HeartDiseaseDiagnosis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.naive_bayes import MultinomialNB, GaussianNB, BernoulliNB\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn import svm\n",
    "from sklearn import tree\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from xgboost import XGBClassifier\n",
    "# For neural networks!\n",
    "from keras.models import Sequential, load_model\n",
    "from keras.layers import Dense\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from keras.utils import np_utils\n",
    "from keras.wrappers.scikit_learn import KerasClassifier\n",
    "from sklearn.model_selection import KFold, StratifiedKFold\n",
    "from sklearn.model_selection import cross_val_score\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>slop</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>pred_attribute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0</td>\n",
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       "      <th>1</th>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>160</td>\n",
       "      <td>286</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>108</td>\n",
       "      <td>1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>120</td>\n",
       "      <td>229</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>129</td>\n",
       "      <td>1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>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  slop  \\\n",
       "0   63    1   1       145   233    1        2      150      0      2.3     3   \n",
       "1   67    1   4       160   286    0        2      108      1      1.5     2   \n",
       "2   67    1   4       120   229    0        2      129      1      2.6     2   \n",
       "3   37    1   3       130   250    0        0      187      0      3.5     3   \n",
       "4   41    0   2       130   204    0        2      172      0      1.4     1   \n",
       "\n",
       "    ca  thal  pred_attribute  \n",
       "0  0.0   6.0               0  \n",
       "1  3.0   3.0               2  \n",
       "2  2.0   7.0               1  \n",
       "3  0.0   3.0               0  \n",
       "4  0.0   3.0               0  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('Heart_Disease_Data.csv', na_values='?')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</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>slop</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>pred_attribute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>299.000000</td>\n",
       "      <td>301.000000</td>\n",
       "      <td>303.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>54.438944</td>\n",
       "      <td>0.679868</td>\n",
       "      <td>3.158416</td>\n",
       "      <td>131.689769</td>\n",
       "      <td>246.693069</td>\n",
       "      <td>0.148515</td>\n",
       "      <td>0.990099</td>\n",
       "      <td>149.607261</td>\n",
       "      <td>0.326733</td>\n",
       "      <td>1.039604</td>\n",
       "      <td>1.600660</td>\n",
       "      <td>0.672241</td>\n",
       "      <td>4.734219</td>\n",
       "      <td>0.937294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.038662</td>\n",
       "      <td>0.467299</td>\n",
       "      <td>0.960126</td>\n",
       "      <td>17.599748</td>\n",
       "      <td>51.776918</td>\n",
       "      <td>0.356198</td>\n",
       "      <td>0.994971</td>\n",
       "      <td>22.875003</td>\n",
       "      <td>0.469794</td>\n",
       "      <td>1.161075</td>\n",
       "      <td>0.616226</td>\n",
       "      <td>0.937438</td>\n",
       "      <td>1.939706</td>\n",
       "      <td>1.228536</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>133.500000</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>241.000000</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>275.000000</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  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
       "mean    54.438944    0.679868    3.158416  131.689769  246.693069    0.148515   \n",
       "std      9.038662    0.467299    0.960126   17.599748   51.776918    0.356198   \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  241.000000    0.000000   \n",
       "75%     61.000000    1.000000    4.000000  140.000000  275.000000    0.000000   \n",
       "max     77.000000    1.000000    4.000000  200.000000  564.000000    1.000000   \n",
       "\n",
       "          restecg     thalach       exang     oldpeak        slop          ca  \\\n",
       "count  303.000000  303.000000  303.000000  303.000000  303.000000  299.000000   \n",
       "mean     0.990099  149.607261    0.326733    1.039604    1.600660    0.672241   \n",
       "std      0.994971   22.875003    0.469794    1.161075    0.616226    0.937438   \n",
       "min      0.000000   71.000000    0.000000    0.000000    1.000000    0.000000   \n",
       "25%      0.000000  133.500000    0.000000    0.000000    1.000000    0.000000   \n",
       "50%      1.000000  153.000000    0.000000    0.800000    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  pred_attribute  \n",
       "count  301.000000      303.000000  \n",
       "mean     4.734219        0.937294  \n",
       "std      1.939706        1.228536  \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": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age               0\n",
       "sex               0\n",
       "cp                0\n",
       "trestbps          0\n",
       "chol              0\n",
       "fbs               0\n",
       "restecg           0\n",
       "thalach           0\n",
       "exang             0\n",
       "oldpeak           0\n",
       "slop              0\n",
       "ca                4\n",
       "thal              2\n",
       "pred_attribute    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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V51M8YP6TadZE4D+qKsrMzFqn7BnBycCJwEsAEfE07mLCzGxMKBsEGyMiSF85lbRjdSWZ\nmVkrlQ2CayR9GZgq6UPAD/FDaszMxoSydw39c3pW8QvAPsA/RMSiSiszM7OWaBgEksYDt0bEMUDp\nN39JlwPvANZGxAFp3gXAh4B1abHzIuKWZos2M7OR0/DSUERsBl6W9Nomt30FcHyd+ZdGxOw0OATM\nzNqs7DeL/xd4UNIi0p1DABFx9nArRMTdknq2qTozM6tc2SD4XhpGwlmSTgP6gI9HxHMjtF0zM9sK\nWwwCSbMi4smIWDhC+/sicBHFbagXUTz57IPD7Hs+MB9g1qxZI7R76xzbI5V/oM306XuwZs3K6sqx\nFmnu7w7+27dCozaCbw+OSLp+W3cWEf0RsTkiBihuPz10C8suiIjeiOjt6ura1l1bx9lA8Xmg3ODH\nZ44Vzf3d/bdvjUZBUBvde23rziTNqJk8GVi2rds0M7Nt06iNIIYZb0jSN4CjgGmSVgHnA0dJmp22\ntRL4cDPbNDOzkdcoCA6U9ALFmcGkNE6ajoh4zXArRsTcOrMv27oyzcysKlsMgogY36pCzMysPZp5\nHoGZmY1BDgIzs8w5CMzMMucgMDPLnIPAzCxzDgIz63BFtxTNDN3dPe0uelQp2+mcmVmbDHZLUV5/\nf3P9GeXOZwRmZplzEJiZZc5BYGaWOQeBmVnm3Fhso0TzDzQZN24yAwMvV1SP2djhILBRovk7RwYG\n1PQ6r34Eh1kefGnIzCxzDgIzs8w5CMzMMucgMDPLnIPAzCxzDgIzs8xVFgSSLpe0VtKymnm7SFok\n6dH0c+eq9m9mZuVUeUZwBXD8kHnnArdHxN7A7WnazMzaqLIgiIi7gWeHzH4XsDCNLwROqmr/ZmZW\nTqvbCKZHxGqA9PP1wy0oab6kPkl969ata1mBZma56djG4ohYEBG9EdHb1dXV7nLMzMasVgdBv6QZ\nAOnn2hbv38zMhmh1EHwHmJfG5wE3tXj/ZmY2RJW3j34D+Cmwj6RVks4APgMcK+lR4Ng0bWZmbVRZ\nN9QRMXeYl46uap9mZta8jm0sNjOz1nAQmJllzkFgZpY5B4GZWeYcBGZmmXMQmJllzkFgZpY5B4GZ\nWeYcBGZmmXMQmJllzkFgZpY5B4GZWeYcBGNcd3cPkkoPZmPD9k39u+/u7ml3wW1VWe+j1hn6+58A\nook1HAY2FmygmX/3/f15/7v3GYGZWeYcBGZmmXMQmJllzkFgZpY5B4GZWeYcBGZmmWvL7aOSVgIv\nApuBTRHR2446zMysvd8jeFtEPNPG/ZuZGb40ZGaWvXYFQQC3SVoiaX69BSTNl9QnqW/dunUtLs/M\nLB/tCoIjIuJg4ATgo5LeOnSBiFgQEb0R0dvV1dX6Cs3MMtGWIIiIp9PPtcCNwKHtqMPMzNoQBJJ2\nlDRlcBw4DljW6jrMzKzQjruGpgM3pi6PJwBXR8QP2lCHmZnRhiCIiMeAA1u9XzMzq8+3j7ZJsw+M\nkcT48Ts2vY6ZWSN+ME2bNP/AGBgYUNPr+EEzZtaIzwjMzDLnIDAzy5yDwMwscw4CM7PMOQjMzNi+\n6Tvyurt72l30iPFdQ2ZmbKDZO/L6+8fOHXk+IzAzy5yDwMwscw4CM7PMOQhGSLNdRphZframa5lW\nNEq7sXiENN9lhMPALDdb07VMKxqlfUZgZpY5B4GZWeYcBGZmmXMQmJllbswHQae20pvZaNd8txSd\naszfNdSprfRmNto13y1Fp94tOObPCMzMbMvaEgSSjpf0iKRfSjq3HTWYmVmh5UEgaTzweeAEYH9g\nrqT9W12HmZkV2nFGcCjwy4h4LCI2At8E3tWGOszMjPY0Fu8GPFUzvQp489CFJM0H5qfJ9ZIe2fpd\nDttAMw14pu4aW9XC3+w6rdjHsOsMe+xtrqsV+9jCsW/Nfkbd76vNf3v/u296ja2/42iPMgu1Iwjq\nHdHvNb1HxAJgQaWFSH0R0VvlPjqVjz3PY4e8jz/nY9+SdlwaWgXsXjM9E3i6DXWYmRntCYKfA3tL\n2lPSdsB7ge+0oQ4zM6MNl4YiYpOks4BbgfHA5RGxvNV1JJVeeupwPvZ85Xz8OR/7sBTR7DfjzMxs\nLPE3i83MMucgMDPLXBZBIGkHSfdKul/SckkXpvl7Slos6VFJ30qN12OSpPGS7pN0c5rO6dhXSnpQ\n0lJJfWneLpIWpeNfJGnndtdZBUlTJV0n6WFJKyQdntGx75P+5oPDC5LOyeX4m5FFEFB0EzgnIg4E\nZgPHSzoMuAS4NCL2Bp4DzmhjjVX7GLCiZjqnYwd4W0TMrrmH/Fzg9nT8t6fpsejfgB9ExL7AgRT/\nBrI49oh4JP3NZwNvAl4GbiST429GFkEQhfVpcmIaApgDXJfmLwROakN5lZM0E/gz4KtpWmRy7Fvw\nLorjhjF6/JJeA7wVuAwgIjZGxPNkcOx1HA38KiKeIM/j36IsggB+d2lkKbAWWAT8Cng+IjalRVZR\ndH8xFv0r8AlgIE2/jnyOHYrQv03SktR1CcD0iFgNkH6+vm3VVWcvYB3wtXRZ8KuSdiSPYx/qvcA3\n0niOx79F2QRBRGxOp4gzKTq+26/eYq2tqnqS3gGsjYgltbPrLDrmjr3GERFxMEWPtx+V9NZ2F9Qi\nE4CDgS9GxEHAS2R4GSS1f50IXNvuWjpVNkEwKJ0a3wkcBkyVNPilurHa1cURwImSVlL09DqH4gwh\nh2MHICKeTj/XUlwjPhTolzQDIP1c274KK7MKWBURi9P0dRTBkMOx1zoB+EVE9Kfp3I6/oSyCQFKX\npKlpfBJwDEWj2R3AKWmxecBN7amwOhHxyYiYGRE9FKfHP4qIU8ng2AEk7ShpyuA4cBywjKJbk3lp\nsTF5/BGxBnhK0j5p1tHAQ2Rw7EPM5ZXLQpDf8TeUxTeLJb2RolFoPEX4XRMR/0/SXhSfkncB7gP+\nKiI2tK/Sakk6CvibiHhHLseejvPGNDkBuDoiLpb0OuAaYBbwJPAXEfFsm8qsjKTZFDcJbAc8BnyA\n9H+AMX7sAJImU3R7v1dE/E+al8XfvhlZBIGZmQ0vi0tDZmY2PAeBmVnmHARmZplzEJiZZc5BYGaW\nOQeBdRxJJ0sKSfu2YF9dqRfW+yQdOeS1c9Lth4PT639/C5XWdt6Q6Z9sw7ZOl7TrtldlY5GDwDrR\nXOAeii/AVe1o4OGIOCgifjzktXOAyXXWaZVXBUFE/PE2bOt0wEFgdTkIrKNI2omiW4wzqAkCSeMk\nfSE9T+JmSbdIOiW99iZJd6VO5W4d7D5gyHb3kHS7pAfSz1npy1b/BLw99Vc/qWb5syneOO+QdEfN\n/IvTcy1+Jml6mtcl6XpJP0/DEXX2f7qkmyT9QNIjks6vee3bqfblg53iSfoMMCnVdVWat75mnb9N\n+3pArzxfoyc9c+AraVu3SZqUfk+9wFVDj9MMgIjw4KFjBuCvgMvS+E+Ag9P4KcAtFB9euimeoXAK\nRZfiPwG60nLvAS6vs93vAvPS+AeBb6fx04HPDVPLSmBazXQA70zj/wR8Ko1fDbwljc8CVtTZ1unA\naoqeXydRdHPRm17bJf0cnP+6NL1+yDbWp5/HUTyEXen3cTNFd9M9wCZgdlruGopvjEPRv1Zvu/++\nHjpzGOx0zKxTzKXoFA+KLjDmAr8A3gJcGxEDwJqaT+n7AAcAi4rHLDCe4g13qMOBd6fxKyneyJu1\nkeJNF2AJcGwaPwbYP+0f4DWSpkTEi0PWXxQRvwaQdEM6pj7gbEknp2V2B/YGfr2FOo5Lw31peqe0\nzpPA4xGxtKbGnmYO0PLkILCOkfqAmQMcICko3tRD0ieo33U2af7yiDi8yd1tTd8qv42IwfU288r/\nn3HA4RHxmyb3Gan/p2PS+i9LuhPYocF2BPxjRHz5VTOlHoqn8Q3aTHGWYbZFbiOwTnIK8PWI2CMi\neiJid+Bxik/O9wB/ntoKpgNHpXUeAbokHQ4gaaKkP6qz7Z/wSpvDqWl7jbwITCmx3G3AWYMTqe2h\nnmNVPC93EsVTsf4TeC3wXAqBfSm6Rx/0W0kT62znVuCDqT0FSbtJavRwlbLHYhlyEFgnmcsrPYUO\nuh54X/q5iuIa+peBxcD/RMRGigC5RNL9wFKg3t01ZwMfkPQA8H6KZzg3sgD4fm1j8TDOBnpTw+1D\nwJnDLHcPxWWppcD1EdEH/ACYkOq6CPjZkP0/MNhYPCgibqNol/ippAcpnjPQ6E3+CuBLbiy2etz7\nqI0aknaKiPXpEtK9FE8eW9PuusqQdDpFY+1ZjZY1azW3EdhocnN6wNB2wEWjJQTMOp3PCMzMMuc2\nAjOzzDkIzMwy5yAwM8ucg8DMLHMOAjOzzP1/wKl88lVkWhYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efd77eb3278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df['age'], color='blue', edgecolor='black', bins=24)\n",
    "plt.xlabel('Age of the patient')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Age distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 206 males and 97 females in the dataset\n"
     ]
    }
   ],
   "source": [
    "men = df['sex'].sum() # '1' indicates male and '0' indicates female\n",
    "print('There are '+ str(men) + ' males and '+ str(df.shape[0] - men) + ' females in the dataset')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cp\n",
       "1     23\n",
       "2     50\n",
       "3     86\n",
       "4    144\n",
       "Name: cp, dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('cp')['cp'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tn5G0HfD8QG+QNKH/ngNJywO7kTqbfw98IK82Gbh0sJk2M7ORU7SP4AvAhZL6f72vBRzY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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efd77f117f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df['trestbps'], color='blue', edgecolor='black', bins=int(106/3))\n",
    "plt.xlabel('Blood pressure')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Resting Blood pressure distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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za7dWE8HZ7QzCzMzK01IiiIj5knYBdo+IqyRtDkxob2hmZtYJrZahfhNwCfC1vGoH4NJ2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\nTgRmZhXnRGBmVnFOBGZmFfd/s9uae/kqBkUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efd77e48a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df['chol'], color='blue', edgecolor='black', bins=73)\n",
    "plt.xlabel('Serum cholestrol(mg/dl)')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Serum cholestrol distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 45 patients who had resting blood sugar > 120 mg/dl out of the 303 patients\n"
     ]
    }
   ],
   "source": [
    "diabetic = df['fbs'].sum() # '1' indicates yes and '0' indicates no about resting blood sugar\n",
    "print('There are '+ str(diabetic) + ' patients who had resting blood sugar > 120 mg/dl out of the '+ str(df.shape[0]) + ' patients')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "restecg\n",
       "0    151\n",
       "1      4\n",
       "2    148\n",
       "Name: restecg, dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('restecg')['restecg'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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aqS+cW4Et8nInACfn8UkV618E/H0enwf8e8W8C4DDahzTBcD15P73+zmGY4Bz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\nKLsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efd77e21cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df['thalach'], color='blue', edgecolor='black', bins=20)\n",
    "plt.xlabel('Max heart rate achieved')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Max heart rate distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 99 patients who had exercise induced angina out of the 303 patients\n"
     ]
    }
   ],
   "source": [
    "eig = df['exang'].sum() # '1' indicates yes and '0' indicates no about exercise induced angina\n",
    "print('There are '+ str(eig) + ' patients who had exercise induced angina out of the '+ str(df.shape[0]) + ' patients')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qpG9d4CBgU9J1iN5ei5gHzIuIWXn8fFLCWChpIkD++0gvt2tmZgOoNx0GbR4RTwNIOg44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\nzMyKnCDMzKzo/wAacj23mUmRugAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efd77f0cc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df['oldpeak'], color='blue', edgecolor='black', bins=20)\n",
    "plt.xlabel('ST depression induced by exercise relative to rest')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Old peak distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "slop\n",
       "1    142\n",
       "2    140\n",
       "3     21\n",
       "Name: slop, dtype: int64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('slop')['slop'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ca\n",
       "0.0    176\n",
       "1.0     65\n",
       "2.0     38\n",
       "3.0     20\n",
       "Name: ca, dtype: int64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('ca')['ca'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['ca'] = df['ca'].fillna(df['ca'].mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ca\n",
       "0.0    180\n",
       "1.0     65\n",
       "2.0     38\n",
       "3.0     20\n",
       "Name: ca, dtype: int64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('ca')['ca'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "thal\n",
       "3.0    166\n",
       "6.0     18\n",
       "7.0    117\n",
       "Name: thal, dtype: int64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('thal')['thal'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['thal'] = df['thal'].fillna(df['thal'].mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "thal\n",
       "3.0    168\n",
       "6.0     18\n",
       "7.0    117\n",
       "Name: thal, dtype: int64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('thal')['thal'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age               0\n",
       "sex               0\n",
       "cp                0\n",
       "trestbps          0\n",
       "chol              0\n",
       "fbs               0\n",
       "restecg           0\n",
       "thalach           0\n",
       "exang             0\n",
       "oldpeak           0\n",
       "slop              0\n",
       "ca                0\n",
       "thal              0\n",
       "pred_attribute    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# # ML models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y = df['pred_attribute']\n",
    "df.drop('pred_attribute', axis=1, inplace=True)\n",
    "x = df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rstate = 42\n",
    "x_train, x_test, y_train, y_test = train_test_split(x,y,random_state=rstate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=0.01, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=500, multi_class='multinomial',\n",
       "          n_jobs=1, penalty='l2', random_state=None, solver='lbfgs',\n",
       "          tol=10, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LogisticRegression(solver='lbfgs', multi_class='multinomial',C=1e-2,max_iter=500,tol=10)\n",
    "model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "56.57894736842105"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = model.predict(x_test)\n",
    "accuracy_score(predictions, y_test) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultinomialNB(alpha=60, class_prior=None, fit_prior=True)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = MultinomialNB(alpha=60)\n",
    "model.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "56.57894736842105"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = model.predict(x_test)\n",
    "accuracy_score(predictions, y_test) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB(priors=None)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = GaussianNB()\n",
    "model.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "46.05263157894737"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = model.predict(x_test)\n",
    "accuracy_score(predictions, y_test) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BernoulliNB(alpha=1.0, binarize=0.0, class_prior=None, fit_prior=True)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = BernoulliNB()\n",
    "model.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "51.31578947368421"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = model.predict(x_test)\n",
    "accuracy_score(predictions, y_test) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearSVC(C=0.25, class_weight=None, dual=True, fit_intercept=True,\n",
       "     intercept_scaling=1, loss='squared_hinge', max_iter=4000,\n",
       "     multi_class='ovr', penalty='l2', random_state=42, tol=0.0001,\n",
       "     verbose=0)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LinearSVC(C=0.25, max_iter=4000, random_state=42)\n",
    "model.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "57.89473684210527"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = model.predict(x_test)\n",
    "accuracy_score(predictions, y_test) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=5, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=42, shrinking=True, tol=0.1,\n",
       "  verbose=False)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = svm.SVC(C=5, random_state=42, tol=0.1)\n",
    "model.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "51.31578947368421"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = model.predict(x_test)\n",
    "accuracy_score(predictions, y_test) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=42,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = tree.DecisionTreeClassifier(random_state=42)\n",
    "model.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "52.63157894736842"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = model.predict(x_test)\n",
    "accuracy_score(predictions, y_test) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
       "            oob_score=False, random_state=42, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = RandomForestClassifier(random_state=42)\n",
    "model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "56.57894736842105"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = model.predict(x_test)\n",
    "accuracy_score(predictions, y_test) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.11429169, 0.03569407, 0.06302442, 0.08439245, 0.11292135,\n",
       "       0.02340423, 0.03795025, 0.14758517, 0.053465  , 0.10826695,\n",
       "       0.04083989, 0.09000716, 0.08815737])"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=1, n_neighbors=10, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = KNeighborsClassifier(n_neighbors=10)\n",
    "model.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "55.26315789473685"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = model.predict(x_test)\n",
    "accuracy_score(predictions, y_test) * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Neural Networks! "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "encoder = LabelEncoder()\n",
    "encoder.fit(y)\n",
    "encoded_y = encoder.transform(y)\n",
    "dummy_y = np_utils.to_categorical(encoded_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "x = x.drop(x.columns[[1,5,6,8,10]], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def baseline_model():\n",
    "    # create model\n",
    "    model = Sequential()\n",
    "    model.add(Dense(16, input_dim=8, activation='relu'))\n",
    "    model.add(Dense(32, activation='relu'))\n",
    "    model.add(Dense(16, activation='relu'))\n",
    "    model.add(Dense(16, activation='relu'))\n",
    "    model.add(Dense(5, activation='softmax'))\n",
    "    # Compile model\n",
    "    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "estimator = KerasClassifier(build_fn=baseline_model, epochs=200, batch_size=10, verbose=0)\n",
    "kfold = KFold(n_splits=10, shuffle=True, random_state=rstate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "results = cross_val_score(estimator, x, dummy_y, cv=kfold)\n",
    "print(\"Baseline: %.2f%% (%.2f%%)\" % (results.mean()*100, results.std()*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "encoder = LabelEncoder()\n",
    "encoder.fit(y_test)\n",
    "encoded_y = encoder.transform(y_test)\n",
    "dummy_y = np_utils.to_categorical(encoded_y)\n",
    "encoder1 = LabelEncoder()\n",
    "encoder1.fit(y_train)\n",
    "encoded_y1 = encoder1.transform(y_train)\n",
    "dummy_y1 = np_utils.to_categorical(encoded_y1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "#x_test = x_test.drop(x_test.columns[[1,5,6,8,10]], axis=1)\n",
    "x_train = x_train.drop(x_train.columns[[1,5,6,8,10]], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "estimator = KerasClassifier(build_fn=baseline_model, epochs=200, batch_size=10, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "estimator.model = load_model('model.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7efd5d5db550>"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "estimator.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predictions = estimator.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "52.63157894736842"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(predictions,y_test)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "       colsample_bytree=1, gamma=0, learning_rate=0.005, max_delta_step=0,\n",
       "       max_depth=3, min_child_weight=1, missing=None, n_estimators=100,\n",
       "       n_jobs=5, nthread=None, objective='multi:softprob', random_state=0,\n",
       "       reg_alpha=0, reg_lambda=1, scale_pos_weight=1.2, seed=None,\n",
       "       silent=True, subsample=1)"
      ]
     },
     "execution_count": 318,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = XGBClassifier(n_jobs=5,learning_rate=0.005,s)\n",
    "model.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/pratik/anaconda3/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    }
   ],
   "source": [
    "predictions = model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "52.63157894736842"
      ]
     },
     "execution_count": 320,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(predictions,y_test)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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