopcYQNSFLQafzyeHwxEaO51Oeb3e0Pizzz7T5MmTtWHDBi1dulQVFRVKSkqKTloAMREfboZAICCbzRYaB4PBQeP+/n7t27dPv/nNb5SRkaHnn39ezz77rJ599tkhh0hNTR5mbJyPwzHB6gi4QCNl24UthvT0dO3fvz809vv9cjqdobHD4dC0adOUkZEhSVq4cKFKSkqGFaK9vUuBQHBYy1yokfIPH01+/0mrI0TNaN9+sdp2drvtvB/IYQ8lsrOz1dLSoo6ODnV3d6upqSl0PkGSvvWtb6mjo0NHjhyRJO3du1ezZs2KQHQAVgm7x5CWlqbS0lIVFRWpr69PBQUFyszMVHFxsUpKSpSRkaGXXnpJ5eXl6u7uVnp6urZu3RqL7ACiJGwxSJLL5ZLL5Rr0u+rq6tDPN998s3bs2BHZZAAsw52PAAwUAwADxQDAQDEAMFAMAAwUAwADxQDAQDEAMFAMAAwUAwADxQDAQDEAMFAMAAwUAwADxQDAQDEAMFAMAAwUAwDDkL7aDRgpAn29uqbsbatjREWgr9fqCCEUAy4p9svGyfWTeqtjRIXnF4slnbY6hiQOJQCcBcUAwEAxADBQDAAMFAMAA8UAwDDmLleO5uvg0si6Fo5L15grhtF8HVwaWdfCceniUAKAgWIAYKAYABgoBgAGigGAYUjF4PF4lJ+fr9zcXNXU1JxzvnfffVcLFiyIWDgA1gh7udLr9aqyslI7d+7UuHHjtGLFCt1222267rrrBs3X1tamLVu2RC0ogNgJu8fQ3NysrKwspaSkKCkpSXl5eWpoaDDmKy8v17p166ISEkBshd1j8Pl8cjgcobHT6dTBgwcHzfPGG2/oxhtv1M0333xBIVJTky9oOZydwzHB6gi4QCNl24UthkAgIJvNFhoHg8FB408++URNTU167bXXdPz48QsK0d7epUAgeEHLDtdI+YePJr//pNURoma0b79YbTu73XbeD+SwhxLp6eny+/2hsd/vl9PpDI0bGhrk9/u1fPlyPfjgg/L5fCosLLzI2ACsFLYYsrOz1dLSoo6ODnV3d6upqUk5OTmh6SUlJWpsbFR9fb2qqqrkdDrldrujGhpAdIUthrS0NJWWlqqoqEhLlizRwoULlZmZqeLiYh06dCgWGQHE2JCernS5XHK5XIN+V11dbcx39dVXa+/evZFJBsAy3PkIwEAxADBQDAAMFAMAA8UAwEAxADBQDAAMFAMAA8UAwEAxADBQDAAMFAMAA8UAwEAxADBQDAAMFAMAA8UAwEAxADBQDAAMFAMAA8UAwEAxADAM6evjcWkbGOhXZ6df/f29VkcZErs9TomJyUpOvnLQn0NE7FAMY0Bnp1+XX56k8ePTR/x/tGAwqIGBfp08eUKdnX5NmuQMvxAijkOJMaC/v1fjx18x4ktBkmw2m+LjL1NKSqp6e3usjjNmUQxjxKVQCmey2eySYvMX0GGiGAAYKAYMi8dTp50737qodWzZ8oyOHPk4QokQDRQDhuXgwQ/V03Nxx/7vv/+eOEwY2bgqMcbV1+/Ujh3/Jbs9TpMmTVJp6b/p179+VdOnX6vCwpWSpE2bntb06ddq6tSr9cc//o/ef/89JSQk6MSJTh079oV8Pq/a2tr0zW9er8cfL9f48ckqKHDpmWe2aObMGyUpNP797/9bbW1+/fu/l6u8fKNmzbrJyrePc2CPYQw7cOB9ud1v6IUXXtbrr7+pe+65Vxs2/FTB4Nk/zefPv0vz5uXovvsKtXz5fZKkDz/8QBs3bpbbvUNxcXF69dX/PO9rrl79sCZPdqii4hlKYQSjGMaw995r1oIF92jixImSpPx8l/x+37DWcdddd2vSpFTZ7XYtXLhY+/a1RCMqYmxIxeDxeJSfn6/c3FzV1NQY03/3u99p8eLFWrRokdauXasvv/wy4kEReQMDAeMyZjAoeb3HdeY5gL6+vnOuIy4u7oxlA7Lbvx7bbLZBex7nWwdGnrDF4PV6VVlZKbfbrbq6OtXW1qq1tTU0vaurS08//bSqqqq0e/duzZgxQy+++GJUQyMysrLmas+eJnV2dkqSfvvb3bryyit1ww03hq4atLX59eGHH4SWiYuL08BAf2j8hz/8Xl1dXQoEAtq9u063336HJCklJSW0jg8+2K/29rZB6+jv/+c6MPKEPfnY3NysrKwspaSkSJLy8vLU0NCgdevWSfr6k6CiokJpaWmSpBkzZsjj8UQvMSLm29/O0n33FeqRR9YoEAgqJSVFW7ZUavz48dq48Undf/8yTZlylebMuTW0TFZWtl58sTI0njQpVevXl+jEiROaPfsWFRWtkiQ99FCJnntus+rrd2rGjJmaMeOG0DLz59+ljRuf1E9/+oS+852s2L1hDFnYYvD5fHI4HKGx0+nUwYMHQ+OJEyfqnnvukST19PSoqqpKK1eujEJURMPy5feFTiSe6aWXqs86/5133q0777xbkrRt28u69trr9Oijjxnz3XLLrXK73z7rOtaufURr1z5yEakRbWGLIRAYfBwaDAbPenvtyZMn9fDDD2vmzJlaunTpsEKkpiYPa36cn8MxYdDY57MrPj7y55ntdpvsdltU1v31+u3GexntRsr7DVsM6enp2r9/f2js9/vldA5+4s3n8+mBBx5QVlaWNmzYMOwQ7e1dCgRic8PLSPmHjya//+SgcSAQUH9/IOKvs2rVg5IUlXVLX+f+1/cy2rffv77faLHbbef9QA5b9dnZ2WppaVFHR4e6u7vV1NSknJyc0PSBgQGtWbNG3/3ud1VWVnbJPawDwBR2jyEtLU2lpaUqKipSX1+fCgoKlJmZqeLiYpWUlOj48eP66KOPNDAwoMbGRknSTTfdpE2bNkU9PIDoGNIt0S6XSy6Xa9Dvqqu/PjmVkZGhI0eORD4ZAMtw5yMAAw9RjUETrkjU5QmR3/Q9p/t18qvuIc3b1NSgN97Ypv7+fn3ve/ef9ZIprEMxjEGXJ8TL9ZP6iK/X84vFGso5db/fp+rq/9C2bb/WZZeN05o1P9Itt9yq6dOviXgmXBgOJRBz+/fv0y233KorrrhSiYmJuuuuu/Xuu3usjoUzUAyIubY2v1JTJ4fGqamT5fMN76lORBfFgJg72920djv3v4wkFANizulMG/S0ZUdHuyZPdpxnCcQaxYCYu/XW7+jAgffV2dmpnp4evfvuXt1221yrY+EMXJVAzDkcThUXr1VJyWr19fXL5VqsG2/ka95GEophDOo53S/PLxZHZb1DlZt7r3Jz7414BkQGxTAGnfyqe0j3G2Ds4hwDAAPFAMBAMQAwUAwADBQDAAPFAMDA5coxaOKV4xQ/LiHi6+3vPa3OL3uHNO+pU11as+ZH2rr1eU2ZclXEs+DiUAxjUPy4BB3dtDzi672m7G1J4Yvh8OH/1datz+jzzz+LeAZEBocSiDmPZ5ceffQxHpwawdhjQMw9/viTVkdAGOwxADBQDAAMFAMAA+cYxqD+3tP/fwUh8uvF6EAxjEFf32swtPsNomnHDo/VEXAOHEoAMFAMAAwUwxgRDAatjjAsl1re0YZiGAPs9jgNDAz9+xhHgr6+XsXFcQrMKhTDGJCYmKyTJ08oGAxYHSWsYDCo3t7TOnHCr+TkFKvjjFlU8hiQnHylOjv98nq/kDTyd9Hj4uI1YcJEJSaOtzrKmEUxjAE2m02TJjmtjoFLyJAOJTwej/Lz85Wbm6uamhpj+scff6xly5YpLy9PZWVl6u+/tI5nAQwWthi8Xq8qKyvldrtVV1en2tpatba2Dppn/fr1euqpp9TY2KhgMKjt27dHLTCA6At7KNHc3KysrCylpKRIkvLy8tTQ0KB169ZJko4dO6aenh7Nnj1bkrRs2TK98MILKiwsHHKIWP+lY+fExJi+XqyN9r8cPZq3X6y2XbjXCVsMPp9PDsc/v1DD6XTq4MGD55zucDjk9XqHFXLixNieZNpWnhvT14u11NRkqyNE1WjefiNl24U9lAgEArLZ/tkuwWBw0DjcdACXnrDFkJ6eLr/fHxr7/X45nc5zTm9raxs0HcClJ2wxZGdnq6WlRR0dHeru7lZTU5NycnJC06dOnaqEhAQdOHBAklRfXz9oOoBLjy04hJvSPR6PXn75ZfX19amgoEDFxcUqLi5WSUmJMjIydOTIEZWXl6urq0uzZs3S5s2bNW7cuFjkBxAFQyoGAGMLz0oAMFAMAAwUAwADxQDAQDEAMPDYdQT99a9/VWNjo44fPy673S6n06k77rhDGRkZVkcDhoU9hgipqanRo48+KknKyMjQrFmzJElPPvmkXnnlFSujAcPGfQwRkpeXp7q6OiUmDn7yr7u7W0uXLlVDQ4NFyTBUf//73887/aqrropREutxKBEh8fHxZ/2Cmp6eHl122WUWJMJwrV69Wp9++qmcTqfxLdU2m0179uyxKFnsUQwRsmbNGi1ZskRz586Vw+GQzWaTz+fTn/70J5WWllodD0Pw5ptvqrCwUBUVFZozZ47VcSzFoUQEeb1etbS0yOfzKRAIKD09XXPnzlVaWprV0TBEBw8e1FtvvaWf/exnVkexFMUAwMBVCQAGigGAgWJAWBUVFVqwYIHuuOMOHTp0yOo4iAGKAWHV1tbK7XZz2XUM4eQjzquwsFAHDhzQ9ddfr9bWVi1ZskRHjhxRb2+vVq1apYKCAp06dUpPPPGE/va3v8lut2vWrFnauHGj7HY+dy5VbDmcl9vtliS9/vrrmjJlihISErRr1y698sor+uUvf6m//OUveuedd3Tq1CnV19drx44dkqTPP//cyti4SBQDhmXFihWSpLS0NN1+++1qaWnRnDlz1NraqpUrV6qqqko//OEPNW3aNIuT4mJQDBiWMw8PAoGA4uPj9Y1vfEPvvPOOHnzwQXV1dWnVqlXau3evhSlxsSgGDMuuXbskff3AUUtLi+bOnSu3260nnnhC8+bN0/r16zVv3jx99NFHFifFxeBZCQzL6dOntXTpUvX19am8vFzTp09XWlqa9u3bp/z8fCUmJmrKlClauXKl1VFxEbgqAcDAoQQAA8UAwEAxADBQDAAMFAMAA8UAwEAxADBQDAAM/we91dW/TS8DzgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fbs=pd.crosstab(data['fbs'],data['output'])\n",
    "fbs.div(fbs.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True, figsize=(4,4))\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "gentle-inflation",
   "metadata": {},
   "source": [
    "## Following inferences can be made from the above bar plots:\n",
    "\n",
    "1. Blood sugar doesnot have that much impact on heart attack i.e. person having heart attack is independent of whether person have blood sugar or not.\n",
    "\n",
    "## 7. outputs vs restecg(resting electrocardiographic results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "still-reward",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "restecg=pd.crosstab(data['restecg'],data['output'])\n",
    "restecg.div(restecg.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True, figsize=(4,4))\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "correct-august",
   "metadata": {},
   "source": [
    "## Following inferences can be made from the above bar plots:\n",
    "\n",
    "1. People having mid restecg are highly probably to suffer from heart attack.\n",
    "\n",
    "## 8. outputs vs thalachh(heart rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "induced-fifth",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'output')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins=[50,100,150,200,250] # bin range\n",
    "\n",
    "data['extra_col']=pd.cut(data['thalachh'],bins)\n",
    "chol=pd.crosstab(data['extra_col'],data['output']) \n",
    "chol.div(chol.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True) \n",
    "\n",
    "plt.xlabel('chol') \n",
    "plt.ylabel('output')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sunrise-lodge",
   "metadata": {},
   "source": [
    "## Following inferences can be made from the above bar plots:\n",
    "\n",
    "1. people having higher heart rate are very much probable to suffer from heart attack.\n",
    "\n",
    "## 9. outputs vs exng(exercise induced angina) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "compliant-thickness",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "exng=pd.crosstab(data['exng'],data['output'])\n",
    "exng.div(exng.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True, figsize=(4,4))\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dietary-basin",
   "metadata": {},
   "source": [
    "## Following inferences can be made from the above bar plots:\n",
    "\n",
    "1. Person doesnot having exercied indeced enginal are higly probable to suffer from heart attack.\n",
    "\n",
    "## 10. outputs vs slp(Slope) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "dirty-friday",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQYAAAEFCAYAAAABoUmSAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAVSklEQVR4nO3df1TT570H8HdCBAV/QDFf0l9arTtaadC2riJtYXYdVEpUfnj9scqOeqOu9eSUdq60UGnXWq3HHVzX3nOEo+2ccIqzKmanBazO3tNC6+Buxat1Pei1Xa0kAawKQk34fu8f2zKzB8k3JiHJeL/+4snzfJ98eIzvfH8laBRFUUBEdA1tqAsgovDDYCAiAYOBiAQMBiISMBiISMBgICIBg4GIBLpQFwAAFy70QJbD53aKxMTR6OzsDnUZEYFrpU64rZNWq0FCQtx1+8MiGGRZCatgABB29YQzrpU6kbROPJQgIgGDgYgEYXEoQRQp+vtduHDBAZfrqk/b2e1ayLIcpKoGp9NFIyFBj6go9f/dGQxEPrhwwYGRI2MRF2eARqNRvZ1Op4XLNfTBoCgKenou4cIFB8aPv1n1dqoOJbq7u5GTk4Ovv/5a6Pv888+Rl5eHrKwslJSUwOVyqa+aKMK4XFcRFzfWp1AIJY1Gg7i4sT7v4XgNhs8++wxLly7F2bNnB+xfv349NmzYgPr6eiiKgj179vhUAFGkiZRQ+IcbqddrMOzZswdlZWWQJEnoO3fuHPr6+jBz5kwAQF5eHurq6nwugojCi9dzDBs3brxun91uh16vd7f1ej1sNpvPRSQmjvZ5m4HIrqvQ6qIDMpdePyYg8wSypkAJdE2BWKtIWSe7XQud7sYu5g20XW3tfjidThQU/IfqeRRF8dgLePXVl5GXl49p06ZfdxutVuvTv5NfJx9lWfYo8F8LVquzszsgN3/o9WNwZmO+3/ME0uSSd+FwXA51GR64TuoMtE6uuRZ8pwncebQ/ffLfmDRhAr47f1r1NjE33+lxIvPYsU8wf37uoCc3ZVn2WF+tVjPoG7JfwWAwGOBwONztjo6OAQ85iIaLgw2HsO/370MbpUXCuHF4yrwKu9/dh0kTJmDJwvkAgE2vv4FJEybgFkMSPv5jM5o/a0VMdDS+vXQJ5863w97Rgc4L32LKpDvw8yfXIi42FotXP4GXfv4Mpk25EwBQUGDCK6+8hg8//AM6Ohx46aVSlJb+AsnJdwfk9/DrBqdbb70VMTExaGlpAQDU1tYiPT09IIURRZr/aT2Od/bXYtvLZdhZvhWPpD+Iks1bcL1vVU1PnY0Hvj8Li0w5yM1+FADw5xMn8eL6p/HbN7YhKkqL3+zZO+hzrlnzJMaP16Os7JWAhQJwg8FgNptx/PhxAMDWrVuxadMmPProo7hy5QoKCwsDVhxRJPn0T3/G3AfSED9uHABg3sNz0dHV5dMcP0hLxU3x8dBqtXjskR/i2J/+HIRKvVN9KHHkyBH3z5WVle6fp02bhr17B081ouFAlmVodJ7/pRQFsHV0YNKE292POQe51ycqKspjvijt39+7NX87h+eew+kMUNUD42cliALk/ntm4shHH+PbixcBAO8d/gPGjhmNaVPuxF9O/+3kYkdXFz47cdK9TVRUFFz9/e72x8f+iO6eHsiyjN8fOoy0788CAMSPHeueo6WlGZ2dHZ5zBPjGQt4STRQg3585A4tMOXhqw0tQFAXxY8dic8lziIsdhZfLX8fjT1pgkCTcY/znuYDZ996DN9/6jbudEB+PZ1/ZhIuXLiFl+nQ8np8LAFhT+DjKt1fCWn8IdxlnYurUu9zbZGTMxS9+8QJ+9rPncP/9qQH5XTTh8AdneLlyaHGd1BlonS7PtSApPiEoz/fWO3tw8dIlPLX6Pwcd96+XK9Vob/8SBsNEd9vb5UoeShCRgIcSRGFixRL1dz8GG/cYiEjAYCAiAYOBiAQMBiIS8OQjkR/iJ03FyFExAZ+3r/c7fPt/fwn4vGoxGIj8MHJUDEzP1AZ8XusvF6ge29BQh127dsDlcmHRoqXIz/f/6gaDgSiCORx2VFb+F3bs+C1GjIjG2rUrce+9szBp0mS/5uU5BqII1tx8DPfeOwtjx47DqFGjMHfuD3H06GG/52UwEEWwjg4HEhPHu9uJieNht9v9npfBQBTBBvp6Ra3W/2+xZjAQRTBJSvL4CHZXVyfGj9cPsoU6DAaiCDZr1v1oafkjLly4gL6+Phw9egSzZ8/xe15elSDyQ1/vdz5dWvRlXjX0eglm8xOwWNbA6XTBZFqA6dP9/+5HBgORH0J5E9I/ZGY+iszMRwM6Jw8liEjAYCAiAYOBiAQMBiISMBiISMBgICIBL1cS+WH85CnQjYwN+LyuvivoONOmamxPTzfWrl2JLVu24eabbwnI8zMYiPygGxkblL/RMbnkXVXjTpz4X2zZ8gr++tevAvr8PJQgimBW6348/fSzAfl8xLW4x0AUwYqLXwjKvNxjICIBg4GIBAwGIhKoCgar1Yrs7GxkZmaiqqpK6D9x4gTy8/Mxf/58rFmzBpcuXQp4oUQ0dLyefLTZbCgvL8e+ffsQHR2NJUuWYPbs2ZgyZYp7zMaNG2GxWJCRkYHNmzdjx44dKCoqCmrhROHA1XdF9aVFX+f1xd691oA+v9dgaGxsRGpqKuLj4wEAWVlZqKurw7p169xjZFlGT08PAKC3txfjxo0LaJFE4UrtTUiRxuuhhN1uh17/z2ukkiTBZrN5jCkuLkZpaSkefPBBNDY2YsmSJYGvlIiGjNc9hoG+hfbadl9fH0pKSvD2228jJSUFb731Fp599llUVFSoLiIxcbSPZUcWvX5MqEuICFwn9XQ6364baLVan9bXazAYDAY0Nze72w6HA5IkudtffPEFYmJikJKSAgBYvHgxfvWrX/lSMzo7uyHLik/bDCRcX1gOx+VQl+CB66TOwOukCG+OoeByyarHKooCWZY91ler1Qz6huw1dtLS0tDU1ISuri709vaioaEB6enp7v6JEyeivb0dZ86cAQAcPnwYRqNRddFEkUR7yY4rThcUxf83sqGgKAp6ei5Bp4v2aTuvewxJSUkoKipCYWEhnE4nCgoKkJKSArPZDIvFAqPRiE2bNuGpp56CoihITEzEq6++esO/CFE4G3XciiswoXusBCA0ew065UvIsvo9Bp0uGgkJvn2WQtVnJUwmE0wmk8djlZWV7p8zMjKQkZHh0xMTRSLt1SuIa6kJaQ2TS94N+mEX73wkIgGDgYgEDAYiEjAYiEjAYCAiAYOBiAQMBiISMBiISMBgICIBg4GIBAwGIhIwGIhIwGAgIgGDgYgEDAYiEjAYiEjAYCAiAYOBiAQMBiISMBiISMBgICIBg4GIBAwGIhIwGIhIwGAgIgGDgYgEqv5EHf17kZ1XMbnk3VCX4UF2Xg11CXQNBsMwpB0RDdMztaEuw4P1lwsAfBfqMujveChBRAIGAxEJGAxEJGAwEJFAVTBYrVZkZ2cjMzMTVVVVQv+ZM2ewfPlyzJ8/H6tWrcLFixcDXigRDR2vwWCz2VBeXo7q6mocOHAANTU1aGtrc/crioKf/vSnMJvNOHjwIO666y5UVFQEtWgiCi6vwdDY2IjU1FTEx8cjNjYWWVlZqKurc/efOHECsbGxSE9PBwCsXbsWP/7xj4NXMREFnddgsNvt0Ov17rYkSbDZbO72V199hfHjx+P5559Hbm4uysrKEBsbG5xqiWhIeL3BSZZlaDQad1tRFI+2y+XCsWPHsHv3bhiNRmzbtg2bN2/G5s2bVReRmDjax7Iji14/JtQlRASuk3rBXiuvwWAwGNDc3OxuOxwOSJLkbuv1ekycOBFGoxEAkJOTA4vF4lMRnZ3dkGXFp20GEq4vLIfjcqhL8MB1Uidc1wnwf620Ws2gb8heDyXS0tLQ1NSErq4u9Pb2oqGhwX0+AQDuuecedHV14dSpUwCAI0eOIDk52a+iiSi0vO4xJCUloaioCIWFhXA6nSgoKEBKSgrMZjMsFguMRiPefPNNlJaWore3FwaDAVu2bBmK2okoSFR9iMpkMsFkMnk8VllZ6f55xowZ2Lt3b2ArI6KQ4Z2PRCT4t/rYNb9ngCgw/q2Cgd8zQBQYPJQgIgGDgYgEDAYiEjAYiEjAYCAiAYOBiAQMBiISMBiISMBgICIBg4GIBAwGIhIwGIhIwGAgIgGDgYgEDAYiEjAYiEjAYCAiAYOBiAQMBiISMBiISMBgICIBg4GIBAwGIhIwGIhIwGAgIgGDgYgEDAYiEjAYiEjAYCAiAYOBiASqgsFqtSI7OxuZmZmoqqq67rijR4/i4YcfDlhxRBQaOm8DbDYbysvLsW/fPkRHR2PJkiWYPXs2pkyZ4jGuo6MDr732WtAKJaKh43WPobGxEampqYiPj0dsbCyysrJQV1cnjCstLcW6deuCUiQRDS2vewx2ux16vd7dliQJra2tHmN27dqF6dOnY8aMGTdURGLi6BvaLlLo9WNCXUJE4DqpF+y18hoMsixDo9G424qieLS/+OILNDQ04O2330Z7e/sNFdHZ2Q1ZVm5o22uF6wvL4bgc6hI8cJ3UCdd1AvxfK61WM+gbstdDCYPBAIfDcU1BDkiS5G7X1dXB4XAgPz8fq1evht1ux7Jly/wqmohCy2swpKWloampCV1dXejt7UVDQwPS09Pd/RaLBfX19aitrUVFRQUkSUJ1dXVQiyai4PIaDElJSSgqKkJhYSEWLlyInJwcpKSkwGw24/jx40NRIxENMa/nGADAZDLBZDJ5PFZZWSmMu+2223DkyJHAVEZEIcM7H4lIwGAgIgGDgYgEDAYiEjAYiEjAYCAiAYOBiAQMBiISMBiISMBgICIBg4GIBAwGIhIwGIhIwGAgIgGDgYgEDAYiEjAYiEjAYCAiAYOBiAQMBiISMBiISMBgICIBg4GIBAwGIhIwGIhIwGAgIoGqP1FHNBzJzquYXPJuqMsQyM6rQX8OBgPRdWhHRMP0TG2oyxBYf7kAwHdBfQ4eShCRgMFARAIGAxEJGAxEJFAVDFarFdnZ2cjMzERVVZXQ/8EHH2DBggWYP38+nnjiCVy8eDHghRLR0PEaDDabDeXl5aiursaBAwdQU1ODtrY2d393dzdefPFFVFRU4ODBg5g6dSp+/etfB7VoIgour8HQ2NiI1NRUxMfHIzY2FllZWairq3P3O51OlJWVISkpCQAwdepUnD9/PngVE1HQeQ0Gu90OvV7vbkuSBJvN5m4nJCTgRz/6EQCgr68PFRUVeOSRR4JQKhENFa83OMmyDI1G424riuLR/ofLly/jySefxLRp05Cbm+tTEYmJo30aH2n0+jGhLiEicJ3UC/ZaeQ0Gg8GA5uZmd9vhcECSJI8xdrsdq1atQmpqKp5//nmfi+js7IYsKz5v96/C9YXlcFwOdQkeuE7qhOs6Af6vlVarGfQN2euhRFpaGpqamtDV1YXe3l40NDQgPT3d3d/f34+1a9di3rx5KCkpGXBvgogii9c9hqSkJBQVFaGwsBBOpxMFBQVISUmB2WyGxWJBe3s7Tp48if7+ftTX1wMA7r77bmzcuDHoxRNRcKj6EJXJZILJZPJ4rLKyEgBgNBpx6tSpwFdGRCHDOx+JSMBgICIBg4GIBAwGIhIwGIhIwGAgIgGDgYgEDAYiEjAYiEjAYCAiAYOBiAQMBiISMBiISMBgICIBg4GIBAwGIhIwGIhIwGAgIgGDgYgEDAYiEjAYiEjAYCAiAYOBiAQMBiISMBiISMBgICIBg4GIBAwGIhIwGIhIwGAgIgGDgYgEDAYiEjAYiEigKhisViuys7ORmZmJqqoqof/zzz9HXl4esrKyUFJSApfLFfBCiWjoeA0Gm82G8vJyVFdX48CBA6ipqUFbW5vHmPXr12PDhg2or6+HoijYs2dP0AomouDTeRvQ2NiI1NRUxMfHAwCysrJQV1eHdevWAQDOnTuHvr4+zJw5EwCQl5eH119/HcuWLVNdhFar8b3y65ASRgVsrkAJ5O8XKFwndcJxnQD/18rb9l6DwW63Q6/Xu9uSJKG1tfW6/Xq9HjabzaciExLifBo/mB2lmQGbK1ASE0eHugQB10mdcFwnIPhr5fVQQpZlaDT/TBdFUTza3vqJKPJ4DQaDwQCHw+FuOxwOSJJ03f6Ojg6PfiKKPF6DIS0tDU1NTejq6kJvby8aGhqQnp7u7r/11lsRExODlpYWAEBtba1HPxFFHo2iKIq3QVarFdu3b4fT6URBQQHMZjPMZjMsFguMRiNOnTqF0tJSdHd3Izk5GZs2bUJ0dPRQ1E9EQaAqGIhoeOGdj0QkYDAQkYDBQEQCBgMRCRgMRCTwekv0cHD69GnU19ejvb0dWq0WkiThoYcegtFoDHVpFIE++OADnD9/HhkZGZgwYYL78ZqaGixevDiElak37PcYqqqq8PTTTwMAjEYjkpOTAQAvvPACdu7cGcrSKAJt3boVu3fvxtmzZ7F06VLU1ta6+955550QVuabYb/HsGvXLhw4cACjRnl+im7FihXIzc3FypUrQ1RZ+Pnmm28G7b/llluGqJLw9eGHH2L//v3Q6XRYvnw5Vq5ciejoaMybNw+RdMvQsA8GnU434BfL9PX1YcSIESGoKHytWbMGZ8+ehSRJwotco9Hg8OHDIaosfFz7IcI77rgD27dvx4oVK3DTTTdF1IcLh/2dj1arFdu2bcOcOXOg1+uh0Whgt9vxySefoKioCI899lioSwwb3d3dWLZsGcrKynDfffeFupyw9MYbb6CxsRHFxcVISUkBALS0tGDdunW4evWq+zNF4W7YBwPwt2+pampqgt1uhyzLMBgMmDNnDpKSkkJdWthpbW3F7373O7z88suhLiVsNTU1QZIk3Hnnne7Hzp8/j507d6KkpCSElanHYCAiwbC/KkFEIgYDEQkYDOSzTz/9FDk5OaEug4KIwUBEgmF/HwMNrqenB8899xy+/PJLaLVaJCcne1zCLS4uRkxMDE6dOoXOzk488MADKC0t5T0gEY57DDSoQ4cOoaenB7W1tdi7dy8A4Ouvv/YY09raip07d+K9997D6dOnUVNTE4pSKYAYDDSo++67D21tbVi+fDkqKirwk5/8xOODQQCQm5uLuLg4REdHY8GCBfjoo49CVC0FCoOBBnX77bfj0KFDWL16Nbq7u7FixQr09PR4jImKinL/rCgKtFq+rCIdzzHQoKqrq9HS0oKtW7fioYceQmdnJ06ePOkx5v3338eiRYugKAr279+PvLy8EFVLgcJop0EtXLgQ/f39yM7ORl5eHi5fvoypU6d6jBk5ciSWLVsGk8mEWbNmIT8/P0TVUqDwlmjyS3FxMb73ve9h1apVoS6FAoh7DEQk4B4DEQm4x0BEAgYDEQkYDEQkYDAQkYDBQEQCBgMRCf4fkOZUCbCFwyQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "slp=pd.crosstab(data['slp'],data['output'])\n",
    "slp.div(slp.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True, figsize=(4,4))\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "devoted-stadium",
   "metadata": {},
   "source": [
    "## 11. outputs vs caa(number of major vessels) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "previous-bikini",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "caa=pd.crosstab(data['caa'],data['output'])\n",
    "caa.div(caa.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True, figsize=(4,4))\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "perfect-initial",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\n",
    "sns.heatmap(data.corr(),cmap=\"Blues\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "agricultural-partnership",
   "metadata": {},
   "source": [
    "# Next Section\n",
    "## Missing value imputation\n",
    "After exploring all the variables in our data, we can now impute the missing values and treat the outliers because missing data and outliers can have adverse effect on the model performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "intense-burns",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age          0\n",
       "sex          0\n",
       "cp           0\n",
       "trtbps       0\n",
       "chol         0\n",
       "fbs          0\n",
       "restecg      0\n",
       "thalachh     0\n",
       "exng         0\n",
       "oldpeak      0\n",
       "slp          0\n",
       "caa          0\n",
       "thall        0\n",
       "output       0\n",
       "extra_col    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "portuguese-conviction",
   "metadata": {},
   "source": [
    "# Next Section\n",
    "## Outlier Treatment\n",
    "As we saw earlier in univariate analysis, chol contains outliers so we have to treat them as the presence of outliers affects the distribution of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "hollow-butterfly",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "63.5 (303, 15)\n"
     ]
    }
   ],
   "source": [
    "Q1 = data.quantile(0.25).loc['chol']\n",
    "Q3 = data.quantile(0.75).loc['chol']\n",
    "IQR = Q3 - Q1\n",
    "print(IQR,data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "enhanced-rebate",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(298, 15)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data[~((data.chol < (Q1 - 1.5 * IQR)) | (data.chol > (Q3 + 1.5 * IQR)))]\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "alike-presentation",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}