Result of Echocardiogram Dataset .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# import the python packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd # for data handling\n",
    "import numpy as np # for data manipulation \n",
    "import sklearn as sk\n",
    "from matplotlib import pyplot as plt # for plotting\n",
    "import matplotlib.patches as mpatches\n",
    "from sklearn.preprocessing import LabelEncoder # For encoding class variables\n",
    "from sklearn.model_selection import train_test_split # for train and test split\n",
    "from sklearn.svm import SVC # to built svm model\n",
    "from sklearn import svm # inherits other SVM objects\n",
    "from sklearn import metrics # to calculate classifiers accuracy\n",
    "from sklearn.model_selection import cross_val_score # to perform cross validation\n",
    "from sklearn.preprocessing import StandardScaler # to perform standardization\n",
    "from sklearn.model_selection import GridSearchCV # to perform grid search for all classifiers\n",
    "from sklearn import tree # to perform decision tree classification\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn import neighbors # to perform knn\n",
    "from sklearn import naive_bayes # to perform Naive Bayes\n",
    "from sklearn.metrics import classification_report # produce classifier reports\n",
    "from sklearn.ensemble import RandomForestClassifier # to perform ensemble bagging - random forest\n",
    "from sklearn.ensemble import AdaBoostClassifier # to perform ensemble boosting\n",
    "from sklearn.metrics import roc_curve, auc # to plot ROC Curve\n",
    "from scipy import stats\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Manipulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = pd.read_table('echocardiogram.txt',sep=',',na_values=['?'])\n",
    "df_train.columns = [\"survival\", \"still-alive\", \"age-at-heart-attack\", \"pericardial-effusion\",\"fractional-shortening\",\"epss\",\"lvdd\",\"wall-motion-score\",\"wall-motion-index\",\"mult\",\"name\",\"group\",\"alive-at-1\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'survival', u'still-alive', u'age-at-heart-attack',\n",
       "       u'pericardial-effusion', u'fractional-shortening', u'epss', u'lvdd',\n",
       "       u'wall-motion-score', u'wall-motion-index', u'mult', u'name', u'group',\n",
       "       u'alive-at-1'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(131, 13)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survival</th>\n",
       "      <th>still-alive</th>\n",
       "      <th>age-at-heart-attack</th>\n",
       "      <th>pericardial-effusion</th>\n",
       "      <th>fractional-shortening</th>\n",
       "      <th>epss</th>\n",
       "      <th>lvdd</th>\n",
       "      <th>wall-motion-score</th>\n",
       "      <th>wall-motion-index</th>\n",
       "      <th>mult</th>\n",
       "      <th>name</th>\n",
       "      <th>group</th>\n",
       "      <th>alive-at-1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.380</td>\n",
       "      <td>6.000</td>\n",
       "      <td>4.100</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.70</td>\n",
       "      <td>0.588</td>\n",
       "      <td>name</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.260</td>\n",
       "      <td>4.000</td>\n",
       "      <td>3.420</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.000</td>\n",
       "      <td>name</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>57.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.253</td>\n",
       "      <td>12.062</td>\n",
       "      <td>4.603</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1.45</td>\n",
       "      <td>0.788</td>\n",
       "      <td>name</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.160</td>\n",
       "      <td>22.000</td>\n",
       "      <td>5.750</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.25</td>\n",
       "      <td>0.571</td>\n",
       "      <td>name</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>26.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.260</td>\n",
       "      <td>5.000</td>\n",
       "      <td>4.310</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.857</td>\n",
       "      <td>name</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survival  still-alive  age-at-heart-attack  pericardial-effusion  \\\n",
       "0      19.0          0.0                 72.0                   0.0   \n",
       "1      16.0          0.0                 55.0                   0.0   \n",
       "2      57.0          0.0                 60.0                   0.0   \n",
       "3      19.0          1.0                 57.0                   0.0   \n",
       "4      26.0          0.0                 68.0                   0.0   \n",
       "\n",
       "   fractional-shortening    epss   lvdd  wall-motion-score  wall-motion-index  \\\n",
       "0                  0.380   6.000  4.100               14.0               1.70   \n",
       "1                  0.260   4.000  3.420               14.0               1.00   \n",
       "2                  0.253  12.062  4.603               16.0               1.45   \n",
       "3                  0.160  22.000  5.750               18.0               2.25   \n",
       "4                  0.260   5.000  4.310               12.0               1.00   \n",
       "\n",
       "    mult  name  group  alive-at-1  \n",
       "0  0.588  name    1.0         0.0  \n",
       "1  1.000  name    1.0         0.0  \n",
       "2  0.788  name    1.0         0.0  \n",
       "3  0.571  name    1.0         0.0  \n",
       "4  0.857  name    1.0         0.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = df_train.sort_values('survival')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>survival</th>\n",
       "      <th>still-alive</th>\n",
       "      <th>age-at-heart-attack</th>\n",
       "      <th>pericardial-effusion</th>\n",
       "      <th>fractional-shortening</th>\n",
       "      <th>epss</th>\n",
       "      <th>lvdd</th>\n",
       "      <th>wall-motion-score</th>\n",
       "      <th>wall-motion-index</th>\n",
       "      <th>mult</th>\n",
       "      <th>name</th>\n",
       "      <th>group</th>\n",
       "      <th>alive-at-1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>76</td>\n",
       "      <td>0.03</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.260</td>\n",
       "      <td>19.4</td>\n",
       "      <td>4.77</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2.10</td>\n",
       "      <td>0.714</td>\n",
       "      <td>name</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>47</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2.30</td>\n",
       "      <td>0.714</td>\n",
       "      <td>name</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>69</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.225</td>\n",
       "      <td>12.2</td>\n",
       "      <td>5.20</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.786</td>\n",
       "      <td>name</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>75</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.220</td>\n",
       "      <td>21.7</td>\n",
       "      <td>4.85</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.15</td>\n",
       "      <td>0.928</td>\n",
       "      <td>name</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>name</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  survival  still-alive  age-at-heart-attack  pericardial-effusion  \\\n",
       "0     76      0.03          1.0                  NaN                   0.0   \n",
       "1     47      0.25          1.0                 63.0                   1.0   \n",
       "2     69      0.25          1.0                 86.0                   0.0   \n",
       "3     75      0.25          1.0                 68.0                   0.0   \n",
       "4     27      0.25          1.0                 75.0                   0.0   \n",
       "\n",
       "   fractional-shortening  epss  lvdd  wall-motion-score  wall-motion-index  \\\n",
       "0                  0.260  19.4  4.77               21.0               2.10   \n",
       "1                    NaN   NaN   NaN               23.0               2.30   \n",
       "2                  0.225  12.2  5.20               24.0               2.18   \n",
       "3                  0.220  21.7  4.85               15.0               1.15   \n",
       "4                    NaN   NaN   NaN                NaN               1.00   \n",
       "\n",
       "    mult  name  group  alive-at-1  \n",
       "0  0.714  name    2.0         1.0  \n",
       "1  0.714  name    2.0         1.0  \n",
       "2  0.786  name    2.0         1.0  \n",
       "3  0.928  name    2.0         NaN  \n",
       "4    NaN  name    2.0         NaN  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train = df_train.reset_index()\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Removal of unnessary Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "del df_train[\"group\"]\n",
    "del df_train[\"name\"] \n",
    "del df_train[\"wall-motion-score\"]\n",
    "del df_train[\"mult\"]\n",
    "del df_train[\"index\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survival</th>\n",
       "      <th>still-alive</th>\n",
       "      <th>age-at-heart-attack</th>\n",
       "      <th>pericardial-effusion</th>\n",
       "      <th>fractional-shortening</th>\n",
       "      <th>epss</th>\n",
       "      <th>lvdd</th>\n",
       "      <th>wall-motion-index</th>\n",
       "      <th>alive-at-1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.03</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.260</td>\n",
       "      <td>19.4</td>\n",
       "      <td>4.77</td>\n",
       "      <td>2.10</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.30</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.225</td>\n",
       "      <td>12.2</td>\n",
       "      <td>5.20</td>\n",
       "      <td>2.18</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.220</td>\n",
       "      <td>21.7</td>\n",
       "      <td>4.85</td>\n",
       "      <td>1.15</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survival  still-alive  age-at-heart-attack  pericardial-effusion  \\\n",
       "0      0.03          1.0                  NaN                   0.0   \n",
       "1      0.25          1.0                 63.0                   1.0   \n",
       "2      0.25          1.0                 86.0                   0.0   \n",
       "3      0.25          1.0                 68.0                   0.0   \n",
       "4      0.25          1.0                 75.0                   0.0   \n",
       "\n",
       "   fractional-shortening  epss  lvdd  wall-motion-index  alive-at-1  \n",
       "0                  0.260  19.4  4.77               2.10         1.0  \n",
       "1                    NaN   NaN   NaN               2.30         1.0  \n",
       "2                  0.225  12.2  5.20               2.18         1.0  \n",
       "3                  0.220  21.7  4.85               1.15         NaN  \n",
       "4                    NaN   NaN   NaN               1.00         NaN  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index([u'survival', u'still-alive', u'age-at-heart-attack',\n",
      "       u'pericardial-effusion', u'fractional-shortening', u'epss', u'lvdd',\n",
      "       u'wall-motion-index', u'alive-at-1'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "lable = df_train.columns\n",
    "print lable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data types of feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "survival                 float64\n",
       "still-alive              float64\n",
       "age-at-heart-attack      float64\n",
       "pericardial-effusion     float64\n",
       "fractional-shortening    float64\n",
       "epss                     float64\n",
       "lvdd                     float64\n",
       "wall-motion-index        float64\n",
       "alive-at-1               float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Seperating Independent and Target Variables: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Independent var: \\n',    survival  still-alive  age-at-heart-attack  pericardial-effusion  \\\n",
      "0      0.03          1.0               -1.000                   0.0   \n",
      "1      0.25          1.0               63.000                   1.0   \n",
      "2      0.25          1.0               86.000                   0.0   \n",
      "3      0.25          1.0               68.000                   0.0   \n",
      "4      0.25          1.0               75.000                   0.0   \n",
      "5      0.50          1.0               73.000                   0.0   \n",
      "6      0.50          1.0               59.000                   0.0   \n",
      "7      0.50          1.0               67.000                   0.0   \n",
      "8      0.50          1.0               62.529                   1.0   \n",
      "9      0.50          1.0               69.000                   1.0   \n",
      "\n",
      "   fractional-shortening    epss  lvdd  wall-motion-index  \n",
      "0                  0.260  19.400  4.77               2.10  \n",
      "1                 -1.000  -1.000 -1.00               2.30  \n",
      "2                  0.225  12.200  5.20               2.18  \n",
      "3                  0.220  21.700  4.85               1.15  \n",
      "4                 -1.000  -1.000 -1.00               1.00  \n",
      "5                  0.230  12.733  6.06               1.50  \n",
      "6                  0.130  16.400  4.96               1.37  \n",
      "7                  0.155  11.300  5.16               1.00  \n",
      "8                  0.070  20.000  5.20               2.00  \n",
      "9                  0.260  11.000  4.65               1.64  , '\\n')\n",
      "('Dependent var: \\n', 0    1.0\n",
      "1    1.0\n",
      "2    1.0\n",
      "3   -1.0\n",
      "4   -1.0\n",
      "5    1.0\n",
      "6   -1.0\n",
      "7   -1.0\n",
      "8    1.0\n",
      "9    1.0\n",
      "Name: alive-at-1, dtype: float64)\n"
     ]
    }
   ],
   "source": [
    "data_x = df_train[lable[0:8]].copy()\n",
    "data_x = data_x.fillna(value=-1.0)\n",
    "data_y = df_train[lable[-1]].copy()\n",
    "data_y = data_y.fillna(value=-1.0)\n",
    "print('Independent var: \\n',data_x.head(10),'\\n')\n",
    "print('Dependent var: \\n',data_y.head(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Target Variable Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('sample values of target values:\\n', array([2, 2, 2, 0, 0, 2, 0, 0, 2, 2]))\n"
     ]
    }
   ],
   "source": [
    "encode_obj = LabelEncoder()\n",
    "data_y = encode_obj.fit_transform(data_y)\n",
    "print('sample values of target values:\\n',data_y[0:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setting a benchmark accuracy for classifiers using Raw Data & Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_x_train,test_x_test,test_y_train,test_y_test = train_test_split(data_x,data_y,train_size=0.75,test_size=0.25,random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Accuracy obtained from train-test split on training data is:', 0.58163265306122447)\n",
      "('Accuracy obtained from train-test split on testing data is:', 0.60606060606060608)\n"
     ]
    }
   ],
   "source": [
    "nbclf = naive_bayes.GaussianNB()\n",
    "nbclf = nbclf.fit(test_x_train, test_y_train)\n",
    "nbpreds_test = nbclf.predict(test_x_test)\n",
    "print('Accuracy obtained from train-test split on training data is:',nbclf.score(test_x_train, test_y_train))\n",
    "print('Accuracy obtained from train-test split on testing data is:',nbclf.score(test_x_test, test_y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Accuracy obtained from 10-fold cross validation on actual raw data is:', 0.55232267732267737)\n"
     ]
    }
   ],
   "source": [
    "test_eval_result = cross_val_score(nbclf, data_x, data_y, cv=10, scoring='accuracy')\n",
    "print('Accuracy obtained from 10-fold cross validation on actual raw data is:',test_eval_result.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_y = df_train[lable[-1]].copy()\n",
    "data_y = data_y.fillna(value=-1.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([   1.,    0.,    0.,    0.,    0.,  106.,    0.,    0.,    0.,   24.]),\n",
       " array([-1. , -0.8, -0.6, -0.4, -0.2,  0. ,  0.2,  0.4,  0.6,  0.8,  1. ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAGCNJREFUeJzt3X+MXWWdx/H3Z1t+KKAttLCVFoca\ngvSPtZAJohiDFDZSTAsRNrAGiylbUdnAYqJVk93VbGIxiqzRQApF64afFpAKKFZoY0iWyhQqtA7Y\nFivUju2A8mtNwMJ3/zjPwHU6M/fMvfecc+fM55VM7vl17/M95zz99tznPs85igjMzGzi+7uqAzAz\ns85wQjczqwkndDOzmnBCNzOrCSd0M7OacEI3M6sJJ3Qzs5pwQjczqwkndDOzmphaZmEzZsyInp6e\nMou0SWTTpk3PRcTMsst1vbai5a3bpSb0np4e+vr6yizSJhFJv6+iXNdrK1reuu0mFzOzmnBCNzOr\niaYJXdLBkn4l6deStkr6alp+rKSNkrZJuk3SgcWHa2Zmo8nThv4qcHpEvCLpAOAhST8FrgS+HRG3\nSroOWApc22ogPcvvbel9O1ec3WqRZlaRVv69+996c02v0CPzSpo9IP0FcDqwJi1fDZxTSIRmZpZL\nrjZ0SVMkbQb2AuuAHcALEbEvbbILOLqYEM3MLI9cCT0iXo+I+cBs4GTghJE2G+m9kpZJ6pPUNzg4\n2HqkZmY2pnH1comIF4ANwCnANElDbfCzgd2jvGdlRPRGRO/MmaWP+TAzmzTy9HKZKWlamn4bcAbQ\nD6wHzkubLQHuLipIMzNrLk8vl1nAaklTyP4DuD0i7pH0G+BWSf8FPAasKjBOMzNromlCj4jHgRNH\nWP40WXu6mZl1AY8UNTOriVJvzmXWTSQdD9zWsGgu8O/ANOBfgKFuWV+OiPtKDs9s3JzQbdKKiKeA\n+ZCNtQD+ANwFfIpsFPQ3KwzPbNzc5GKWWQDsiIhKbsFr1glO6GaZC4BbGuYvk/S4pBslTa8qKLPx\ncEK3SS/dKXQR8KO06FrgPWTNMQPAt0Z4j0dAW9dxQjeDs4BHI2IPQETsSbe7eAO4nhG653oEtHUj\nJ3QzuJCG5hZJsxrWnQtsKT0isxa4l4tNapLeDpwJfLph8TckzSe74dzOYevMupYTuk1qEfEX4Ihh\nyy6qKByztrjJxcysJpzQzcxqwgndzKwmnNDNzGrCCd3MrCac0M3MasIJ3cysJpzQzcxqwgndzKwm\nnNDNzGrCCd3MrCac0M3MaqJpQpc0R9J6Sf2Stkq6PC0/XNI6SdvSq5/qYmZWoTxX6PuAz0fECcAp\nwOckzQOWAw9ExHHAA2nezMwq0jShR8RARDyapl8G+oGjgcXA6rTZauCcooI0M7PmxtWGLqkHOBHY\nCBwVEQOQJX3gyFHe42cvmpmVIHdCl3QocAdwRUS8lPd9fvaimVk5ciV0SQeQJfObIuLOtHjP0LMX\n0+veYkI0M7M88vRyEbAK6I+IqxtWrQWWpOklwN2dD8+sWJJ2SnpC0mZJfWmZe3DZhJTnCv1U4CLg\n9FTpN0taCKwAzpS0jewhuysKjNOsSB+JiPkR0Zvm3YPLJqSmD4mOiIcAjbJ6QWfDMesKi4HT0vRq\nYAPwxaqCMcvLI0Vtsgvg55I2SVqWluXqwWXWbZpeoZvV3KkRsVvSkcA6SU/meVNK/ssAjjnmmCLj\nM8vNV+g2qUXE7vS6F7gLOJkcPbjcHde6kRO6TVqSDpF02NA08I/AFtyDyyYoN7mUoGf5vaWWt3PF\n2aWWN4EdBdyV9cxlKnBzRPxM0iPA7ZKWAs8A51cYo1luTug2aUXE08D7Rlj+PO7BZROQm1zMzGrC\nCd3MrCac0M3MasJt6GZmHdRqJ4hOdGZwQh+HsnurmJmNh5tczMxqwgndzKwmnNDNzGrCCd3MrCac\n0M3MasK9XGqoym5TZlYdX6GbmdWEE7qZWU04oZuZ1YQTuplZTTRN6JJulLRX0paGZYdLWidpW3qd\nXmyYZmbWTJ5eLj8Avgv8sGHZcuCBiFghaXma/2LnwyuG78liZnXU9Ao9In4J/GnY4sXA6jS9Gjin\nw3GZmdk4tdqGflREDACk1yM7F5JZ8STNkbReUr+krZIuT8v/U9IfJG1OfwurjtUsr8IHFklaBiwD\nOOaYY4ouziyvfcDnI+JRSYcBmyStS+u+HRHfrDA2s5a0eoW+R9IsgPS6d7QNI2JlRPRGRO/MmTNb\nLM6ssyJiICIeTdMvA/3A0dVGZdaeVhP6WmBJml4C3N2ZcMzKJ6kHOBHYmBZdJunx1MPLPbhswsjT\nbfEW4H+B4yXtkrQUWAGcKWkbcGaaN5twJB0K3AFcEREvAdcC7wHmAwPAt0Z53zJJfZL6BgcHS4vX\nbCxN29Aj4sJRVi3ocCxmpZJ0AFkyvyki7gSIiD0N668H7hnpvRGxElgJ0NvbG8VHa9acR4rapCRJ\nwCqgPyKublg+q2Gzc4Etw99r1q0m/O1zPUjIWnQqcBHwhKTNadmXgQslzQcC2Al8uprwzMZvwid0\ns1ZExEOARlh1X9mxmHWKm1zMzGrCV+j2plaar/yUI7Pu4St0M7OacEI3M6sJN7lYW8ruZeQmHrPR\n+QrdzKwmnNDNzGrCCd3MrCac0M3MasIJ3cysJpzQzcxqwgndzKwmnNDNzGrCCd3MrCac0M3MasIJ\n3cysJpzQzcxqwgndzKwmnNDNzGqirYQu6aOSnpK0XdLyTgVlVjXXbZuIWk7okqYA3wPOAuaRPS19\nXqcCM6uK67ZNVO084OJkYHtEPA0g6VZgMfCbTgRmVqGO1e1WHwDiB3lYK9pJ6EcDzzbM7wLeP3wj\nScuAZWn2FUlPNfncGcBzbcRVNcdfIF015urjO1RM07o9jnrd0vFssp+t6qZzO+5Y6n5MdNWYsbw7\nz2e0k9A1wrLYb0HESmBl7g+V+iKit424KuX4qyOpr1MfNcKyv6nbeet1Nx1Px9K9cUBnYmnnR9Fd\nwJyG+dnA7naCMesSrts2IbWT0B8BjpN0rKQDgQuAtZ0Jy6xSrts2IbXc5BIR+yRdBtwPTAFujIit\nHYgpd/NMl3L81elI7B2u2910PB3L/rolDuhALIrYr9nbzMwmII8UNTOrCSd0M7Oa6KqEPpGGW0ua\nI2m9pH5JWyVdnpYfLmmdpG3pdXrVsY5F0hRJj0m6J80fK2ljiv+29KNgV5I0TdIaSU+m8/CBqo+/\npPNTfXhD0qhd0Mqo63mPhaTXJW1Ofx378bfZPko6KNWx7anO9XSq7BZiuVjSYMNxuKSgOG6UtFfS\nllHWS9J3UpyPSzppXAVERFf8kf34tAOYCxwI/BqYV3VcY8Q7CzgpTR8G/JZsmPg3gOVp+XLgqqpj\nbbIfVwI3A/ek+duBC9L0dcBnqo5xjNhXA5ek6QOBaVUff+AEsgFOG4DeUbYppa7nPRbAKwWU3XQf\ngc8C16XpC4DbCjoneWK5GPhuCfXjw8BJwJZR1i8Efko2FuIUYON4Pr+brtDfHG4dEa8BQ8Otu1JE\nDETEo2n6ZaCfbIThYrJEQ3o9p5oIm5M0GzgbuCHNCzgdWJM26dr4Jb2D7B/HKoCIeC0iXqDi4x8R\n/RHRbDR0WXW9ymORZx8b41sDLEh1sIpYShERvwT+NMYmi4EfRuZhYJqkWXk/v5sS+kjDrY+uKJZx\nSV8VTwQ2AkdFxABkSR84srrImroG+ALwRpo/AnghIval+W4+B3OBQeD7qcnoBkmHMDGOf1l1Pe+x\nOFhSn6SHJXUq6efZxze3SXXuRbI62Gl5j/fHUzPHGklzRlhfhrbqRjtD/zst160Euo2kQ4E7gCsi\n4qViLjA6T9LHgL0RsUnSaUOLR9i0W8/BVLKvrv8aERsl/TdZs0LhJP0C+PsRVn0lIu7O8xEjLGvp\nOI8Vyzg+5piI2C1pLvCgpCciYkcr8TSGNsKy4ftYVn3LU85PgFsi4lVJl5J9czi9gFiaaeuYdFNC\nn3DDrSUdQJbMb4qIO9PiPZJmRcRA+qq0t7oIx3QqsEjSQuBg4B1kV+zTJE1NV0zdfA52AbsiYmOa\nX0OW0As//hFxRpsf0bG6PlYsknIdi4jYnV6flrSB7Ntmuwk9zz4ObbNL0lTgnYzdHFFYLBHxfMPs\n9UAxtwJrrq260U1NLhNquHVq61sF9EfE1Q2r1gJL0vQSIM8VW+ki4ksRMTsiesiO9YMR8QlgPXBe\n2qyb4/8j8KykoTssLiC7ve1EOP5l1fWmx0LSdEkHpekZZP/Rd+IW2Hn2sTG+88jqYBFX6E1jGdZO\nvYjsN7EqrAU+mXq7nAK8ONRslkvRv+qO8xfghWS9RXaQfX2tPKYxYv0Q2Vehx4HN6W8hWRvgA8C2\n9Hp41bHm2JfTeKuXy1zgV8B24EfAQVXHN0bc84G+dA5+DEyv+vgD55JdZb0K7AHuT8vfBdzXsF3h\ndX20YwH0Ajek6Q8CT5D1/HgCWNrB8vfbR+BrwKI0fXCqY9tTnZtb4HlpFsvXga3pOKwH3ltQHLcA\nA8BfUz1ZClwKXJrWi+zhKjvS+Rixp9Rofx76b2ZWE93U5GJmZm1wQjczqwkndDOzmii12+KMGTOi\np6enzCJtEtm0adNzETGz7HJdr61oeet2qQm9p6eHvr5OPfbR7G9J+n0V5bpeW9Hy1m03uZiZ1YQT\nuplZTTihm5nVRDfdy8Uq1rP83nG/Z+eKswuIxOrOda0YvkK32hvpKTGjPc2n7SfGmFXICd0mgx8A\nHx22bDnwQEQcR3afk6Fb754FHJf+lgHXlhSjWduc0K32YuSnxIz2NJ+2nhhjViUndJusRnuaz4R9\ncpaZE7rZ38r1xBhJy9Jj2/oGBwdLCMusOSd0m6z2DDWlDHuaT64nxkTEyojojYjemTNLv9uA2Yic\n0G2yGu1pPu09McasQu6HbrUn6RaypzLNkLQL+A9gBXC7pKXAM8D5afP7yJ5usx34C/Cp0gM2a5ET\nutVeRFw4yqoFI2wbwOeKjcisGG5yMTOrCSd0M7OacEI3M6sJJ3Qzs5pwQjczqwkndDOzmnBCNzOr\nCSd0M7OacEI3M6sJJ3Qzs5pwQjczqwkndDOzmnBCNzOridwJXdIUSY9JuifNHytpY3pq+m2SDiwu\nTDMza2Y8V+iXA/0N81cB305PTf8zsLSTgZmZ2fjkSuiSZgNnAzekeQGnA2vSJo1PTTczswrkvUK/\nBvgC8EaaPwJ4ISL2pXk/Gd3MrGJNE7qkjwF7I2JT4+IRNt3vyejp/X46uplZCfJcoZ8KLJK0E7iV\nrKnlGmCapKFH2I34ZHTw09HNzMrSNKFHxJciYnZE9AAXAA9GxCeA9cB5abPGp6abmVkF2umH/kXg\nSknbydrUV3UmJDMza8XU5pu8JSI2ABvS9NPAyZ0PyczMWuGRomZmNeGEbmZWE07oZmY14YRuZlYT\nTuhmZjUxrl4uNjH0LL+36hDMrAK+QjczqwkndDOzmnBCNzOrCSd0M7OacEI3M6sJJ3Qzs5pwQjcz\nqwkndDOzmvDAIpvU0pO4XgZeB/ZFRK+kw4HbgB5gJ/BPEfHnqmI0y8tX6GbwkYiYHxG9aX458EBE\nHAc8kObNup4Tutn+FgOr0/Rq4JwKYzHLzQndJrsAfi5pk6RladlRETEAkF6PHP4mScsk9UnqGxwc\nLDFcs9G5Dd0mu1MjYrekI4F1kp7M86aIWAmsBOjt7Y0iAzTLy1foNqlFxO70uhe4i+w5uXskzQJI\nr3uri9AsPyd0m7QkHSLpsKFp4B+BLcBaYEnabAlwdzURmo2Pm1xsMjsKuEsSZP8Wbo6In0l6BLhd\n0lLgGeD8CmM0y80J3SatiHgaeN8Iy58HFpQfkVl73ORiZlYTTuhmZjXRNKFLmiNpvaR+SVslXZ6W\nHy5pnaRt6XV68eGamdlo8lyh7wM+HxEnAKcAn5M0Dw+PNjPrKk0TekQMRMSjafploB84Gg+PNjPr\nKuNqQ5fUA5wIbCTH8Oj0Hg+RNjMrQe6ELulQ4A7gioh4Ke/7ImJlRPRGRO/MmTNbidHMzHLI1Q9d\n0gFkyfymiLgzLd4jaVZEDHh49OTVs/zelt63c8XZHY7EzPL0chGwCuiPiKsbVnl4tJlZF8lzhX4q\ncBHwhKTNadmXgRV4eLSZWddomtAj4iFAo6z28Ggzsy7hkaJmZjXhhG5mVhNO6GZmNeGEbmZWE07o\nZmY14YRuZlYTfmKRmVkHVTl62lfoZmY14YRuZlYTTuhmZjXhhG5mVhNO6GZmNeGEbmZWE+62aJXw\ngzHMOs9X6GZmNeGEbmZWE25y6WKtNkuY2eTkK3Qzs5pwQjczq4muaXJxrwczs/b4Ct3MrCac0M3M\nasIJ3cysJpzQzcxqoq2ELumjkp6StF3S8k4FZVY1122biFpO6JKmAN8DzgLmARdKmtepwMyq4rpt\nE1U73RZPBrZHxNMAkm4FFgO/6URgdeIRnxOO67ZNSO0k9KOBZxvmdwHvby8cs67Qsbrt8RVWpnYS\nukZYFvttJC0DlqXZVyQ9NcZnzgCeG1cQV41n66bGXX6Hufwm5Tc53+/uUBxN6/Y46nVLx7TD9XpI\n1ee3UdX/1luOoyi6asxYctXtdhL6LmBOw/xsYPfwjSJiJbAyzwdK6ouI3jZiaovLn9zlN2hat/PW\n6y7aJ8fSxXFAZ2Jpp5fLI8Bxko6VdCBwAbC2nWDMuoTrtk1ILV+hR8Q+SZcB9wNTgBsjYmvHIjOr\niOu2TVRt3ZwrIu4D7utQLJCzaaZALn9yl/+mDtbtrtknHMtIuiUO6EAsitjvd0wzM5uAPPTfzKwm\nuiahlz3UWtIcSesl9UvaKunytPxwSeskbUuv0wuMYYqkxyTdk+aPlbQxlX1b+kGuqLKnSVoj6cl0\nDD5Q8r7/WzruWyTdIungMve/KJLOT/v1hqRReyyUUd/znk9Jr0vanP469uNvs32UdFA6z9vTee/p\nVNktxHKxpMGG43BJQXHcKGmvpC2jrJek76Q4H5d00rgKiIjK/8h+eNoBzAUOBH4NzCu4zFnASWn6\nMOC3ZMO8vwEsT8uXA1cVGMOVwM3APWn+duCCNH0d8JkCy14NXJKmDwSmlbXvZAN3fge8rWG/Ly5z\n/ws8ricAxwMbgN5Rtimlvuc9n8ArBZTddB+BzwLXpekLgNsKOid5YrkY+G4J9ePDwEnAllHWLwR+\nSjYW4hRg43g+v1uu0N8cah0RrwFDQ60LExEDEfFomn4Z6CdLNIvJkh3p9Zwiypc0GzgbuCHNCzgd\nWFNC2e8gq1irACLitYh4gZL2PZkKvE3SVODtwAAl7X+RIqI/IsYaPAfl1fcyz+dwefaxMb41wIL0\n76CKWEoREb8E/jTGJouBH0bmYWCapFl5P79bEvpIQ62PLqvw9FXvRGAjcFREDECW9IEjCyr2GuAL\nwBtp/gjghYjYl+aLPAZzgUHg+6nJ5wZJh1DSvkfEH4BvAs+QJfIXgU2Ut/9VK6u+5z2fB0vqk/Sw\npE4l/Tz7+OY26by/SPbvoNPyHu+Pp2aONZLmjLC+DG3VjW5J6LluI1BIwdKhwB3AFRHxUkllfgzY\nGxGbGhePsGlRx2Aq2de+ayPiROD/yL6SlyK15S4GjgXeBRxCdmfD4bqyC5akX6S2/+F/ea/6Onau\nOxALwDGRjVD8Z+AaSe9pJZbhoY2wbPg+llXn85TzE6AnIv4B+AVvfXMoW1vHpFseEp3rNgKdJukA\nsmR+U0TcmRbvkTQrIgbSV529BRR9KrBI0kLgYOAdZFfs0yRNTVcrRR6DXcCuiNiY5teQJfQy9h3g\nDOB3ETEIIOlO4IOUt/9tiYgz2vyIjtX3sWKRlOt8RsTu9Pq0pA1k31Z3tBJPgzz7OLTNrtT09k7G\nbo4oLJaIeL5h9nqgmDvHNNdW3eiWK/TSh1qntrpVQH9EXN2wai2wJE0vAe7udNkR8aWImB0RPWT7\n+mBEfAJYD5xXZNmp/D8Cz0o6Pi1aQHZr2ML3PXkGOEXS29N5GCq/lP3vAmXV96bnU9J0SQel6Rlk\nFxuduE1wnn1sjO88sn8HRVyhN41lWDv1IrLf1KqwFvhk6u1yCvDiULNZLkX/qjuOX38XkvU02QF8\npYTyPkT2VeZxYHP6W0jWhvcAsC29Hl5wHKfxVi+XucCvgO3Aj4CDCix3PtCX9v/HwPQy9x34KvAk\nsAX4H+CgMve/wP06l+wq61VgD3B/Wv4u4L6G7Qqv76OdT6AXuCFNfxB4gqznxxPA0g6Wv98+Al8D\nFqXpg9N53p7O+9wCz0uzWL4ObE3HYT3w3oLiuIXsd6O/pnqyFLgUuDStF9nDVXak8zFiT6nR/jxS\n1MysJrqlycXMzNrkhG5mVhNO6GZmNeGEbmZWE07oZmY14YRuZlYTTuhmZjXhhG5mVhP/D+oTluY+\nBYpzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc88ec90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(221)\n",
    "plt.hist(data_x['survival'])\n",
    "plt.subplot(222)\n",
    "plt.hist(data_x['still-alive'])\n",
    "plt.subplot(223)\n",
    "plt.hist(data_x['age-at-heart-attack'])\n",
    "plt.subplot(224)\n",
    "plt.hist(data_x['pericardial-effusion'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Variable survival is normally distributed and variables still-alive, age-at-heart-attack,pericardial-effusion are skewed to the right "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  1.,   0.,   0.,   0.,   0.,  84.,  23.,  12.,   9.,   2.]),\n",
       " array([-1. , -0.6, -0.2,  0.2,  0.6,  1. ,  1.4,  1.8,  2.2,  2.6,  3. ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\noEvSQPw/Aqxs+M0JQgMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xcdea5d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(221)\n",
    "plt.hist(data_x['fractional-shortening'])\n",
    "plt.subplot(222)\n",
    "plt.hist(data_x['epss'])\n",
    "plt.subplot(223)\n",
    "plt.hist(data_x['lvdd'])\n",
    "plt.subplot(224)\n",
    "plt.hist(data_x['wall-motion-index'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Variable epss is normally distributed and variables fractional-shortening,       vdd,wall-motion-index are skewed to the right "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Mungling and Data Reduction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_y = data_y.to_frame(name='alive-at-1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_x1 = data_x[((data_x['survival'] > -0.1) & (data_x['still-alive'] > -0.1)) & (data_x['age-at-heart-attack'] > -0.1) & (data_x['pericardial-effusion'] > -0.1) & (data_x['fractional-shortening'] > -0.1) & (data_x['epss'] > -0.1) & (data_x['lvdd'] > -0.1) & (data_x['wall-motion-index'] > -0.1)]\n",
    "data_y1 = data_y[data_y['alive-at-1'] > -0.1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alive-at-1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   alive-at-1\n",
       "0         1.0\n",
       "1         1.0\n",
       "2         1.0\n",
       "5         1.0\n",
       "8         1.0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_y1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_y1 = data_y1.reset_index()\n",
    "del data_y1[\"index\"]\n",
    "del data_y1[\"level_0\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alive-at-1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   alive-at-1\n",
       "0         1.0\n",
       "1         1.0\n",
       "2         1.0\n",
       "3         1.0\n",
       "4         1.0"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_y1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(73, 1)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_y1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(106, 8)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_x1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survival</th>\n",
       "      <th>still-alive</th>\n",
       "      <th>age-at-heart-attack</th>\n",
       "      <th>pericardial-effusion</th>\n",
       "      <th>fractional-shortening</th>\n",
       "      <th>epss</th>\n",
       "      <th>lvdd</th>\n",
       "      <th>wall-motion-index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.225</td>\n",
       "      <td>12.200</td>\n",
       "      <td>5.20</td>\n",
       "      <td>2.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.220</td>\n",
       "      <td>21.700</td>\n",
       "      <td>4.85</td>\n",
       "      <td>1.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.50</td>\n",
       "      <td>1.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.230</td>\n",
       "      <td>12.733</td>\n",
       "      <td>6.06</td>\n",
       "      <td>1.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.50</td>\n",
       "      <td>1.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.130</td>\n",
       "      <td>16.400</td>\n",
       "      <td>4.96</td>\n",
       "      <td>1.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.50</td>\n",
       "      <td>1.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.155</td>\n",
       "      <td>11.300</td>\n",
       "      <td>5.16</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survival  still-alive  age-at-heart-attack  pericardial-effusion  \\\n",
       "0      0.25          1.0                 86.0                   0.0   \n",
       "1      0.25          1.0                 68.0                   0.0   \n",
       "2      0.50          1.0                 73.0                   0.0   \n",
       "3      0.50          1.0                 59.0                   0.0   \n",
       "4      0.50          1.0                 67.0                   0.0   \n",
       "\n",
       "   fractional-shortening    epss  lvdd  wall-motion-index  \n",
       "0                  0.225  12.200  5.20               2.18  \n",
       "1                  0.220  21.700  4.85               1.15  \n",
       "2                  0.230  12.733  6.06               1.50  \n",
       "3                  0.130  16.400  4.96               1.37  \n",
       "4                  0.155  11.300  5.16               1.00  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_x1 = data_x1.reset_index()\n",
    "del data_x1[\"index\"]\n",
    "data_x1.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xe2efbd0>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdf2bed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAD8CAYAAAB6paOMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEEhS4QwCSSqcQSBJhfv/wY2PCHw+Dt8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe130cd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure() \n",
    "data_y1.plot.hist(alpha=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Validating the cleaned dataset with benchmark accuracy obtained"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "nbclf = naive_bayes.GaussianNB()\n",
    "data_x1 = data_x1.drop(data_x1.index[73:])\n",
    "data_x1_train,data_x1_test,data_y1_train,data_y1_test = train_test_split(data_x1,data_y1,train_size=0.8,test_size=0.2,random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(58, 8)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_x1_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15, 8)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_x1_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(58, 1)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_y1_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15, 1)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_y1_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Mean accuracy with 10 fold cross validation on Naive Bayes with treated data: ', 0.91904761904761911)\n"
     ]
    }
   ],
   "source": [
    "nbclf = nbclf.fit(data_x1_train, data_y1_train)\n",
    "nbpreds_test = nbclf.predict(data_x1_test)\n",
    "nb_eval_result1 = cross_val_score(nbclf, data_x1, data_y1, cv=10, scoring='accuracy')\n",
    "print('Mean accuracy with 10 fold cross validation on Naive Bayes with treated data: ',nb_eval_result1.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Core Model Building with SVM Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "def funct_svm(kernal_type,xTrain,yTrain,xTest,yTest):\n",
    "    svm_obj=SVC(kernel=kernal_type)\n",
    "    svm_obj.fit(xTrain,yTrain)\n",
    "    yPredicted=svm_obj.predict(xTest)\n",
    "    print('Accuracy Score of',kernal_type,'Kernal SVM is:',metrics.accuracy_score(yTest,yPredicted))\n",
    "    return metrics.accuracy_score(yTest,yPredicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The slowest run took 46.08 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
      "10000000 loops, best of 3: 52.4 ns per loop\n",
      "('Accuracy Score of', 'linear', 'Kernal SVM is:', 1.0)\n"
     ]
    }
   ],
   "source": [
    "%timeit 10\n",
    "PN_linear_result = funct_svm('linear',data_x1_train,data_y1_train,data_x1_test,data_y1_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The slowest run took 56.25 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
      "10000000 loops, best of 3: 53.7 ns per loop\n",
      "('Accuracy Score of', 'rbf', 'Kernal SVM is:', 0.93333333333333335)\n"
     ]
    }
   ],
   "source": [
    "%timeit 10\n",
    "PN_rbf_result = funct_svm('rbf',data_x1_train,data_y1_train,data_x1_test,data_y1_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000000 loops, best of 3: 53.2 ns per loop\n",
      "('Accuracy Score of', 'poly', 'Kernal SVM is:', 0.93333333333333335)\n"
     ]
    }
   ],
   "source": [
    "%timeit 10\n",
    "PN_poly_result = funct_svm('poly',data_x1_train,data_y1_train,data_x1_test,data_y1_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The slowest run took 24.04 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
      "10000000 loops, best of 3: 50.2 ns per loop\n",
      "('Accuracy Score of', 'sigmoid', 'Kernal SVM is:', 0.80000000000000004)\n"
     ]
    }
   ],
   "source": [
    "%timeit 10\n",
    "PN_sigmoid_result = funct_svm('sigmoid',data_x1_train,data_y1_train,data_x1_test,data_y1_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Parameter tuning on the best Kernal for SVM with 5-fold cross validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "def funct_tune_svm(kernal_type,margin_val,xData,yData,k,eval_param):\n",
    "    if(kernal_type=='poly'):\n",
    "        svm_obj=SVC(kernel=kernal_type,degree=margin_val) \n",
    "    eval_result = cross_val_score(svm_obj, xData, yData, cv=k, scoring=eval_param)\n",
    "    return eval_result.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "accu_list = list()\n",
    "for c in np.arange(0.1,10,1):\n",
    "    result = funct_tune_svm('poly',c,data_x1,data_y1,5,'accuracy')\n",
    "    accu_list.append(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.1,  1.1,  2.1,  3.1,  4.1,  5.1,  6.1,  7.1,  8.1,  9.1])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(0.1,10,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,u'Cross-Validated Accuracy')"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAEKCAYAAADjDHn2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nZMnCGGNMRJYsjDHGRGTJwhhjTET/H3CLcO8GC3eDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe367e30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "C_values=list(np.arange(0.1,10,1))\n",
    "# plot the value of C for SVM (x-axis) versus the cross-validated accuracy (y-axis)\n",
    "plt.plot(C_values,accu_list)\n",
    "plt.xticks(np.arange(0.1,10,1))\n",
    "plt.xlabel('Value of C for SVC')\n",
    "plt.ylabel('Cross-Validated Accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Parameter Degree</th>\n",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.671795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.1</td>\n",
       "      <td>0.902564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.1</td>\n",
       "      <td>0.891282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.1</td>\n",
       "      <td>0.877949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.1</td>\n",
       "      <td>0.851282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.1</td>\n",
       "      <td>0.835897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6.1</td>\n",
       "      <td>0.851282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7.1</td>\n",
       "      <td>0.835897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8.1</td>\n",
       "      <td>0.835897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9.1</td>\n",
       "      <td>0.820513</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Parameter Degree  Accuracy\n",
       "0               0.1  0.671795\n",
       "1               1.1  0.902564\n",
       "2               2.1  0.891282\n",
       "3               3.1  0.877949\n",
       "4               4.1  0.851282\n",
       "5               5.1  0.835897\n",
       "6               6.1  0.851282\n",
       "7               7.1  0.835897\n",
       "8               8.1  0.835897\n",
       "9               9.1  0.820513"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuning_poly_svm = pd.DataFrame(columns=['Parameter Degree', 'Accuracy'])\n",
    "tuning_poly_svm['Parameter Degree'] = np.arange(0.1,10,1)\n",
    "tuning_poly_svm['Accuracy'] = accu_list\n",
    "tuning_poly_svm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization of kernal Margin and boundries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0xe875710>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nmPsoAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe782070>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data_x1['survival'],data_x1['still-alive'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0xe9dc770>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/wELJ5JLQuZ1QgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xea6eeb0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data_x1['fractional-shortening'],data_x1['still-alive'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizing the margin modeled "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZwAAAEWCAYAAABSaiGHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTvH/AY+LSsG/2eUmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe33b250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = data_x1[['survival','still-alive']].copy()\n",
    "X = np.array(X)\n",
    "y = np.array(data_y1)\n",
    "\n",
    "# fit the model, don't regularize for illustration purposes\n",
    "clf = SVC(kernel='poly', degree=1.1, gamma = 0.05,C=1.6)\n",
    "clf.fit(X, y)\n",
    "\n",
    "# title for the plots\n",
    "title = ('SVC with poly kernel(with degree=1.1 & gamma=0.05 & C=1.6)')\n",
    "\n",
    "plt.scatter(X[:,0], X[:,1], c='y', s=30, cmap=plt.cm.Paired)\n",
    "\n",
    "# plot the decision function\n",
    "ax = plt.gca()\n",
    "xlim = ax.get_xlim()\n",
    "ylim = ax.get_ylim()\n",
    "\n",
    "# create grid to evaluate model\n",
    "xx = np.linspace(xlim[0], xlim[1], 30)\n",
    "yy = np.linspace(ylim[0], ylim[1], 30)\n",
    "YY, XX = np.meshgrid(yy, xx)\n",
    "xy = np.vstack([XX.ravel(), YY.ravel()]).T\n",
    "Z = clf.decision_function(xy).reshape(XX.shape)\n",
    "\n",
    "# plot decision boundary and margins\n",
    "ax.contour(XX, YY, Z, colors='k', levels=[-1, 0, 1], alpha=0.5,\n",
    "           linestyles=['--', '-', '--'])\n",
    "# plot support vectors\n",
    "ax.scatter(clf.support_vectors_[:, 0], clf.support_vectors_[:, 1], s=100,\n",
    "           linewidth=1, facecolors='none')\n",
    "ax.set_xlabel('survival')\n",
    "ax.set_ylabel('still-alive')\n",
    "ax.set_title(title)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Final Model Detail:\\n', SVC(C=1.6, cache_size=200, class_weight=None, coef0=0.0,\n",
      "  decision_function_shape='ovr', degree=1, gamma=0.005, kernel='poly',\n",
      "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
      "  tol=0.001, verbose=False))\n",
      "('\\nAccuracy obtained from final model with 10 fold CV:\\n', 0.97083333333333344)\n",
      "('\\nROC Computed Area Under Curve:\\n', 1.0)\n"
     ]
    }
   ],
   "source": [
    "final_model = SVC(kernel='poly', C=1.6, gamma=0.005, degree=1)\n",
    "print('Final Model Detail:\\n',final_model)\n",
    "final_model_score = final_model.fit(data_x1_train, data_y1_train).decision_function(data_x1_test)\n",
    "# CV Accuracy \n",
    "final_eval_result = cross_val_score(final_model, data_x1, data_y1, cv=10, scoring='accuracy')\n",
    "print('\\nAccuracy obtained from final model with 10 fold CV:\\n',final_eval_result.mean())\n",
    "# ROC measure\n",
    "fpr, tpr, _ = roc_curve(data_y1_test,final_model_score)\n",
    "roc_auc= auc(fpr, tpr)\n",
    "print('\\nROC Computed Area Under Curve:\\n',roc_auc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYoAAAEWCAYAAAB42tAoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xeb0dc30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "lw = 2\n",
    "plt.plot(fpr, tpr, color='darkorange',lw=lw, label='ROC curve (area = %0.2f)' % roc_auc)\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver operating characteristic for Best SVM model')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Personal idea"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "col_name =data_y1.columns[0]\n",
    "data_y1=data_y1.rename(columns = {col_name:'DoA'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "Person = []\n",
    "for row in data_y1['DoA']:\n",
    "    if row == 1.0:\n",
    "        Person.append('Alive')\n",
    "    else:\n",
    "        Person.append('Dead')\n",
    "        \n",
    "        \n",
    "data_y1['Person'] = Person   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DoA</th>\n",
       "      <th>Person</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Alive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Alive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Alive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Alive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Alive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Alive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Alive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Alive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Alive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Alive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Alive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Alive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Alive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Alive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Alive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Alive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Alive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Alive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Alive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Alive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Alive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Alive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Alive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.0</td>\n",
       "      <td>Dead</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.0</td>\n",
       "      <td>Dead</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.0</td>\n",
       "      <td>Dead</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.0</td>\n",
       "      <td>Dead</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.0</td>\n",
       "      <td>Dead</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.0</td>\n",
       "      <td>Dead</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.0</td>\n",
       "      <td>Dead</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    DoA Person\n",
       "0   1.0  Alive\n",
       "1   1.0  Alive\n",
       "2   1.0  Alive\n",
       "3   1.0  Alive\n",
       "4   1.0  Alive\n",
       "5   1.0  Alive\n",
       "6   1.0  Alive\n",
       "7   1.0  Alive\n",
       "8   1.0  Alive\n",
       "9   1.0  Alive\n",
       "10  1.0  Alive\n",
       "11  1.0  Alive\n",
       "12  1.0  Alive\n",
       "13  1.0  Alive\n",
       "14  1.0  Alive\n",
       "15  1.0  Alive\n",
       "16  1.0  Alive\n",
       "17  1.0  Alive\n",
       "18  1.0  Alive\n",
       "19  1.0  Alive\n",
       "20  1.0  Alive\n",
       "21  1.0  Alive\n",
       "22  1.0  Alive\n",
       "23  0.0   Dead\n",
       "24  0.0   Dead\n",
       "25  0.0   Dead\n",
       "26  0.0   Dead\n",
       "27  0.0   Dead\n",
       "28  0.0   Dead\n",
       "29  0.0   Dead"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_y1.head(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors = 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=1, n_neighbors=5, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.fit(data_x1_train, data_y1_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.score(data_x1_test,data_y1_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0.0: 'Dead', 1.0: 'Alive'}"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Result =  dict(zip(data_y1.DoA.unique() , data_y1.Person.unique()))\n",
    "Result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Let's test the Prediction Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Result_after_one_year = knn.predict([[0.50,1.0,59.0,0.0,0.130,16.400,4.96,1.37]])\n",
    "Result_after_one_year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Alive'"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Result_after_one_year = int(Result_after_one_year)\n",
    "Result[Result_after_one_year]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.])"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Result_after_one_year = knn.predict([[19.00,0.0,46.000,0.0,0.340,0.000,5.090,1.140]])\n",
    "Result_after_one_year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Dead'"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Result_after_one_year = int(Result_after_one_year)\n",
    "Result[Result_after_one_year]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Final Result Graph "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0xef424f0>"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEcCAYAAADKlrO6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nBGZmBedEYGZWcE4EZmYF9/8B6cUGCBKSohIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xef42050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.Series(data_y1.DoA).value_counts().sort_values().plot(kind='bar',color='gr')\n",
    "plt.title(\"The Bar Graph of people diagnose with Heart Attack\")\n",
    "plt.ylabel('No of People')\n",
    "plt.xlabel('Number of People Alive/dead after one year')\n",
    "\n",
    "NA = mpatches.Patch(color='green', label='Alive')\n",
    "EU = mpatches.Patch(color='red', label='Dead')\n",
    "plt.legend(handles=[NA,EU,], loc=2)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}