Heart-Dieseas-Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import sklearn\n",
    "import matplotlib\n",
    "import keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "keras:2.3.0\n"
     ]
    }
   ],
   "source": [
    "print('keras:{}'.format(keras.__version__))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python:3.6.5 |Anaconda, Inc.| (default, Apr 26 2018, 08:42:37) \n",
      "[GCC 4.2.1 Compatible Clang 4.0.1 (tags/RELEASE_401/final)]\n",
      "Pandas:0.23.0\n",
      "Numpy:1.17.1\n",
      "Sklearn:0.19.1\n",
      "Matplotlib:2.2.2\n"
     ]
    }
   ],
   "source": [
    "print('Python:{}'.format(sys.version))\n",
    "print('Pandas:{}'.format(pd.__version__))\n",
    "print('Numpy:{}'.format(np.__version__))\n",
    "print('Sklearn:{}'.format(sklearn.__version__))\n",
    "print('Matplotlib:{}'.format(matplotlib.__version__))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from pandas.plotting import scatter_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import the dataset\n",
    "url=\"http://archive.ics.uci.edu/ml/machine-learning-databases/heart-disease/processed.cleveland.data\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#nomenclature of attributes for our pandas DataFrame\n",
    "names = ['age',\n",
    "        'sex',\n",
    "        'cp',\n",
    "        'trestbps',\n",
    "        'chol',\n",
    "        'fbs',\n",
    "        'restecg',\n",
    "        'thalach',\n",
    "        'exang',\n",
    "        'oldpeak',\n",
    "        'slope',\n",
    "        'ca',\n",
    "        'thal',\n",
    "        'class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#read the csv file\n",
    "cleveland =pd.read_csv(url, names=names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of DataFrame: (303, 14)\n",
      "age          67\n",
      "sex           1\n",
      "cp            4\n",
      "trestbps    160\n",
      "chol        286\n",
      "fbs           0\n",
      "restecg       2\n",
      "thalach     108\n",
      "exang         1\n",
      "oldpeak     1.5\n",
      "slope         2\n",
      "ca          3.0\n",
      "thal        3.0\n",
      "class         2\n",
      "Name: 1, dtype: object\n"
     ]
    }
   ],
   "source": [
    "#print the shape of DataFrame, to know the number of entities\n",
    "print('Shape of DataFrame: {}'.format(cleveland.shape))\n",
    "print (cleveland.loc[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <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",
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       "      <td>57.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>335.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>143.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <th>281</th>\n",
       "      <td>47.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>253.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>179.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>282</th>\n",
       "      <td>55.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>205.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3</td>\n",
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       "      <th>283</th>\n",
       "      <td>35.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>192.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>174.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>61.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>203.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>58.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>318.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>58.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.8</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>58.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>?</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>56.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>221.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>56.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>67.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>212.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>55.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>342.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>44.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>63.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>63.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>197.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>41.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>182.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>59.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>57.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>241.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>45.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>264.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>68.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>193.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>57.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>57.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>236.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>38.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>173.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>?</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      age  sex   cp  trestbps   chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "280  57.0  1.0  4.0     110.0  335.0  0.0      0.0    143.0    1.0      3.0   \n",
       "281  47.0  1.0  3.0     130.0  253.0  0.0      0.0    179.0    0.0      0.0   \n",
       "282  55.0  0.0  4.0     128.0  205.0  0.0      1.0    130.0    1.0      2.0   \n",
       "283  35.0  1.0  2.0     122.0  192.0  0.0      0.0    174.0    0.0      0.0   \n",
       "284  61.0  1.0  4.0     148.0  203.0  0.0      0.0    161.0    0.0      0.0   \n",
       "285  58.0  1.0  4.0     114.0  318.0  0.0      1.0    140.0    0.0      4.4   \n",
       "286  58.0  0.0  4.0     170.0  225.0  1.0      2.0    146.0    1.0      2.8   \n",
       "287  58.0  1.0  2.0     125.0  220.0  0.0      0.0    144.0    0.0      0.4   \n",
       "288  56.0  1.0  2.0     130.0  221.0  0.0      2.0    163.0    0.0      0.0   \n",
       "289  56.0  1.0  2.0     120.0  240.0  0.0      0.0    169.0    0.0      0.0   \n",
       "290  67.0  1.0  3.0     152.0  212.0  0.0      2.0    150.0    0.0      0.8   \n",
       "291  55.0  0.0  2.0     132.0  342.0  0.0      0.0    166.0    0.0      1.2   \n",
       "292  44.0  1.0  4.0     120.0  169.0  0.0      0.0    144.0    1.0      2.8   \n",
       "293  63.0  1.0  4.0     140.0  187.0  0.0      2.0    144.0    1.0      4.0   \n",
       "294  63.0  0.0  4.0     124.0  197.0  0.0      0.0    136.0    1.0      0.0   \n",
       "295  41.0  1.0  2.0     120.0  157.0  0.0      0.0    182.0    0.0      0.0   \n",
       "296  59.0  1.0  4.0     164.0  176.0  1.0      2.0     90.0    0.0      1.0   \n",
       "297  57.0  0.0  4.0     140.0  241.0  0.0      0.0    123.0    1.0      0.2   \n",
       "298  45.0  1.0  1.0     110.0  264.0  0.0      0.0    132.0    0.0      1.2   \n",
       "299  68.0  1.0  4.0     144.0  193.0  1.0      0.0    141.0    0.0      3.4   \n",
       "300  57.0  1.0  4.0     130.0  131.0  0.0      0.0    115.0    1.0      1.2   \n",
       "301  57.0  0.0  2.0     130.0  236.0  0.0      2.0    174.0    0.0      0.0   \n",
       "302  38.0  1.0  3.0     138.0  175.0  0.0      0.0    173.0    0.0      0.0   \n",
       "\n",
       "     slope   ca thal  class  \n",
       "280    2.0  1.0  7.0      2  \n",
       "281    1.0  0.0  3.0      0  \n",
       "282    2.0  1.0  7.0      3  \n",
       "283    1.0  0.0  3.0      0  \n",
       "284    1.0  1.0  7.0      2  \n",
       "285    3.0  3.0  6.0      4  \n",
       "286    2.0  2.0  6.0      2  \n",
       "287    2.0    ?  7.0      0  \n",
       "288    1.0  0.0  7.0      0  \n",
       "289    3.0  0.0  3.0      0  \n",
       "290    2.0  0.0  7.0      1  \n",
       "291    1.0  0.0  3.0      0  \n",
       "292    3.0  0.0  6.0      2  \n",
       "293    1.0  2.0  7.0      2  \n",
       "294    2.0  0.0  3.0      1  \n",
       "295    1.0  0.0  3.0      0  \n",
       "296    2.0  2.0  6.0      3  \n",
       "297    2.0  0.0  7.0      1  \n",
       "298    2.0  0.0  7.0      1  \n",
       "299    2.0  2.0  7.0      2  \n",
       "300    2.0  1.0  7.0      3  \n",
       "301    2.0  1.0  3.0      1  \n",
       "302    1.0    ?  3.0      0  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#print last few data points\n",
    "cleveland.loc[280:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "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>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>57.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>335.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281</th>\n",
       "      <td>47.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>253.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>179.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>282</th>\n",
       "      <td>55.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>205.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283</th>\n",
       "      <td>35.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>192.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>61.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>203.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>58.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>318.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>58.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.8</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>58.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>56.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>221.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>56.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>67.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>212.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>55.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>342.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>44.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>63.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>63.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>197.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>41.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>182.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>59.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>57.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>241.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>45.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>264.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>68.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>193.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>57.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>57.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>236.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>38.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>173.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      age  sex   cp  trestbps   chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "280  57.0  1.0  4.0     110.0  335.0  0.0      0.0    143.0    1.0      3.0   \n",
       "281  47.0  1.0  3.0     130.0  253.0  0.0      0.0    179.0    0.0      0.0   \n",
       "282  55.0  0.0  4.0     128.0  205.0  0.0      1.0    130.0    1.0      2.0   \n",
       "283  35.0  1.0  2.0     122.0  192.0  0.0      0.0    174.0    0.0      0.0   \n",
       "284  61.0  1.0  4.0     148.0  203.0  0.0      0.0    161.0    0.0      0.0   \n",
       "285  58.0  1.0  4.0     114.0  318.0  0.0      1.0    140.0    0.0      4.4   \n",
       "286  58.0  0.0  4.0     170.0  225.0  1.0      2.0    146.0    1.0      2.8   \n",
       "287  58.0  1.0  2.0     125.0  220.0  0.0      0.0    144.0    0.0      0.4   \n",
       "288  56.0  1.0  2.0     130.0  221.0  0.0      2.0    163.0    0.0      0.0   \n",
       "289  56.0  1.0  2.0     120.0  240.0  0.0      0.0    169.0    0.0      0.0   \n",
       "290  67.0  1.0  3.0     152.0  212.0  0.0      2.0    150.0    0.0      0.8   \n",
       "291  55.0  0.0  2.0     132.0  342.0  0.0      0.0    166.0    0.0      1.2   \n",
       "292  44.0  1.0  4.0     120.0  169.0  0.0      0.0    144.0    1.0      2.8   \n",
       "293  63.0  1.0  4.0     140.0  187.0  0.0      2.0    144.0    1.0      4.0   \n",
       "294  63.0  0.0  4.0     124.0  197.0  0.0      0.0    136.0    1.0      0.0   \n",
       "295  41.0  1.0  2.0     120.0  157.0  0.0      0.0    182.0    0.0      0.0   \n",
       "296  59.0  1.0  4.0     164.0  176.0  1.0      2.0     90.0    0.0      1.0   \n",
       "297  57.0  0.0  4.0     140.0  241.0  0.0      0.0    123.0    1.0      0.2   \n",
       "298  45.0  1.0  1.0     110.0  264.0  0.0      0.0    132.0    0.0      1.2   \n",
       "299  68.0  1.0  4.0     144.0  193.0  1.0      0.0    141.0    0.0      3.4   \n",
       "300  57.0  1.0  4.0     130.0  131.0  0.0      0.0    115.0    1.0      1.2   \n",
       "301  57.0  0.0  2.0     130.0  236.0  0.0      2.0    174.0    0.0      0.0   \n",
       "302  38.0  1.0  3.0     138.0  175.0  0.0      0.0    173.0    0.0      0.0   \n",
       "\n",
       "     slope   ca thal  class  \n",
       "280    2.0  1.0  7.0      2  \n",
       "281    1.0  0.0  3.0      0  \n",
       "282    2.0  1.0  7.0      3  \n",
       "283    1.0  0.0  3.0      0  \n",
       "284    1.0  1.0  7.0      2  \n",
       "285    3.0  3.0  6.0      4  \n",
       "286    2.0  2.0  6.0      2  \n",
       "287    2.0  NaN  7.0      0  \n",
       "288    1.0  0.0  7.0      0  \n",
       "289    3.0  0.0  3.0      0  \n",
       "290    2.0  0.0  7.0      1  \n",
       "291    1.0  0.0  3.0      0  \n",
       "292    3.0  0.0  6.0      2  \n",
       "293    1.0  2.0  7.0      2  \n",
       "294    2.0  0.0  3.0      1  \n",
       "295    1.0  0.0  3.0      0  \n",
       "296    2.0  2.0  6.0      3  \n",
       "297    2.0  0.0  7.0      1  \n",
       "298    2.0  0.0  7.0      1  \n",
       "299    2.0  2.0  7.0      2  \n",
       "300    2.0  1.0  7.0      3  \n",
       "301    2.0  1.0  3.0      1  \n",
       "302    1.0  NaN  3.0      0  "
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#datacleaning,removing ? fields\n",
    "data= cleveland[~cleveland.isin(['?'])]\n",
    "data.loc[280:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "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>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>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>57.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>335.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281</th>\n",
       "      <td>47.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>253.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>179.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>282</th>\n",
       "      <td>55.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>205.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283</th>\n",
       "      <td>35.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>192.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>61.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>203.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>58.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>318.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>58.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.8</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>56.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>221.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>56.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>67.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>212.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>55.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>342.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>44.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>63.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>63.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>197.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>41.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>182.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>59.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>57.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>241.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>45.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>264.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>68.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>193.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>57.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>57.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>236.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</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  \\\n",
       "280  57.0  1.0  4.0     110.0  335.0  0.0      0.0    143.0    1.0      3.0   \n",
       "281  47.0  1.0  3.0     130.0  253.0  0.0      0.0    179.0    0.0      0.0   \n",
       "282  55.0  0.0  4.0     128.0  205.0  0.0      1.0    130.0    1.0      2.0   \n",
       "283  35.0  1.0  2.0     122.0  192.0  0.0      0.0    174.0    0.0      0.0   \n",
       "284  61.0  1.0  4.0     148.0  203.0  0.0      0.0    161.0    0.0      0.0   \n",
       "285  58.0  1.0  4.0     114.0  318.0  0.0      1.0    140.0    0.0      4.4   \n",
       "286  58.0  0.0  4.0     170.0  225.0  1.0      2.0    146.0    1.0      2.8   \n",
       "288  56.0  1.0  2.0     130.0  221.0  0.0      2.0    163.0    0.0      0.0   \n",
       "289  56.0  1.0  2.0     120.0  240.0  0.0      0.0    169.0    0.0      0.0   \n",
       "290  67.0  1.0  3.0     152.0  212.0  0.0      2.0    150.0    0.0      0.8   \n",
       "291  55.0  0.0  2.0     132.0  342.0  0.0      0.0    166.0    0.0      1.2   \n",
       "292  44.0  1.0  4.0     120.0  169.0  0.0      0.0    144.0    1.0      2.8   \n",
       "293  63.0  1.0  4.0     140.0  187.0  0.0      2.0    144.0    1.0      4.0   \n",
       "294  63.0  0.0  4.0     124.0  197.0  0.0      0.0    136.0    1.0      0.0   \n",
       "295  41.0  1.0  2.0     120.0  157.0  0.0      0.0    182.0    0.0      0.0   \n",
       "296  59.0  1.0  4.0     164.0  176.0  1.0      2.0     90.0    0.0      1.0   \n",
       "297  57.0  0.0  4.0     140.0  241.0  0.0      0.0    123.0    1.0      0.2   \n",
       "298  45.0  1.0  1.0     110.0  264.0  0.0      0.0    132.0    0.0      1.2   \n",
       "299  68.0  1.0  4.0     144.0  193.0  1.0      0.0    141.0    0.0      3.4   \n",
       "300  57.0  1.0  4.0     130.0  131.0  0.0      0.0    115.0    1.0      1.2   \n",
       "301  57.0  0.0  2.0     130.0  236.0  0.0      2.0    174.0    0.0      0.0   \n",
       "\n",
       "     slope   ca thal  class  \n",
       "280    2.0  1.0  7.0      2  \n",
       "281    1.0  0.0  3.0      0  \n",
       "282    2.0  1.0  7.0      3  \n",
       "283    1.0  0.0  3.0      0  \n",
       "284    1.0  1.0  7.0      2  \n",
       "285    3.0  3.0  6.0      4  \n",
       "286    2.0  2.0  6.0      2  \n",
       "288    1.0  0.0  7.0      0  \n",
       "289    3.0  0.0  3.0      0  \n",
       "290    2.0  0.0  7.0      1  \n",
       "291    1.0  0.0  3.0      0  \n",
       "292    3.0  0.0  6.0      2  \n",
       "293    1.0  2.0  7.0      2  \n",
       "294    2.0  0.0  3.0      1  \n",
       "295    1.0  0.0  3.0      0  \n",
       "296    2.0  2.0  6.0      3  \n",
       "297    2.0  0.0  7.0      1  \n",
       "298    2.0  0.0  7.0      1  \n",
       "299    2.0  2.0  7.0      2  \n",
       "300    2.0  1.0  7.0      3  \n",
       "301    2.0  1.0  3.0      1  "
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dropping rows with NaN values\n",
    "data= data.dropna(axis=0)\n",
    "data.loc[280:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(297, 14)\n",
      "age         float64\n",
      "sex         float64\n",
      "cp          float64\n",
      "trestbps    float64\n",
      "chol        float64\n",
      "fbs         float64\n",
      "restecg     float64\n",
      "thalach     float64\n",
      "exang       float64\n",
      "oldpeak     float64\n",
      "slope       float64\n",
      "ca           object\n",
      "thal         object\n",
      "class         int64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "#print the shape and datatype of DataFrame\n",
    "print (data.shape)\n",
    "print (data.dtypes)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age         float64\n",
       "sex         float64\n",
       "cp          float64\n",
       "trestbps    float64\n",
       "chol        float64\n",
       "fbs         float64\n",
       "restecg     float64\n",
       "thalach     float64\n",
       "exang       float64\n",
       "oldpeak     float64\n",
       "slope       float64\n",
       "ca          float64\n",
       "thal        float64\n",
       "class         int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#transform data to numeric to enable further analysis\n",
    "data=data.apply(pd.to_numeric)\n",
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "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>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>297.000000</td>\n",
       "      <td>297.000000</td>\n",
       "      <td>297.000000</td>\n",
       "      <td>297.000000</td>\n",
       "      <td>297.000000</td>\n",
       "      <td>297.000000</td>\n",
       "      <td>297.000000</td>\n",
       "      <td>297.000000</td>\n",
       "      <td>297.000000</td>\n",
       "      <td>297.000000</td>\n",
       "      <td>297.000000</td>\n",
       "      <td>297.000000</td>\n",
       "      <td>297.000000</td>\n",
       "      <td>297.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>54.542088</td>\n",
       "      <td>0.676768</td>\n",
       "      <td>3.158249</td>\n",
       "      <td>131.693603</td>\n",
       "      <td>247.350168</td>\n",
       "      <td>0.144781</td>\n",
       "      <td>0.996633</td>\n",
       "      <td>149.599327</td>\n",
       "      <td>0.326599</td>\n",
       "      <td>1.055556</td>\n",
       "      <td>1.602694</td>\n",
       "      <td>0.676768</td>\n",
       "      <td>4.730640</td>\n",
       "      <td>0.946128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.049736</td>\n",
       "      <td>0.468500</td>\n",
       "      <td>0.964859</td>\n",
       "      <td>17.762806</td>\n",
       "      <td>51.997583</td>\n",
       "      <td>0.352474</td>\n",
       "      <td>0.994914</td>\n",
       "      <td>22.941562</td>\n",
       "      <td>0.469761</td>\n",
       "      <td>1.166123</td>\n",
       "      <td>0.618187</td>\n",
       "      <td>0.938965</td>\n",
       "      <td>1.938629</td>\n",
       "      <td>1.234551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>48.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>211.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>133.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>56.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>243.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>153.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>61.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>276.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>77.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>564.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              age         sex          cp    trestbps        chol         fbs  \\\n",
       "count  297.000000  297.000000  297.000000  297.000000  297.000000  297.000000   \n",
       "mean    54.542088    0.676768    3.158249  131.693603  247.350168    0.144781   \n",
       "std      9.049736    0.468500    0.964859   17.762806   51.997583    0.352474   \n",
       "min     29.000000    0.000000    1.000000   94.000000  126.000000    0.000000   \n",
       "25%     48.000000    0.000000    3.000000  120.000000  211.000000    0.000000   \n",
       "50%     56.000000    1.000000    3.000000  130.000000  243.000000    0.000000   \n",
       "75%     61.000000    1.000000    4.000000  140.000000  276.000000    0.000000   \n",
       "max     77.000000    1.000000    4.000000  200.000000  564.000000    1.000000   \n",
       "\n",
       "          restecg     thalach       exang     oldpeak       slope          ca  \\\n",
       "count  297.000000  297.000000  297.000000  297.000000  297.000000  297.000000   \n",
       "mean     0.996633  149.599327    0.326599    1.055556    1.602694    0.676768   \n",
       "std      0.994914   22.941562    0.469761    1.166123    0.618187    0.938965   \n",
       "min      0.000000   71.000000    0.000000    0.000000    1.000000    0.000000   \n",
       "25%      0.000000  133.000000    0.000000    0.000000    1.000000    0.000000   \n",
       "50%      1.000000  153.000000    0.000000    0.800000    2.000000    0.000000   \n",
       "75%      2.000000  166.000000    1.000000    1.600000    2.000000    1.000000   \n",
       "max      2.000000  202.000000    1.000000    6.200000    3.000000    3.000000   \n",
       "\n",
       "             thal       class  \n",
       "count  297.000000  297.000000  \n",
       "mean     4.730640    0.946128  \n",
       "std      1.938629    1.234551  \n",
       "min      3.000000    0.000000  \n",
       "25%      3.000000    0.000000  \n",
       "50%      3.000000    0.000000  \n",
       "75%      7.000000    2.000000  \n",
       "max      7.000000    4.000000  "
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#print characteristics\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot histograms for each variable\n",
    "data.hist(figsize = (12,12))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Createing training(80% of total) and testing(20% of total) datasets\n",
    "from sklearn import model_selection\n",
    "\n",
    "X=np.array(data.drop(['class'], 1))\n",
    "y=np.array(data['class'])\n",
    "\n",
    "X_train, X_test, y_train, y_test = model_selection.train_test_split(X,y,test_size = .2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(237, 5)\n",
      "[[0. 0. 1. 0. 0.]\n",
      " [1. 0. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0.]\n",
      " [1. 0. 0. 0. 0.]\n",
      " [1. 0. 0. 0. 0.]\n",
      " [1. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 1.]\n",
      " [1. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 1. 0.]]\n"
     ]
    }
   ],
   "source": [
    "# convert the data to categorical labels\n",
    "from keras.utils.np_utils import to_categorical\n",
    "\n",
    "Y_train = to_categorical(y_train, num_classes=None)\n",
    "Y_test = to_categorical(y_test, num_classes=None)\n",
    "\n",
    "#Returns binary representation of the presence of heart diseas(1,2,3,4) and no presence (0) \n",
    "print (Y_train.shape)\n",
    "print (Y_train[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "#building the neural network\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Activation\n",
    "from keras.optimizers import Adam\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_29\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_121 (Dense)            (None, 8)                 112       \n",
      "_________________________________________________________________\n",
      "dense_122 (Dense)            (None, 4)                 36        \n",
      "_________________________________________________________________\n",
      "dense_123 (Dense)            (None, 5)                 25        \n",
      "=================================================================\n",
      "Total params: 173\n",
      "Trainable params: 173\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# define a function to build the keras model\n",
    "def create_model():\n",
    "    # create model\n",
    "    model = Sequential()\n",
    "    model.add(Dense(8, input_dim=13, kernel_initializer='normal', activation='relu'))\n",
    "    model.add(Dense(4, kernel_initializer='normal', activation='relu'))\n",
    "    model.add(Dense(5, activation='softmax'))\n",
    "    \n",
    "    # compile model\n",
    "    adam = Adam(lr=0.001)\n",
    "    model.compile(loss='categorical_crossentropy', optimizer=adam, metrics=['accuracy'])\n",
    "    return model\n",
    "\n",
    "\n",
    "model = create_model()\n",
    "print(model.summary())\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "237/237 [==============================] - 0s 284us/step - loss: 0.9235 - accuracy: 0.6076\n",
      "Epoch 2/100\n",
      "237/237 [==============================] - 0s 172us/step - loss: 0.8918 - accuracy: 0.6203\n",
      "Epoch 3/100\n",
      "237/237 [==============================] - 0s 173us/step - loss: 0.8901 - accuracy: 0.6076\n",
      "Epoch 4/100\n",
      "237/237 [==============================] - 0s 163us/step - loss: 0.8968 - accuracy: 0.6118\n",
      "Epoch 5/100\n",
      "237/237 [==============================] - 0s 170us/step - loss: 0.9020 - accuracy: 0.6203\n",
      "Epoch 6/100\n",
      "237/237 [==============================] - 0s 165us/step - loss: 0.8933 - accuracy: 0.6329\n",
      "Epoch 7/100\n",
      "237/237 [==============================] - 0s 176us/step - loss: 0.8919 - accuracy: 0.6329\n",
      "Epoch 8/100\n",
      "237/237 [==============================] - 0s 182us/step - loss: 0.8973 - accuracy: 0.6245\n",
      "Epoch 9/100\n",
      "237/237 [==============================] - 0s 166us/step - loss: 0.8870 - accuracy: 0.6160\n",
      "Epoch 10/100\n",
      "237/237 [==============================] - 0s 288us/step - loss: 0.8943 - accuracy: 0.6118\n",
      "Epoch 11/100\n",
      "237/237 [==============================] - 0s 207us/step - loss: 0.9186 - accuracy: 0.6034\n",
      "Epoch 12/100\n",
      "237/237 [==============================] - 0s 176us/step - loss: 0.9018 - accuracy: 0.6034\n",
      "Epoch 13/100\n",
      "237/237 [==============================] - 0s 185us/step - loss: 0.9057 - accuracy: 0.6118\n",
      "Epoch 14/100\n",
      "237/237 [==============================] - 0s 166us/step - loss: 0.8953 - accuracy: 0.6118\n",
      "Epoch 15/100\n",
      "237/237 [==============================] - 0s 175us/step - loss: 0.8961 - accuracy: 0.6076\n",
      "Epoch 16/100\n",
      "237/237 [==============================] - 0s 175us/step - loss: 0.8879 - accuracy: 0.6118\n",
      "Epoch 17/100\n",
      "237/237 [==============================] - 0s 179us/step - loss: 0.8972 - accuracy: 0.6203\n",
      "Epoch 18/100\n",
      "237/237 [==============================] - 0s 174us/step - loss: 0.8862 - accuracy: 0.6203\n",
      "Epoch 19/100\n",
      "237/237 [==============================] - 0s 173us/step - loss: 0.8903 - accuracy: 0.6245\n",
      "Epoch 20/100\n",
      "237/237 [==============================] - 0s 206us/step - loss: 0.8866 - accuracy: 0.6118\n",
      "Epoch 21/100\n",
      "237/237 [==============================] - 0s 174us/step - loss: 0.8949 - accuracy: 0.6203\n",
      "Epoch 22/100\n",
      "237/237 [==============================] - 0s 176us/step - loss: 0.8855 - accuracy: 0.6118\n",
      "Epoch 23/100\n",
      "237/237 [==============================] - 0s 172us/step - loss: 0.9073 - accuracy: 0.6160\n",
      "Epoch 24/100\n",
      "237/237 [==============================] - 0s 178us/step - loss: 0.8974 - accuracy: 0.6160\n",
      "Epoch 25/100\n",
      "237/237 [==============================] - 0s 173us/step - loss: 0.8844 - accuracy: 0.6118\n",
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      "Epoch 78/100\n",
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      "Epoch 79/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "237/237 [==============================] - 0s 169us/step - loss: 0.8789 - accuracy: 0.6287\n",
      "Epoch 80/100\n",
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      "Epoch 81/100\n",
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      "Epoch 82/100\n",
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      "Epoch 83/100\n",
      "237/237 [==============================] - 0s 157us/step - loss: 0.8799 - accuracy: 0.6160\n",
      "Epoch 84/100\n",
      "237/237 [==============================] - 0s 235us/step - loss: 0.9049 - accuracy: 0.6034\n",
      "Epoch 85/100\n",
      "237/237 [==============================] - 0s 150us/step - loss: 0.8793 - accuracy: 0.6203\n",
      "Epoch 86/100\n",
      "237/237 [==============================] - 0s 152us/step - loss: 0.8799 - accuracy: 0.6160\n",
      "Epoch 87/100\n",
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      "Epoch 88/100\n",
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      "Epoch 89/100\n",
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      "Epoch 90/100\n",
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      "Epoch 91/100\n",
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      "Epoch 92/100\n",
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      "Epoch 93/100\n",
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      "Epoch 94/100\n",
      "237/237 [==============================] - 0s 148us/step - loss: 0.8782 - accuracy: 0.6203\n",
      "Epoch 95/100\n",
      "237/237 [==============================] - 0s 147us/step - loss: 0.8773 - accuracy: 0.6160\n",
      "Epoch 96/100\n",
      "237/237 [==============================] - 0s 147us/step - loss: 0.8838 - accuracy: 0.6076\n",
      "Epoch 97/100\n",
      "237/237 [==============================] - 0s 148us/step - loss: 0.9114 - accuracy: 0.6203\n",
      "Epoch 98/100\n",
      "237/237 [==============================] - 0s 148us/step - loss: 0.8866 - accuracy: 0.6118\n",
      "Epoch 99/100\n",
      "237/237 [==============================] - 0s 145us/step - loss: 0.9255 - accuracy: 0.6160\n",
      "Epoch 100/100\n",
      "237/237 [==============================] - 0s 151us/step - loss: 0.8826 - accuracy: 0.6203\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.callbacks.History at 0x1a46fdaa20>"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "# fit the model to the training data\n",
    "model.fit(X_train, Y_train, epochs=100, batch_size=10, verbose = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results for Categorical Model\n",
      "0.55\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.68      1.00      0.81        30\n",
      "          1       0.33      0.08      0.13        12\n",
      "          2       0.15      0.29      0.20         7\n",
      "          3       0.00      0.00      0.00         8\n",
      "          4       0.00      0.00      0.00         3\n",
      "\n",
      "avg / total       0.43      0.55      0.46        60\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report, accuracy_score\n",
    "\n",
    "categorical_pred = np.argmax(model.predict(X_test), axis=1)\n",
    "\n",
    "print('Results for Categorical Model')\n",
    "print(accuracy_score(y_test, categorical_pred))\n",
    "print(classification_report(y_test, categorical_pred))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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