neural network.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import seaborn as sns # used for plot interactive graph.\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import average_precision_score\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.metrics import precision_recall_curve\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import auc\n",
    "from sklearn.svm import SVC\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>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  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   0     1       1  \n",
       "1   0     2       1  \n",
       "2   0     2       1  \n",
       "3   0     2       1  \n",
       "4   0     2       1  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('https://raw.githubusercontent.com/Monk5088/College-project/master/heart.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "#It's a clean, easy to understand set of data. However, the meaning of some of the column headers are not obvious. Here's what they mean,\n",
    "\n",
    "#1.age: The person's age in years\n",
    "\n",
    "#2.sex: The person's sex (1 = male, 0 = female)\n",
    "\n",
    "#3.cp: The chest pain experienced (Value 1: typical angina, Value 2: atypical angina, Value 3: non-anginal pain, Value 4: asymptomatic)\n",
    "\n",
    "#4.trestbps: The person's resting blood pressure (mm Hg on admission to the hospital)\n",
    "\n",
    "#5.chol: The person's cholesterol measurement in mg/dl\n",
    "\n",
    "#6.fbs: The person's fasting blood sugar (> 120 mg/dl, 1 = true; 0 = false)\n",
    "\n",
    "#7.restecg: Resting electrocardiographic measurement (0 = normal, 1 = having ST-T wave abnormality, 2 = showing probable or definite left ventricular hypertrophy by Estes' criteria)\n",
    "\n",
    "#8.thalach: The person's maximum heart rate achieved\n",
    "\n",
    "#9.exang: Exercise induced angina (1 = yes; 0 = no)\n",
    "\n",
    "#10.oldpeak: ST depression induced by exercise relative to rest ('ST' relates to positions on the ECG plot. See more here)\n",
    "\n",
    "#11.slope: the slope of the peak exercise ST segment (Value 1: upsloping, Value 2: flat, Value 3: downsloping)\n",
    "\n",
    "#12.ca: The number of major vessels (0-3)\n",
    "\n",
    "#14.target: Heart disease (0 = no, 1 = yes)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1008x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.describe()\n",
    "plt.figure(figsize=(14,10))\n",
    "sns.heatmap(df.corr(),annot=True,cmap='hsv',fmt='.3f',linewidths=2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>cp</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.272727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.820000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.793103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.695652</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cp    target\n",
       "0   0  0.272727\n",
       "1   1  0.820000\n",
       "2   2  0.793103\n",
       "3   3  0.695652"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('cp',as_index=False)['target'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>slope</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.753521</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   slope    target\n",
       "0      0  0.428571\n",
       "1      1  0.350000\n",
       "2      2  0.753521"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('slope',as_index=False)['target'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.333333</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.783133</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.239316</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   thal    target\n",
       "0     0  0.500000\n",
       "1     1  0.333333\n",
       "2     2  0.783133\n",
       "3     3  0.239316"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "df.groupby('thal',as_index=False)['target'].mean()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>56.601449</td>\n",
       "      <td>0.826087</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>134.398551</td>\n",
       "      <td>251.086957</td>\n",
       "      <td>0.159420</td>\n",
       "      <td>0.449275</td>\n",
       "      <td>139.101449</td>\n",
       "      <td>0.550725</td>\n",
       "      <td>1.585507</td>\n",
       "      <td>1.166667</td>\n",
       "      <td>1.166667</td>\n",
       "      <td>2.543478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>52.496970</td>\n",
       "      <td>0.563636</td>\n",
       "      <td>1.375758</td>\n",
       "      <td>129.303030</td>\n",
       "      <td>242.230303</td>\n",
       "      <td>0.139394</td>\n",
       "      <td>0.593939</td>\n",
       "      <td>158.466667</td>\n",
       "      <td>0.139394</td>\n",
       "      <td>0.583030</td>\n",
       "      <td>1.593939</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>2.121212</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              age       sex        cp    trestbps        chol       fbs  \\\n",
       "target                                                                    \n",
       "0       56.601449  0.826087  0.478261  134.398551  251.086957  0.159420   \n",
       "1       52.496970  0.563636  1.375758  129.303030  242.230303  0.139394   \n",
       "\n",
       "         restecg     thalach     exang   oldpeak     slope        ca      thal  \n",
       "target                                                                          \n",
       "0       0.449275  139.101449  0.550725  1.585507  1.166667  1.166667  2.543478  \n",
       "1       0.593939  158.466667  0.139394  0.583030  1.593939  0.363636  2.121212  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('target').mean()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\akhil\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['target'],rug=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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sY0Z28FyzMDkjnvGjY3XUzDBouAew8roWXt9ZzfJ5uSRGW9fiykyMYmJ6HOsP1dLRpePeg82O8npCBKYFQZdMDxHh2llZbD16irLaFqvLCUoa7gHs8Q8PEyLwxfPGWl0K5xWm0dLRzbayU1aXoobAaQw7KuopGBX7iSUXg8Gyma6Zw18t0tb72dBwD1B1zR08t7mMZTOzyEiIsroc8lKiyUmK4sOSkzh1zpmgUV7XQn1LZ1CcSO0rKzGK+WOTeXV7pc5zdBY03APU0+uO0Nbp5CvnW9fX3puIcF5hGnXNHRRXNVpdjvJQUXk9YQ5hcoa1S+mdrWtnZVF6spndlfqZGyoN9wDU1tnNnzcc5eKJoygcHWd1OR+bnBlPSkw4aw6c0JZUEOh2GnZVNjApI56IMIfV5ZyVpVPTCXMIr2nXzJBpuAeglTuqqGvu4IvnWt/X3luICOcWplJZ30pZnZ7kCnQHa5po6ehmZhB2yfRIjA7ngvGj+NvOKl0hbIg03AOMMYanPjrC+NGxLByXYnU5nzIrJ4nIsBDWHaq1uhQ1iKLyeqLCHBSM9v1cRL60bGYmxxvb2XhYP3NDoeEeYDYfOcWe6kbuWDQ2IC8TDw8NYe6YZIqrGnQx4wDW3tXN3upGpmUnEBoS3D/ml0waTXS4g5VFur7qUAT3u25DT607TEJUGNfNyrK6lAEtyE/BGLQlFcD2VjfS2W2CcpRMX1HhDi6f4popsr1Lr5L2lIZ7AKmsb+XN4uPcMi+HqPDAPQGWHBPOxPQ4Nh+uo7NbL2oKRDvKG0iMCmNMSrTVpXjFspmZNLZ18cF+XbzdUxruAeQvG45ijOHzC8ZYXcqgFo5Lpbmjm13uCalU4Djd3sXBmiamB/CiHEO1uCCVlJhwXtuhXTOe0nAPEG2d3azYVMZlk9PJTgr81ta4tBhGxUWwrvSkDosMMLsrXYtyBOI6qWcrzBHCVdMzeGfPcU63d1ldTlDQcA8QrxVVUt/SyR2L86wuxSMiwsJxKVTVt+mwyABTVF7P6PgI0gN8UY6hWjYzk/YuJ28VH7O6lKCg4R4AjDE8+dERJmXEM39sstXleEyHRQaeuuYOyupagnps+0Bm5yaRnRTFazpqxiMa7gFgQ2kd+441ceeivIAc/jgQHRYZeHZW1AMw3UZdMj1EhGtmZPJhyUlOnm63upyAp+EeAJ5ed4Sk6DCumZlpdSlD1jMscpMOi7ScMYai8nrGpESTZOEU0b60bGYW3U7DKl3EY1Aa7harqm/lrT3HuPmcXCKDcP6P5JhwJqTHselwHV06LNJSxxrbqGlqt8XY9oFMSI9jYnqcds14QMPdYs9uLAPgc/NzLa7k7C0cl+IaFlmpwyKtVFTmXpQjK3hWXDob18zMZOvRU5Trifwz0nC3UHuXa/jjxZNGk5Mc+MMfBzIuLZbU2Ag2lGrXjFWcxlBUUc+E0XHEBNmiHEN19XRX9+VKHfN+RhruFlq1q5ra5g5uWxj4Fy2dSYgIC/KTKT/VSsUpbU1Z4VDNaZraupiVm2R1KT6XkxzN3DFJOtfMIDTcLfT0uqPkp8WweFyq1aUM2+zcJMJDQ7T1bpHt5fVEhoUwMT1w5v/3pWUzM9l/vIl9x3QRj4FouFtkZ0U9ReX13LZgDCEhwTP8cSCRYQ5m5SSys6KBZr2C0K/au7oprmpgelYioY6R8SN95bQMHCGiJ1bPYGR8EgLQM+uPEh3u4Po52VaX4jUL8lPochq2HNVFtP2puMo1A+SsXPuOkukrJTaC8wpTWVlUhVMX8eiXhrsF6po7WLmjiutnZxEfGWZ1OV4zOj6S/NQYNpbW6iLafrS97BTJMeHkBvFJ+bNx3awsKutb2aDXWPRLw90CL2wpp6PLyW0L86wuxesW5KdQ39rJvuomq0sZERpaOyk90czMnMSgurrZGy6fkk5cZCgvbamwupSApOHuZ91Ow5/XH2VBfjLjA2jxa2+ZlBFPQlSYnlj1k6Lyegwwy4bTDQwmMszB1TMyWbW7mqY2nf6iLw13P3t3Xw2V9a3cbsNWO4AjRJg/NpmSE6epaWqzuhxbM8awvewUucnRpMRGWF2OJW6ck01bp5M3dup0BH1puPvZM+uPkB4fyaWTR1tdis/MzUvGESJsLK2zuhRbq2pwTTcwkk6k9jUrJ5FxaTG8uFW7ZvrScPejQydOs/bgST43P9fWQ9ZiI0KZnpXAtrJTtHfqmpe+UlR2CkeI2H66gTMREW6am8PWo6c4dOK01eUEFPsmTAD68/qjhDmEW+YF7zwynlqQn0J7l5Pt5fVWl2JL3U5DUUUDE9PjiA6393QDg7l+VhaOEOElbb1/goa7nzS3d/HXrRVcOS2DtDj794/mJEeTnRTF+tJaXYbPBw4eb6K5vYtZOfafbmAwo+IjuWB8Gi9vq6Bbx7x/zKNwF5ErRGS/iJSIyAP9PH6HiJwQkSL315e8X2pwe2V7JU3tXbYc/jiQBfkpnGhqp/Rks9Wl2M7mo6eIjQhlwgiZbmAwN83J5nhjO2sPnrC6lIAxaLiLiAP4PbAUmAwsF5HJ/Wz6vDFmpvvrMS/XGdSMMTy97ghTMuOZPYJOfk3LSiA63MF6XYbPqxrbOtl/rJHZuUk4bDB1hTdcPGk0SdFhemK1F09a7vOAEmNMqTGmA3gOWObbsuzl/QMnOFhzmi+dN3ZEXWgS5gjhnLxk9lY3UtfcYXU5trHt6CmcBubmaZdMj/DQEJbNzOLt4uPUt+hnDTwL9yygvNf3Fe77+rpBRHaKyEsiktPfC4nIXSKyRUS2nDgxcv58enRNKenxkXxmevAtozdcC/JTCBHho5KTVpdiC07jmrtnbGoMqSN0bPtAbpqbTUe3U+d5d/Mk3PtravY9a/E3IM8YMx14B3i6vxcyxjxijJlrjJmblpY2tEqD1O7KBtYdquXOxXmE2Xj440ASosKYkZPAlqN1tOhskcN2+GQzdc0dnKOt9k+ZkpnA5Ix4nt9crifx8SzcK4DeLfFs4BO/Go0xtcaYnuXIHwXmeKe84Pfo2lJiI0JZHsTL6A3XuYVpdHYbNhzWi5qGa9PhOiLDQpiSOXLHtp/J8vm5FFc1UqRDcD0K981AoYiMFZFw4BZgZe8NRCSj17fXAHu9V2Lwqqxv5fWd1dxyTo6tZn8cqvT4SMaPjmV9aS2duoj2WWts66S4qoE5uUkj8q9AT1w3K4uYcAd/3nDU6lIsN+gnxBjTBdwHvIkrtF8wxhSLyI9F5Br3Zl8TkWIR2QF8DbjDVwUHkyc/PAzAneeOtbgS651XmEZzexdFZdqiOlubj9ThNDA/P8XqUgJWbEQo18/O5vWd1Zwa4SfxPfr1b4xZZYwZb4wZZ4z5qfu+HxhjVrpvP2iMmWKMmWGMWWKM2efLooNBQ2snKzaV8ZnpGWQlRlldjuXyU2PITIxkbclJnev9LHR2O9l8uI7CUbF6InUQty4YQ0eXkxe3lg++sY3p33Y+8tymMpo7uvnyeflWlxIQRITzCtM4ebpd53o/C2/vOU5jWxcLtNU+qAnpcczLS+YvG8pG9CpNGu4+0NHl5MmPjrBoXApTR/CkTn1NzUwgKTpMryI8C8+sP0JidJhekeqhWxeOoayuhff211hdimU03H3g9Z1VHGts48vna6u9N0eIsLgglaN1LZTV6pQEntpT1ciG0joWjHVdM6AGt3RqOhkJkTzuPu81Emm4e5nTafjD+4eYMDqOC8ePjLH8QzFnTBJRYQ7WHNSLmjz12NpSosMdnJOXbHUpQSPMEcIdi/JYd6iW4qoGq8uxhIa7l63aXU1JzWnuu6hgRE014KmIUAcL8lPYU91IdUOr1eUEvOqGVlbuqOLmc3KICndYXU5QuWVeLtHhjhHbetdw9yKn0/Db1SUUjIrlymkZgz9hhFpckEJEaAjv7hu5/aGeemrdEZzG8IXFOpx2qBKiwviXuTn8bUcVxxtH3pKPGu5e9GbxMfYfb+L+iwp0tr4ziA4PZdG4VIqrGtlb3Wh1OQHrdHsXz24sY+m0DHKSo60uJyh9YfFYupyGp9YdsboUv9Nw9xKn0/DQuyXkp8aMyAnChqqn9f7bdw9aXUrAWrGxjKa2Lh1OOwy5KdFcOS2DP68/OuJmi9Rw95LXd1Wzt7qRr11cqK12D/S03lftOsaeKm2999Xa0c2f1pRybkEqM3NGzhoAvnDfkgJOt3fx5EdHrC7FrzTcvaCz28n/vLWfielxXDNDW+2eOrcglYSoMH72jxF/QfOnPLupjJOn2/n6JYVWlxL0JmXEc+nk0Tz50WGa2jqtLsdvNNy94LnN5RytbeHbV0wgRFvtHosKd3DfkgI+OHCCdTrf+8faOrt5+INDLMxP0eGPXvK1iwppbOvimfUjZ0IxDfdhauno4qHVB5mXl8ySCaOsLifofH7hGDITIvnvf+wb0ZeK9/bcpjJONGmr3ZumZSdw4YQ0HltbOmJa7xruw/TImlJONLXz7Ssm6Lj2sxAZ5uCbl01gZ0UDb+yqtrocy7V0dPH79w8xf2yyziPjZd+8dDynWjp5dO3IGPeu4T4MlfWtPPzBIa6alsFc/fP5rF03K4uJ6XH899/30drRbXU5lnps7WF3Y2Gi1aXYzvTsRK6ansFja0upabL/uHcN92H4r1V7MQYevFJ/EIfDESL86JopVNa38scPDlldjmVONLXzpw8OsXRqOnPG6DJ6vvBvl02go8vJb1eXWF2Kz2m4n6WNpbW8sbOauy8YR3aSXmAyXAvyU7h6RiYPf3CIstoWq8uxxG9WH6C9y8m/Xz7B6lJsa2xqDMvn5bJiUxmHT9p78joN97PQ0eXkhyuLyUyI5O4Lxlldjm1898qJhIYIP3ljj9Wl+N3B402s2FTOZ+fnkp8Wa3U5tnb/xQVEhIbwk9f32HohbQ33s/DImkPsO9bEfyybqpM5eVFGQhT3X1TI23uO84/dI+fkqjGG77+6m9iIUL5+sY6Q8bVRcZH866XjeXdfDe/ste/8RhruQ1RSc5qHVpdw1fQMLp082upybOdL541lSmY833+1eMRcLv7ytko2Hq7jgaUTSdEl9Pzi9kV5jB8dy49WFtv2JL6G+xA4nYYHX95JVLiDH109xepybCnMEcLPb5xOfUsHP37d/t0z9S0d/HTVXmbnJnLz3Byryxkxwhwh/HjZVCrrW/n9e/Y8uarhPgSPri1l85FTfP+qSaTFaQvLV6ZkJnDPheN4eVsl7+w5bnU5PvXTN/bS0NrJf147Ta9u9rMF+SlcPyuLhz84xK4K+y3ooeHuoZ0V9fzizf0snZrOjXOyrS7H9u69qIDJGfH8+0s7ONZgzzHJbxYf48WtFXzl/HwmZ8ZbXc6I9IOrJ5McE863XiyirdNe3TMa7h5obu/i688VkRYXwf+7fppeieoHEaEOfvvZWbR3Ofn6c9vpttnUBDVNbTz48i6mZsXzjUvGW13OiJUYHc7PbpzOgeOn+fU7B6wux6s03AdhjOG7r+ziSG0zv755JonR4VaXNGKMS4vlJ8umsvFwHQ+tts+8706n4YG/7qK5vYtf/8tMwkP1x9BKSyaMYvm8HB5ZU8pHNprATj9Vg/jTmlJeK6riW5eO17k+LHDDnGxumJ3Nb1YftM3wyN+/V8K7+2r47pWTKBwdZ3U5Cvj+VZMpSIvl/hXbqaq3x9q+Gu5n8N6+Gn72j318ZnoG9y4psLqcEeun101lZk4i//r8DnZXBveJr3f3HedX7xzgullZ3LZwjNXlKLeYiFD+eOscOrqc3PN/22jvCv7+dw33AeyqaOD+FduZnBHPL26cof3sFooMc/DIbXNIig7jy89sCdqWVUnNab6+oogpmfF67iYAFYyK5Zc3TaeovJ7vvbI76K9e1XDvR0nNaW5/chMJUWE8dvtcvQo1AIyKi+TR2+dyuq2Lzz22Mehm9ausb+W2xzcSERbCw7fOITJMP1OB6IqpGXz94kJe2lrBL97cb3U5w6Lh3kd5XQuff3wjISL835fmk5EQZXVJym1KZgJP3nkOxxrauPWxjdQ1B8cVrCdPt/P5xzbS1N7F01+YpxPNBbhvXFLI8nm5/OH9QzzxYfDO/a7h3sv+Y03c8Md1tHR08+cvziM+AghIAAAOmklEQVQvNcbqklQfc/OSefz2uRypbeHmP60P+C6amkbXL6KqhlaeuOMcpmQmWF2SGoSI8J/XTuXyKaP58et7eDxIA17D3W3r0TpuengdIvDi3QuZlKEXlQSqRQWpPH3nPI41tHH9H9ax/1iT1SX16/DJZm54eB1ldS08dts5uh5qEHGECA8tn8XSqen85PU9/Oadg0HXB6/hDqzYVMbyRzaSHBPOS3cvYrwNh6d995VdVpfgVQvHpfDC3QtxGsMNf1zH3wNsib6NpbXc9PA6mtu7WfHlBZxbmGp1ST6T98AbVpfgExGhDn67fBY3zsnm1+8c4Dt/3RlUV7GO6HBv6ejiOy/t5MGXdzE/P5lX7llMTrL2hwaLSRnxvHLvYgpGxfLV/9vGj1YWWz6Ezek0/OH9EpY/uoH4yDBevHshM3ISLa1Jnb1QRwg/v2E6919UwAtbKoKiK7DHiA339YdqueJ/1/L8lnLuXTKOp+6cR1KMXn0abLISo3jhKwv5wuKxPLXuCFf+Zi2bDtdZUsvhk83c+vhGfv6P/SydlsFr9y1mnC68EfRCQoRvXTaBP31+DodONHPF/67hpa0VAd9NM+LCvaq+lX97cQfLH92ACDx/1wL+/fKJOHRGvqAVHhrCD66ezFN3nkN7l5N/+dN6vvXCDsrr/LNc3+n2Ln7zzkEu/9817Kpo4KfXTeV3y2cRFxnml/0r/7h8Sjp/u/9cJqbH828v7uCOJzdTUnPa6rIGFGp1Af5S3dDKkx8d4el1RzDAVy7I5xsXj9cx7DZy4YRRvPWv5/PQ6hKe+OgwK3dUctPcHL6wOI+CUd4/j9LQ2smzG8t4ZM0hTrV0ctW0DH549WRGxUd6fV8qMIxNjeG5uxbwzPoj/PKtA1z26w+4aU4O911UEHBdurYO926nYUNpLc9vLmfVrmqcxnDtzCy+edl4HWtsU9HhoTywdCJ3LMrj9++V8Pzmcp7dWMb8sclcPzuLiyeNJnUYqx11dDnZdLiOl7dV8Mauatq7nFw4IY1vXDKemdq3PiKEhAh3LB7L1TMy+d17Jfxlw1Fe3FrOpZNH8/kFeSwclxIQPQG2C/e65g7WH6rlo0Mneav4OCdPtxMXEcrti/K4Y1FewP12Vb6RnhDJT66dytcvcV1tuGJTGd/56y5EdjE9O5HZuYnMzElkXFosY1Ki++1C6ehycryxjcMnm9lV2UBReT3rSk7S3NFNXEQoN83N5pZzcpmapWPXR6KU2Ah+ePUUvnxePn/ZcJQVm8p4s/g4qbHhXDYlnfMLU5k3NoVki87leRTuInIF8BvAATxmjPnvPo9HAM8Ac4Ba4GZjzBHvlupSe7qd/ceaaOnopqWzm5rGNqob2jha28ze6iYq3WeyYyNCOa8wlWtmZLJk4ii93HuESo2N4O4LxvGV8/PZW93EW3uO8VHJSVZsKuPJj458vF1UmIOYiFCiwkPo7DK0dXXT0NpJ73NmeSnRXDsriyUTRrG4IFW79BQAmYlRfPuKiXzt4kLe21fD67uqeXV7Jc9uLANgTEo0haPiKBgVy+j4CFJjI5iUEU/BKN+ebB803EXEAfweuBSoADaLyEpjTO8FLr8InDLGFIjILcDPgJt9UfD60lrue3b7J+6LDAshJymaOWOSuHXBGOaNTWZGdgKhjhF3vlgNQESYnBnP5EzX4hid3U4OHj/N0dpmjta1UHu6ndPt3bR1dhPuCCE8NISU2HAyE6LISY5mcmY8CVF6glQNLDLMwdJpGSydlkFHl5NdlfVsKK1jT1UjB4438cGBGjq7Xa2Fr144ju9cMdGn9XjScp8HlBhjSgFE5DlgGdA73JcBP3Lffgn4nYiI8cFYoQX5Kaz48gJiIhxEhztIiYkgMTpMZ9hTQxLmCPk47JXytvDQEOaMSWbOmH9elex0GupbOznR1E5cpO97xD3ZQxZQ3uv7CmD+QNsYY7pEpAFIAby+rElqbMSwTogppZQVQkKE5Jhwv/XBexLu/TWJ+7bIPdkGEbkLuMv97WkRGc6cmqn44JeHFwRsXZ/7WeDV9bkAPl4EXl2BWBNAqgTgZ4sAPl4Mry6PVnnxJNwrgJxe32cDVQNsUyEioUAC8KnLBI0xjwCPeFLYYERkizFmrjdey5u0rqHRujwXiDWB1jVU/qrLkzOOm4FCERkrIuHALcDKPtusBG53374ReNcX/e1KKaU8M2jL3d2Hfh/wJq6hkE8YY4pF5MfAFmPMSuBx4M8iUoKrxX6LL4tWSil1Zh6dsjXGrAJW9bnvB71utwE3ebe0QXmle8cHtK6h0bo8F4g1gdY1VH6pS7T3RCml7Eev8lFKKRsK6HAXkWQReVtEDrr/TRpgu24RKXJ/rex1/1gR2eh+/vPuE8J+qUtEZorIehEpFpGdInJzr8eeEpHDvWqeOcx6rhCR/SJSIiIP9PN4hPv/X+I+Hnm9HnvQff9+Ebl8OHUMsaZvisge97FZLSJjej3W7/vpp7ruEJETvfb/pV6P3e5+zw+KyO19n+vjun7dq6YDIlLf6zGfHC8ReUJEakRk9wCPi4g85K55p4jM7vWYL4/VYHV9zl3PThFZJyIzej12RER2uY/VFj/XdaGINPR6r37Q67Ezvv9nxRgTsF/Az4EH3LcfAH42wHanB7j/BeAW9+2Hga/6qy5gPFDovp0JVAOJ7u+fAm70Ui0O4BCQD4QDO4DJfba5B3jYffsW4Hn37cnu7SOAse7XcfippiVAtPv2V3tqOtP76ae67gB+189zk4FS979J7ttJ/qqrz/b34xrY4OvjdT4wG9g9wONXAn/HdZ3LAmCjr4+Vh3Ut6tkfsLSnLvf3R4BUi47XhcDrw33/Pf0K6JY7rmkNnnbffhq41tMniogAF+GaDmHIzx9uXcaYA8aYg+7bVUANkOal/ff28fQQxpgOoGd6iIHqfQm42H18lgHPGWPajTGHgRL36/m8JmPMe8aYntU0NuC6fsLXPDlWA7kceNsYU2eMOQW8DVxhUV3LgRVe2veAjDFr6Od6lV6WAc8Ylw1Aoohk4NtjNWhdxph17v2C/z5bnhyvgQznczmgQA/30caYagD3v6MG2C5SRLaIyAYR6QnaFKDeGNPl/r4C1zQJ/qwLABGZh+s38qFed//U/Wfjr8U1q+bZ6m96iL7/z09MDwH0TA/hyXN9VVNvX8TVAuzR3/vpDZ7WdYP7vXlJRHou4PPVsRrSa7u7r8YC7/a621fHazAD1e3LYzVUfT9bBnhLRLaK64p5f1soIjtE5O8iMsV9n0+Ol+XzuYvIO0B6Pw99bwgvk2uMqRKRfOBdEdkFNPazncdDg7xUF+6WzJ+B240xTvfdDwLHcAX+I8B3gB8P5XV776Kf+zydHsKjaSPOgsevKyK3AnOBC3rd/an30xhzqL/n+6CuvwErjDHtInI3rr94LvLwub6sq8ctwEvGmN4rgfvqeA3G35+rIRGRJbjC/dxedy92H6tRwNsiss/d4vaHbcAYY8xpEbkSeBUoxEfHy/KWuzHmEmPM1H6+XgOOu8OxJyRrBniNKve/pcD7wCxcczckims6BOh/2gSf1iUi8cAbwPfdf7b2vHa1+0/ZduBJhtcVMpTpIZBPTg/hyXN9VRMicgmuX5bXuI8FMOD76Q2D1mWMqe1Vy6O41ijw6Lm+rKuXW+jTJePD4zWYger25bHyiIhMBx4Dlhljanvu73WsaoBX8E43pEeMMY3GmNPu26uAMBFJxVfHa7id9r78An7BJ09c/ryfbZKACPftVOAg7pMRwIt88oTqPX6sKxxYDXyjn8cy3P8K8L/Afw+jllBcJ6zG8s+TMVP6bHMvnzyh+oL79hQ+eUK1FO+cUPWkplm4uqkKPX0//VRXRq/b1wEb3LeTgcPu+pLct5P9VZd7uwm4TgiKP46X+zXzGPgE4VV88oTqJl8fKw/rysV1/mhRn/tjgLhet9cBV/ixrvSe9w7XL5Uy97Hz6P0fci3e/I95+wtXv/Bq9wd2dc8HBNef8Y+5by8CdrkPyC7gi72enw9scr/RL/b8EPiprluBTqCo19dM92PvumvdDfwFiB1mPVcCB3CF5ffc9/0YV4sYINL9/y9xH4/8Xs/9nvt5+4GlXnzvBqvpHeB4r2OzcrD30091/T+g2L3/94CJvZ77BfcxLAHu9Gdd7u9/RJ+GgC+PF66/EKrdn+MKXF0cdwN3ux8XXAv5HHLve66fjtVgdT0GnOr12drivj/ffZx2uN/j7/m5rvt6fbY20OuXT3/v/3C/9ApVpZSyIcv73JVSSnmfhrtSStmQhrtSStmQhrtSStmQhrtSStmQhruyLRFJFJF7/LCfC0Vkka/3o9RQaLgrO0vENSOmR9xT2J7Nz8SFuMabKxUwdJy7si0R6Zldbz+uC5Km47piMgzXlBCviWtu+7+7H1+Ia4bPS3DN91OF60K1dmPMfSKShutK51z3Lr4BVOK6IKUbOAHcb4xZ64//n1JnouGubMsd3K8bY6a659SJNsY0uufz2IBr0qYxuC79XmSM2SAimbguS58NNOG6mniHO9yfBf5gjPlQRHKBN40xk0TkR7jmVP+lv/+PSg3E8lkhlfITAf5LRM4HnLimVB3tfuyo+efEbvOAD4wxdQAi8iKuhVfA1aKf7JoKH4B4EYnzR/FKDZWGuxopPodrsZQ5xphOETmCa84dgOZe2/U3/WqPEGChMaa19529wl6pgKEnVJWdNQE9LesEoMYd7Etwdcf0ZxNwgYgkubtybuj12Fu4Jn8CXOvk9rMfpQKChruyLeOax/sj94LFM4G57kWRPwfsG+A5lcB/ARtxzVy5B9fKVQBfc7/GThHZg2vGP3At8HGde9Hj83z2H1JqCPSEqlJ9iEisca2WE4prQYcnjDGvWF2XUkOhLXelPu1HIlKEa779w7iWQ1MqqGjLXSmlbEhb7kopZUMa7kopZUMa7kopZUMa7kopZUMa7kopZUMa7kopZUP/H+IY2+sUvwgsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['target'],rug=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(df.age,df.target).plot(kind=\"bar\",figsize=(25,8),color=['green','cyan' ])\n",
    "plt.title('Heart Disease Frequency for Ages')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(df.sex,df.target).plot(kind=\"bar\",figsize=(10,5),color=['pink','yellow' ])\n",
    "plt.xlabel('Sex (0 = Female, 1 = Male)')\n",
    "plt.xticks(rotation=0)\n",
    "plt.legend([\"Haven't Disease\", \"Have Disease\"])\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Relation of tresbps with target')"
      ]
     },
     "execution_count": 19,
     "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": [
    "\n",
    "\n",
    "plt.figure(figsize=(12,8))\n",
    "sns.boxplot(df['target'], df['trestbps'], palette = 'rainbow')\n",
    "plt.title('Relation of tresbps with target', fontsize = 10)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(df.cp,df.target).plot(kind=\"bar\",figsize=(10,5),color=['tomato','indigo' ])\n",
    "plt.xlabel('Chest Pain Type')\n",
    "plt.xticks(rotation = 0)\n",
    "plt.ylabel('Frequency of Disease or Not')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,8))\n",
    "plt.subplot(2,2,1)\n",
    "plt.scatter(x=df.age[df.target==1],y=df.thalach[df.target==1],c='blue')\n",
    "plt.scatter(x=df.age[df.target==0],y=df.thalach[df.target==0],c='black')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Max Heart Rate')\n",
    "plt.legend(['Disease','No Disease'])\n",
    "\n",
    "plt.subplot(2,2,2)\n",
    "plt.scatter(x=df.age[df.target==1],y=df.chol[df.target==1],c='red')\n",
    "plt.scatter(x=df.age[df.target==0],y=df.chol[df.target==0],c='green')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Cholesterol')\n",
    "plt.legend(['Disease','No Disease'])\n",
    "\n",
    "plt.subplot(2,2,3)\n",
    "plt.scatter(x=df.age[df.target==1],y=df.trestbps[df.target==1],c='cyan')\n",
    "plt.scatter(x=df.age[df.target==0],y=df.trestbps[df.target==0],c='fuchsia')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Resting Blood Pressure')\n",
    "plt.legend(['Disease','No Disease'])\n",
    "\n",
    "plt.subplot(2,2,4)\n",
    "plt.scatter(x=df.age[df.target==1],y=df.oldpeak[df.target==1],c='grey')\n",
    "plt.scatter(x=df.age[df.target==0],y=df.oldpeak[df.target==0],c='navy')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('ST depression')\n",
    "plt.legend(['Disease','No Disease'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "chest_pain=pd.get_dummies(df['cp'],prefix='cp',drop_first=True)\n",
    "df=pd.concat([df,chest_pain],axis=1)\n",
    "df.drop(['cp'],axis=1,inplace=True)\n",
    "sp=pd.get_dummies(df['slope'],prefix='slope')\n",
    "th=pd.get_dummies(df['thal'],prefix='thal')\n",
    "rest_ecg=pd.get_dummies(df['restecg'],prefix='restecg')\n",
    "frames=[df,sp,th,rest_ecg]\n",
    "df=pd.concat(frames,axis=1)\n",
    "df.drop(['slope','thal','restecg'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(['target'], axis = 1)\n",
    "y = df.target.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n",
      "C:\\Users\\akhil\\AppData\\Roaming\\Python\\Python36\\site-packages\\tensorflow\\python\\framework\\dtypes.py:523: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "C:\\Users\\akhil\\AppData\\Roaming\\Python\\Python36\\site-packages\\tensorflow\\python\\framework\\dtypes.py:524: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "C:\\Users\\akhil\\AppData\\Roaming\\Python\\Python36\\site-packages\\tensorflow\\python\\framework\\dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "C:\\Users\\akhil\\AppData\\Roaming\\Python\\Python36\\site-packages\\tensorflow\\python\\framework\\dtypes.py:526: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "C:\\Users\\akhil\\AppData\\Roaming\\Python\\Python36\\site-packages\\tensorflow\\python\\framework\\dtypes.py:527: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "C:\\Users\\akhil\\AppData\\Roaming\\Python\\Python36\\site-packages\\tensorflow\\python\\framework\\dtypes.py:532: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Conv2D, MaxPooling2D\n",
    "from keras.layers import Activation, Dropout, Flatten, Dense\n",
    "import keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "import warnings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\akhil\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:4: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(activation=\"relu\", input_dim=22, units=11, kernel_initializer=\"uniform\")`\n",
      "  after removing the cwd from sys.path.\n",
      "C:\\Users\\akhil\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:7: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(activation=\"relu\", units=11, kernel_initializer=\"uniform\")`\n",
      "  import sys\n",
      "C:\\Users\\akhil\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:10: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(activation=\"sigmoid\", units=1, kernel_initializer=\"uniform\")`\n",
      "  # Remove the CWD from sys.path while we load stuff.\n"
     ]
    }
   ],
   "source": [
    "classifier = Sequential()\n",
    "\n",
    "# Adding the input layer and the first hidden layer\n",
    "classifier.add(Dense(output_dim = 11, init = 'uniform', activation = 'relu', input_dim = 22))\n",
    "\n",
    "# Adding the second hidden layer\n",
    "classifier.add(Dense(output_dim = 11, init = 'uniform', activation = 'relu'))\n",
    "\n",
    "# Adding the output layer\n",
    "classifier.add(Dense(output_dim = 1, init = 'uniform', activation = 'sigmoid'))\n",
    "\n",
    "# Compiling the ANN\n",
    "classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\akhil\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "242/242 [==============================] - 2s 10ms/step - loss: 0.6916 - acc: 0.5372\n",
      "Epoch 2/100\n",
      "242/242 [==============================] - 0s 229us/step - loss: 0.6882 - acc: 0.5909\n",
      "Epoch 3/100\n",
      "242/242 [==============================] - 0s 205us/step - loss: 0.6826 - acc: 0.5868\n",
      "Epoch 4/100\n",
      "242/242 [==============================] - 0s 205us/step - loss: 0.6740 - acc: 0.5868\n",
      "Epoch 5/100\n",
      "242/242 [==============================] - 0s 206us/step - loss: 0.6541 - acc: 0.6157\n",
      "Epoch 6/100\n",
      "242/242 [==============================] - 0s 188us/step - loss: 0.6357 - acc: 0.6777\n",
      "Epoch 7/100\n",
      "242/242 [==============================] - 0s 249us/step - loss: 0.6249 - acc: 0.6488\n",
      "Epoch 8/100\n",
      "242/242 [==============================] - 0s 206us/step - loss: 0.5915 - acc: 0.7149\n",
      "Epoch 9/100\n",
      "242/242 [==============================] - 0s 269us/step - loss: 0.5650 - acc: 0.7273\n",
      "Epoch 10/100\n",
      "242/242 [==============================] - 0s 289us/step - loss: 0.5491 - acc: 0.7231\n",
      "Epoch 11/100\n",
      "242/242 [==============================] - 0s 311us/step - loss: 0.5357 - acc: 0.7231\n",
      "Epoch 12/100\n",
      "242/242 [==============================] - 0s 207us/step - loss: 0.5487 - acc: 0.7231\n",
      "Epoch 13/100\n",
      "242/242 [==============================] - 0s 205us/step - loss: 0.5420 - acc: 0.7149\n",
      "Epoch 14/100\n",
      "242/242 [==============================] - 0s 208us/step - loss: 0.5134 - acc: 0.7521\n",
      "Epoch 15/100\n",
      "242/242 [==============================] - 0s 186us/step - loss: 0.5037 - acc: 0.7562\n",
      "Epoch 16/100\n",
      "242/242 [==============================] - 0s 185us/step - loss: 0.5097 - acc: 0.7645\n",
      "Epoch 17/100\n",
      "242/242 [==============================] - 0s 187us/step - loss: 0.4952 - acc: 0.7562\n",
      "Epoch 18/100\n",
      "242/242 [==============================] - 0s 206us/step - loss: 0.4848 - acc: 0.7603\n",
      "Epoch 19/100\n",
      "242/242 [==============================] - 0s 186us/step - loss: 0.4717 - acc: 0.7686\n",
      "Epoch 20/100\n",
      "242/242 [==============================] - 0s 186us/step - loss: 0.5065 - acc: 0.7355\n",
      "Epoch 21/100\n",
      "242/242 [==============================] - 0s 289us/step - loss: 0.5354 - acc: 0.7355\n",
      "Epoch 22/100\n",
      "242/242 [==============================] - 0s 258us/step - loss: 0.5054 - acc: 0.7562\n",
      "Epoch 23/100\n",
      "242/242 [==============================] - 0s 247us/step - loss: 0.4653 - acc: 0.7893\n",
      "Epoch 24/100\n",
      "242/242 [==============================] - 0s 291us/step - loss: 0.4539 - acc: 0.7727\n",
      "Epoch 25/100\n",
      "242/242 [==============================] - 0s 206us/step - loss: 0.4464 - acc: 0.8099\n",
      "Epoch 26/100\n",
      "242/242 [==============================] - 0s 402us/step - loss: 0.4479 - acc: 0.7769\n",
      "Epoch 27/100\n",
      "242/242 [==============================] - 0s 269us/step - loss: 0.4537 - acc: 0.7769\n",
      "Epoch 28/100\n",
      "242/242 [==============================] - ETA: 0s - loss: 0.4421 - acc: 0.786 - 0s 276us/step - loss: 0.4426 - acc: 0.7934\n",
      "Epoch 29/100\n",
      "242/242 [==============================] - 0s 392us/step - loss: 0.4283 - acc: 0.8017\n",
      "Epoch 30/100\n",
      "242/242 [==============================] - 0s 331us/step - loss: 0.4306 - acc: 0.8140\n",
      "Epoch 31/100\n",
      "242/242 [==============================] - 0s 249us/step - loss: 0.4190 - acc: 0.8099\n",
      "Epoch 32/100\n",
      "242/242 [==============================] - 0s 291us/step - loss: 0.4353 - acc: 0.7975\n",
      "Epoch 33/100\n",
      "242/242 [==============================] - 0s 224us/step - loss: 0.4146 - acc: 0.8347\n",
      "Epoch 34/100\n",
      "242/242 [==============================] - 0s 248us/step - loss: 0.4481 - acc: 0.8058\n",
      "Epoch 35/100\n",
      "242/242 [==============================] - 0s 290us/step - loss: 0.4323 - acc: 0.8058\n",
      "Epoch 36/100\n",
      "242/242 [==============================] - 0s 248us/step - loss: 0.4052 - acc: 0.8264\n",
      "Epoch 37/100\n",
      "242/242 [==============================] - 0s 205us/step - loss: 0.4083 - acc: 0.8099\n",
      "Epoch 38/100\n",
      "242/242 [==============================] - 0s 229us/step - loss: 0.3977 - acc: 0.8223\n",
      "Epoch 39/100\n",
      "242/242 [==============================] - 0s 206us/step - loss: 0.3836 - acc: 0.8471\n",
      "Epoch 40/100\n",
      "242/242 [==============================] - 0s 313us/step - loss: 0.4171 - acc: 0.8140\n",
      "Epoch 41/100\n",
      "242/242 [==============================] - 0s 229us/step - loss: 0.4147 - acc: 0.8264\n",
      "Epoch 42/100\n",
      "242/242 [==============================] - 0s 269us/step - loss: 0.4068 - acc: 0.8306\n",
      "Epoch 43/100\n",
      "242/242 [==============================] - 0s 268us/step - loss: 0.4270 - acc: 0.7934\n",
      "Epoch 44/100\n",
      "242/242 [==============================] - 0s 268us/step - loss: 0.3897 - acc: 0.8223\n",
      "Epoch 45/100\n",
      "242/242 [==============================] - 0s 228us/step - loss: 0.4053 - acc: 0.8099\n",
      "Epoch 46/100\n",
      "242/242 [==============================] - 0s 206us/step - loss: 0.3922 - acc: 0.8347\n",
      "Epoch 47/100\n",
      "242/242 [==============================] - 0s 269us/step - loss: 0.4214 - acc: 0.8264\n",
      "Epoch 48/100\n",
      "242/242 [==============================] - 0s 268us/step - loss: 0.3836 - acc: 0.8471\n",
      "Epoch 49/100\n",
      "242/242 [==============================] - 0s 311us/step - loss: 0.3822 - acc: 0.8554\n",
      "Epoch 50/100\n",
      "242/242 [==============================] - 0s 290us/step - loss: 0.3882 - acc: 0.7934\n",
      "Epoch 51/100\n",
      "242/242 [==============================] - 0s 330us/step - loss: 0.3868 - acc: 0.8099\n",
      "Epoch 52/100\n",
      "242/242 [==============================] - 0s 207us/step - loss: 0.3698 - acc: 0.8388\n",
      "Epoch 53/100\n",
      "242/242 [==============================] - ETA: 0s - loss: 0.2341 - acc: 0.900 - 0s 206us/step - loss: 0.3900 - acc: 0.8388\n",
      "Epoch 54/100\n",
      "242/242 [==============================] - 0s 186us/step - loss: 0.3669 - acc: 0.8678\n",
      "Epoch 55/100\n",
      "242/242 [==============================] - 0s 185us/step - loss: 0.3860 - acc: 0.8099\n",
      "Epoch 56/100\n",
      "242/242 [==============================] - 0s 208us/step - loss: 0.3856 - acc: 0.8347\n",
      "Epoch 57/100\n",
      "242/242 [==============================] - 0s 205us/step - loss: 0.3751 - acc: 0.8223\n",
      "Epoch 58/100\n",
      "242/242 [==============================] - 0s 185us/step - loss: 0.3735 - acc: 0.8512\n",
      "Epoch 59/100\n",
      "242/242 [==============================] - 0s 271us/step - loss: 0.3670 - acc: 0.8471\n",
      "Epoch 60/100\n",
      "242/242 [==============================] - 0s 187us/step - loss: 0.3705 - acc: 0.8388\n",
      "Epoch 61/100\n",
      "242/242 [==============================] - 0s 207us/step - loss: 0.4022 - acc: 0.8306\n",
      "Epoch 62/100\n",
      "242/242 [==============================] - 0s 187us/step - loss: 0.3881 - acc: 0.8140\n",
      "Epoch 63/100\n",
      "242/242 [==============================] - 0s 206us/step - loss: 0.3803 - acc: 0.8430\n",
      "Epoch 64/100\n",
      "242/242 [==============================] - 0s 186us/step - loss: 0.3847 - acc: 0.8554\n",
      "Epoch 65/100\n",
      "242/242 [==============================] - 0s 185us/step - loss: 0.3586 - acc: 0.8512\n",
      "Epoch 66/100\n",
      "242/242 [==============================] - 0s 207us/step - loss: 0.3723 - acc: 0.8471\n",
      "Epoch 67/100\n",
      "242/242 [==============================] - 0s 206us/step - loss: 0.3623 - acc: 0.8388\n",
      "Epoch 68/100\n",
      "242/242 [==============================] - 0s 272us/step - loss: 0.3772 - acc: 0.8388\n",
      "Epoch 69/100\n",
      "242/242 [==============================] - 0s 287us/step - loss: 0.3778 - acc: 0.8347\n",
      "Epoch 70/100\n",
      "242/242 [==============================] - 0s 311us/step - loss: 0.3787 - acc: 0.8430\n",
      "Epoch 71/100\n",
      "242/242 [==============================] - 0s 210us/step - loss: 0.3832 - acc: 0.8347\n",
      "Epoch 72/100\n",
      "242/242 [==============================] - 0s 186us/step - loss: 0.3607 - acc: 0.8430\n",
      "Epoch 73/100\n",
      "242/242 [==============================] - 0s 207us/step - loss: 0.3551 - acc: 0.8471\n",
      "Epoch 74/100\n",
      "242/242 [==============================] - 0s 269us/step - loss: 0.3898 - acc: 0.8140\n",
      "Epoch 75/100\n",
      "242/242 [==============================] - 0s 207us/step - loss: 0.3662 - acc: 0.8388\n",
      "Epoch 76/100\n",
      "242/242 [==============================] - 0s 206us/step - loss: 0.3600 - acc: 0.8678\n",
      "Epoch 77/100\n",
      "242/242 [==============================] - 0s 288us/step - loss: 0.3806 - acc: 0.8512\n",
      "Epoch 78/100\n",
      "242/242 [==============================] - 0s 208us/step - loss: 0.3842 - acc: 0.8264\n",
      "Epoch 79/100\n",
      "242/242 [==============================] - 0s 169us/step - loss: 0.3820 - acc: 0.8347\n",
      "Epoch 80/100\n",
      "242/242 [==============================] - 0s 187us/step - loss: 0.3838 - acc: 0.8347\n",
      "Epoch 81/100\n",
      "242/242 [==============================] - 0s 267us/step - loss: 0.3552 - acc: 0.8554\n",
      "Epoch 82/100\n",
      "242/242 [==============================] - 0s 288us/step - loss: 0.3690 - acc: 0.8264\n",
      "Epoch 83/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "242/242 [==============================] - 0s 289us/step - loss: 0.3583 - acc: 0.8595\n",
      "Epoch 84/100\n",
      "242/242 [==============================] - 0s 287us/step - loss: 0.3799 - acc: 0.8347\n",
      "Epoch 85/100\n",
      "242/242 [==============================] - 0s 250us/step - loss: 0.3728 - acc: 0.8512\n",
      "Epoch 86/100\n",
      "242/242 [==============================] - ETA: 0s - loss: 0.2527 - acc: 0.900 - 0s 205us/step - loss: 0.3947 - acc: 0.8306\n",
      "Epoch 87/100\n",
      "242/242 [==============================] - 0s 269us/step - loss: 0.3900 - acc: 0.8223\n",
      "Epoch 88/100\n",
      "242/242 [==============================] - 0s 268us/step - loss: 0.3531 - acc: 0.8471\n",
      "Epoch 89/100\n",
      "242/242 [==============================] - 0s 309us/step - loss: 0.3635 - acc: 0.8471\n",
      "Epoch 90/100\n",
      "242/242 [==============================] - 0s 269us/step - loss: 0.3634 - acc: 0.8388\n",
      "Epoch 91/100\n",
      "242/242 [==============================] - 0s 226us/step - loss: 0.3553 - acc: 0.8430\n",
      "Epoch 92/100\n",
      "242/242 [==============================] - 0s 229us/step - loss: 0.3595 - acc: 0.8388\n",
      "Epoch 93/100\n",
      "242/242 [==============================] - 0s 207us/step - loss: 0.3646 - acc: 0.8471\n",
      "Epoch 94/100\n",
      "242/242 [==============================] - 0s 226us/step - loss: 0.3545 - acc: 0.8430\n",
      "Epoch 95/100\n",
      "242/242 [==============================] - 0s 166us/step - loss: 0.3735 - acc: 0.8430\n",
      "Epoch 96/100\n",
      "242/242 [==============================] - 0s 206us/step - loss: 0.3596 - acc: 0.8471\n",
      "Epoch 97/100\n",
      "242/242 [==============================] - 0s 228us/step - loss: 0.3839 - acc: 0.8512\n",
      "Epoch 98/100\n",
      "242/242 [==============================] - 0s 185us/step - loss: 0.3695 - acc: 0.8430\n",
      "Epoch 99/100\n",
      "242/242 [==============================] - 0s 261us/step - loss: 0.3564 - acc: 0.8512\n",
      "Epoch 100/100\n",
      "242/242 [==============================] - 0s 205us/step - loss: 0.3520 - acc: 0.8636\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x28a11602fd0>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.fit(X_train, y_train, batch_size = 10, nb_epoch = 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy of the model:  0.8688524590163934\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = classifier.predict(X_test)\n",
    "\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(y_test, y_pred.round())\n",
    "sns.heatmap(cm,annot=True,cmap=\"Oranges\",fmt=\"d\",cbar=False)\n",
    "#accuracy score\n",
    "from sklearn.metrics import accuracy_score\n",
    "ac=accuracy_score(y_test, y_pred.round())\n",
    "print('accuracy of the model: ',ac)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}