AdaBoost.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import math\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('heart_disease.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 303 entries, 0 to 302\n",
      "Data columns (total 14 columns):\n",
      "age         303 non-null int64\n",
      "sex         303 non-null int64\n",
      "cp          293 non-null object\n",
      "trestbps    303 non-null int64\n",
      "chol        303 non-null int64\n",
      "fbs         303 non-null int64\n",
      "restecg     303 non-null int64\n",
      "thalach     303 non-null int64\n",
      "exang       303 non-null int64\n",
      "oldpeak     303 non-null float64\n",
      "slope       303 non-null int64\n",
      "ca          303 non-null int64\n",
      "thal        303 non-null int64\n",
      "target      303 non-null int64\n",
      "dtypes: float64(1), int64(12), object(1)\n",
      "memory usage: 33.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### age: age in years\n",
    "###### sex(1 = male; 0 = female)\n",
    "###### cp : chest pain type ( 0-2 states different types of angina and  3 means no chest pain )\n",
    "###### trestbps : resting blood pressure (in mm Hg on admission to the hospital)\n",
    "###### chol : serum cholestoral in mg/dl\n",
    "###### fbs : (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false)\n",
    "###### restec : gresting electrocardiographic results\n",
    "###### thalach : maximum heart rate achieved\n",
    "###### exang : exercise induced angina (1 = yes; 0 = no)\n",
    "###### oldpeak : ST depression induced by exercise relative to rest\n",
    "###### slope : the slope of the peak exercise ST segment\n",
    "###### ca : number of major vessels (0-3) colored by flourosopy\n",
    "###### thal: 3 = normal; 6 = fixed defect; 7 = reversable defect\n",
    "###### target: 1 or "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age          0\n",
       "sex          0\n",
       "cp          10\n",
       "trestbps     0\n",
       "chol         0\n",
       "fbs          0\n",
       "restecg      0\n",
       "thalach      0\n",
       "exang        0\n",
       "oldpeak      0\n",
       "slope        0\n",
       "ca           0\n",
       "thal         0\n",
       "target       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# We have null values in iur dataset, which we need to handle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Mehul\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:2881: UserWarning: To output multiple subplots, the figure containing the passed axes is being cleared\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
     ]
    },
    {
     "data": {
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48ZrZvzprD189kOeWJEmSlrO+mlBW1R3t/QbgC8C+wJ2TEw639xv6DVKSJEmS1EcBLsm2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X5CVD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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15,15))\n",
    "ax = fig.gca()\n",
    "df.hist(ax = ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>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>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>54.366337</td>\n",
       "      <td>0.683168</td>\n",
       "      <td>131.623762</td>\n",
       "      <td>246.264026</td>\n",
       "      <td>0.148515</td>\n",
       "      <td>0.528053</td>\n",
       "      <td>149.646865</td>\n",
       "      <td>0.326733</td>\n",
       "      <td>1.039604</td>\n",
       "      <td>1.399340</td>\n",
       "      <td>0.729373</td>\n",
       "      <td>2.313531</td>\n",
       "      <td>0.544554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.082101</td>\n",
       "      <td>0.466011</td>\n",
       "      <td>17.538143</td>\n",
       "      <td>51.830751</td>\n",
       "      <td>0.356198</td>\n",
       "      <td>0.525860</td>\n",
       "      <td>22.905161</td>\n",
       "      <td>0.469794</td>\n",
       "      <td>1.161075</td>\n",
       "      <td>0.616226</td>\n",
       "      <td>1.022606</td>\n",
       "      <td>0.612277</td>\n",
       "      <td>0.498835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>47.500000</td>\n",
       "      <td>0.000000</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>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>55.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>240.000000</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>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>61.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>274.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</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>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>77.000000</td>\n",
       "      <td>1.000000</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>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              age         sex    trestbps        chol         fbs     restecg  \\\n",
       "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
       "mean    54.366337    0.683168  131.623762  246.264026    0.148515    0.528053   \n",
       "std      9.082101    0.466011   17.538143   51.830751    0.356198    0.525860   \n",
       "min     29.000000    0.000000   94.000000  126.000000    0.000000    0.000000   \n",
       "25%     47.500000    0.000000  120.000000  211.000000    0.000000    0.000000   \n",
       "50%     55.000000    1.000000  130.000000  240.000000    0.000000    1.000000   \n",
       "75%     61.000000    1.000000  140.000000  274.500000    0.000000    1.000000   \n",
       "max     77.000000    1.000000  200.000000  564.000000    1.000000    2.000000   \n",
       "\n",
       "          thalach       exang     oldpeak       slope          ca        thal  \\\n",
       "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
       "mean   149.646865    0.326733    1.039604    1.399340    0.729373    2.313531   \n",
       "std     22.905161    0.469794    1.161075    0.616226    1.022606    0.612277   \n",
       "min     71.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%    133.500000    0.000000    0.000000    1.000000    0.000000    2.000000   \n",
       "50%    153.000000    0.000000    0.800000    1.000000    0.000000    2.000000   \n",
       "75%    166.000000    1.000000    1.600000    2.000000    1.000000    3.000000   \n",
       "max    202.000000    1.000000    6.200000    2.000000    4.000000    3.000000   \n",
       "\n",
       "           target  \n",
       "count  303.000000  \n",
       "mean     0.544554  \n",
       "std      0.498835  \n",
       "min      0.000000  \n",
       "25%      0.000000  \n",
       "50%      1.000000  \n",
       "75%      1.000000  \n",
       "max      1.000000  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### As the coulmn cp (chest pain) has missing values, we need to impute the data. \n",
    "\n",
    "###### The data is numeric and hence mean stratergy will be a suitable choice."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import Imputer\n",
    "imput = Imputer(missing_values='NaN',strategy='mean')\n",
    "df = list(imput.fit_transform(df))\n",
    "\n",
    "for i in range(303):\n",
    "    for j in range(14):\n",
    "        df[i][j] = math.ceil(df[i][j])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df=df.rename(columns={0: 'age', 1:'sex', 2:'cp', 3:'trestbps',4: 'chol',5: 'fbs',6: 'restecg',7: 'thalach',8: 'exang',9: 'oldpeak',10: 'slope',11: 'ca',12: 'thal',13:'target'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Let's find out the most important features in the dataset that will play a major role in predicting the class label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = df.iloc[:,:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y = df.iloc[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "thal        0.127732\n",
       "ca          0.117530\n",
       "thalach     0.115284\n",
       "cp          0.114049\n",
       "age         0.092933\n",
       "chol        0.086188\n",
       "trestbps    0.079302\n",
       "oldpeak     0.072846\n",
       "exang       0.065039\n",
       "slope       0.062542\n",
       "sex         0.036971\n",
       "restecg     0.019255\n",
       "fbs         0.010329\n",
       "dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model= RandomForestClassifier(n_estimators=100,random_state=0)\n",
    "model.fit(x,y)\n",
    "pd.Series(model.feature_importances_,index=x.columns).sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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GoHXSTdE4yFmTjIDlVMqDeXVwdtnzEadxVDGw1cry68jeT0iZo6jZlR/F1otJkQa59gFk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\nYII=\n",
      "text/plain": [
       "<Figure size 411.875x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.FacetGrid(df, hue=\"target\", height=5) \\\n",
    "   .map(plt.scatter, \"age\", \"thalach\") \\\n",
    "   .add_legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ERIJQQRERkSBUUEREJAgVFBERCUIFRUREgihPqmMz+wEwE2h195OSGqeoVi+E\nJ26Grc0weCRMvwFOnlPsrPLzw/Ph5af33B57JiuO/DijVi5guG+m1WpomlTPaed/NrItly6BR78E\nz90D3gmWgsmXwcxvsXhVCwuWrmNjWzsjqtPUzxjP7Im1rFhyZ2T/z95xGae9/jApdtFJGSuGzmLq\nvHsi2z+868+4f3kTne6kzJh7xijmz54Qm+Ob/ziO6o4tXeG28mEc+fX18c9dgDl1rvwxZ/iarjGX\n2wSmNPwqNpd/uf0bzHrjB4ywLWz0YTw85Ar+9gtfi80xbjke7PPPpUt49ZuncvT2l7vCm6rGcsxX\nn2fhD/430zb8K8ewhVcZxjNjPsecK77MN2+9iUve/VFX7j8acAlfvfbG+OVbyDYTavvqy9tpH2Pu\nnkzHZh8EtgE/yreg1NXVeWNjYyL5BLd6ITwyD3a274lVpOG8O3r/ytrzxQRwYJdDyvbE2r2SHYNG\nMfid9bl9DDoGtr2aE14/5iJmvnQB7Ts7u2LpihQ3jf0N5224lbTt6Nb/C5UnMmHH89he47rDmspT\nOWHHb7u1f9cruXbnX7Fk1we6jfnY0G/xvndy15sdpKjwzpy+3ykbxKDyztznbuTpOcul0Dl1OKQg\nZ8ztXkaV7cqJt9kAqryDAT3m+asBH+WjHU/k5Lh+xCxGvLIoZzmunTw//6IS8fwDvEcVVb49J8dW\nG8Lhvi0nx4c5k1k8nRN/rGI6s+2p3OV7yqfh1z/Jb5sJtX31ru3U9t+kb0usoACY2bHAoyVZUG47\nCbY25cYHj4Ivrj30+RSiYXDeTZ3CtoIOyjj+vXtz4s9UzaPWtuTE3bu/+O4v3rxrGB/YcUe32MtV\nny6oj7h4nELnFKXQXDq8jHLbFZlLObnxTdRwdMOLeeUS9/wHyzEmjqUyn/x6itpmQm1fvWs7LfmC\nUvRjKGZ2tZk1mlnj5s2bi51O/rY2Fxbvqwp8v5HyiBcS4Bjye+HdnxH2epB+CpH0nCLHjCga+8pl\nuCeXS5zYHGPikcUEoreZUNtXf9lOe4miFxR3v8vd69y9rqamptjp5G/wyMLifVWB76k6LXqVepVh\nAZKBjT40SD+FSHpOkWPGbJpxubRacrnEic0x7mXFUtHxqG0m1PbVX7bTXqLoBaXPmn5DZl/s3irS\nmXhvN/bMnJADnT0+jbR7JW8NHBfdx6BjIsMbxswhXdH9hSNdkeKZMZ+j3Stz+l9TeSo997ruPobS\ns/27Xsk/d+Tu935hUF1kLjstFdn3O2WDop+7iOVS6Jw6nMgxt3tZZLzNBvBuxDyfHHBuZI4bxsyJ\nXI5Nk+ojc48UM8/tVhWZY6sm06/JAAANbklEQVQNiczxAaZHxh+tmBG9fCdflv82E2r76svbaR+k\ngnKgTp6TObA3eBRgmb994YA8ZM7Q6vGiYmPPZOXkf2YTNexyYxM1rJ08n8H1K3NfgMaeCV/5PdRd\nueddp6Wg7krGXX4nt1w4gdrqNAbUVqe55cIJzLniy6ydPD+n/5O/9jTLhl5AR/YFt8PLWDb0Ak7+\n2tM57X8zeT5HnP5pUtkd+ikzLp4ymvfVPxGZY2XDG7SVD8OzL/LumTOrBt3YEv3cXbrkoOe0avI/\ns9wmdBtzuU3gsJvfjMzlyIZXufvIL9C8axi73GjeNYy7j/wCH/2H+yJzHHf5nZHLsaCzvCKef8ae\nyWENrWyqGtstx01VYzmq4WUeHXMtLZ7JscWH8eiYa5l704Pcnr6mW+63p69h9vU/jV6+M7+V/zYT\navvqy9tpH5TkWV73A2cBw4DXgBvd/d/39Zg+dVBeRKQwJX9QPrHvobj73KT6FhGR3ke7vEREJAgV\nFBERCUIFRUREglBBERGRIFRQREQkCBUUEREJQgVFRESCUEEREZEgVFBERCQIFRQREQlCBUVERIJQ\nQRERkSBUUEREJAgVFBERCUIFRUREglBBERGRIFRQREQkCBUUEREJQgVFRESCUEEREZEgVFBERCQI\nFRQREQlCBUVERIJQQRERkSBUUEREJAgVFBERCUIFRUREglBBERGRIFRQREQkCBUUEREJojzJzs3s\nHODbQAr4vrvfmshAqxfCEzfD1mYYPBKm3wAnz0lkqHzGXbHkTkatXMBw30yr1dA0qZ7Tzv9sYbk/\n+iV47h7wTrAUTL4MZn4Lfng+vPz0nsePPRMuXcLiVS0sWLqOjW3tjKhOUz9jPLMn1rLzpuGU+3Zw\nwKDDqqi4sRW+ewZs+f2efoa9Hz745chc3vzHcVR3bOlq2lY+jCO/vp7XvjOD4a8v6+q7degUjvr8\n0tgc19/9WcZsWEjKd9FpZWwYM4dxl98ZG49cBhC9XGKW1x8WTOeEbY1dqbwwqI731T8RPf9rlsfO\ntZDn6dmX3uC01x8mxS46KWPF0FlMnXcPz95xWWT8sX/6DGe/+4uu+JMDzuWj/3Bf7JyuX7yG+5c3\n0elOyoy5Z4xi/uwJse2jlu8bQyflv45C/DodatuL6geKs13HiNvGZA9z92Q6NksBfwA+AjQDK4C5\n7v7buMfU1dV5Y2Nj3N3RVi+ER+bBzvY9sYo0nHdHsitfzLjrR8xixCuLSNuOrnC7V7J28vzcDTYu\n95Gnd39B3m3QMbDt1Zzwa0OncFbrF2nf2dkVS1ekWJW6hCrfjtmetu7gludH04o02zpSDNy1LaeP\nHZai0jtz4+WHU9X5dk5X71YOJ729Naf9pqqxHL395Zz4W4PGMfid9flkmSkGexeHrDdTw6ju2JLT\n93ar4jC257TvwEi557R/p2wQg8o783qePPu/nn28ZKM4zpty4i/aSI735px4U2o0o3f9MSfHZ4dc\nwNyNn8qJ3z/iAaa+8VBOfOvAcRyxbX1O/52UUW67umKx6yiZYnLSc9fnrNMbj72QcRsfPvhtL2o7\nSFVmEt218+D6DmTxqhauW7QmZxu75cIJhRQV23+Tvi3JgjIVaHD3Gdnb1wG4+y1xjzmggnLbSbC1\nKTc+eBR8cW1hfQUYt4MyytmVE99EDUc3vJhXH4VyYOx7P8mJv1z16W4vJHu3z3fN9h4vjvuNx/Qd\nGy+wn0IUnHuB8WLk0uFlHL/93pz4i1UXdysQXf2Q/3KMXEeBTQ3HczSbc3OJWdcL3vYK2Q6S3q5j\nTLv1SVra2nPitdVpnrn27Hy7KfmCkuQxlFpg77WkORvrxsyuNrNGM2vcvDl3pd2vrc2FxUOJ6T/l\nERsYMNy35AZD5Vjoe4Jk3kPsu+/elGMflop6Ad9HvJDlGLmOAsM9eruMW9cLXq8LaZ/0dh1jY0Qx\n2Ve8v0qyoMS9Ue0ecL/L3evcva6mpqbwUQaPLCweSkz/nRa9SFttWN59FKzQ9z1Jvk+K67s35diH\ndcZssnHxQpZj5DoKtFr0dhm3rhe8XhfSPuntOsaI6nRB8f4qyYLSDIza6/ZIYGPwUabfkNm3ureK\n9J6DekmJGXfDmDm0e2W3cLtX0jSpPu8+GHtm9JiDjokMtw6dQroi1S2Wrkix3arouUfTvYA3rRVp\n3ikbFNnHDktFx1OHR3bVXjk8sv2mqrGR8bcGjss3y8wxlAht5cMi+95uVZHtO80i279TNijv58mz\nj+nZx0s2KjL+oo2MjDelRkfmuGLorILibw0cF9l/h3ff9GPXUaBpUn3kOr1hzJww217UdpCqhLKK\ng+87kPoZ4yO3sfoZ44uST2+VZEFZAZxgZmPNrBK4CFgSfJST52QO1A0eBVjm76E4cBcz7rjL72Tt\n5PlsooZdbmyiJvZgZ2zuly6BuiszZ+pA5m/dlfCV3+e+iI09k6M+v5RbLpxAbXUaI7Nf95YLJ3BY\nQysdZVVdL3IOdJRVUdawNfdFeNj74cLv5eQy6MaWrhfm3f+1lQ+jquENWodN6dZ367ApVH29OTLH\nAV99gZeOvYgOyjIvaJTx0rEXccxXn4+MD65fGb0MomLXLI+MH/n19bwwqK5b7i8MquOwhtbI+Zc3\ntEXOddCNLXk/T1Z3JcuGXkCHl3W9cC8begHjGtZGxk9o+A2PD5jZLf74gJmMvmFN5JymzruHi6eM\nJpU9wJIy4+Ipo5k6757I9oPrV0Yu31WTb81vHQVOO/+zkev0uMvvDLPtRW0Hs/4FZv/rod+uY8ye\nWBu5jeksr+4SOygPYGbnAreTOW34B+7+jX21P6CD8iIifUPJ78hN9Hso7v4L4BdJjiEiIr2Dvikv\nIiJBqKCIiEgQKigiIhKECoqIiAShgiIiIkGooIiISBAqKCIiEoQKioiIBKGCIiIiQaigiIhIECoo\nIiISRKIXhyyUmW0GNiTQ9TAg+teDSk9/mWt/mSdorqVii7ufU+wkktSrCkpSzKzR3euKnceh0F/m\n2l/mCZqr9B3a5SUiIkGooIiISBD9paDcVewEDqH+Mtf+Mk/QXKWP6BfHUEREJHn95ROKiIgkTAVF\nRESCKLmCYmaHmdn/mNmvzew3ZnZTNj7WzJab2Qtm9h9mVlnsXEMws5SZrTKzR7O3S3Wer5jZGjN7\n3swas7EhZvZ4dq6Pm9mRxc7zYJlZtZk9aGa/N7PfmdnUEp3n+Oxzufu/t8zsC6U41/6k5AoKsB04\n291PAU4FzjGzKcA/Abe5+wnAm8CVRcwxpL8DfrfX7VKdJ8CH3P3Uvb6ncC3wRHauT2Rv93XfBn7p\n7u8HTiHz3JbcPN19Xfa5PBWYDLwLPEQJzrU/KbmC4hnbsjcrsv85cDbwYDb+Q2B2EdILysxGAh8H\nvp+9bZTgPPdhFpk5QgnM1cyOAD4I/DuAu+9w9zZKbJ4RpgPr3X0DpT/XklZyBQW6dgM9D7QCjwPr\ngTZ378g2aQZqi5VfQLcDfw/syt4eSmnOEzJvCh4zs+fM7Ops7Ch3fxUg+3d40bIL4zhgM3B3djfm\n981sIKU3z54uAu7P/rvU51rSSrKguHtn9qP0SOB04E+imh3arMIys5lAq7s/t3c4ommfnudeprn7\nJOBjwN+a2QeLnVACyoFJwL+5+0TgHUp8l0/2GN/5wAPFzkUOXkkWlN2yuwueAqYA1WZWnr1rJLCx\nWHkFMg0438xeAX5KZlfX7ZTePAFw943Zv61k9rWfDrxmZscAZP+2Fi/DIJqBZndfnr39IJkCU2rz\n3NvHgJXu/lr2dinPteSVXEExsxozq87+Ow18mMyBzf8CPpltdinwcHEyDMPdr3P3ke5+LJldBk+6\n+2cosXkCmNlAMzt897+BjwJrgSVk5gglMFd33wQ0mdn4bGg68FtKbJ49zGXP7i4o7bmWvJL7pryZ\nnUzmYF6KTMFc6O43m9lxZN7JDwFWARe7+/biZRqOmZ0FfMXdZ5biPLNzeih7sxz4ibt/w8yGAguB\n0cAfgU+5+xtFSjMIMzuVzEkWlcBLwOVk12NKaJ4AZjYAaAKOc/et2VjJPaf9SckVFBERKY6S2+Ul\nIiLFoYIiIiJBqKCIiEgQKigiIhKECoqIiAShgiIiIkGooIiISBAqKFKyzGxx9mKSv9l9QUkzu9LM\n/mBmT5nZ98zsu9l4jZn9zMxWZP+bVtzsRfoefbFRSpaZDXH3N7KX4FkBzACeIXN9rLeBJ4Ffu/s1\nZvYT4F/d/VdmNhpY6u5RFxUVkRjl+28i0mfNM7MLsv8eBfwl8PTuS3mY2QPA+7L3fxg4MfOTMgAc\nYWaHu/vbhzJhkb5MBUVKUvb6Zh8Gprr7u2b2FLCO6J8ygMzu36nu3n5oMhQpPTqGIqVqMPBmtpi8\nn8xPGAwAzjSzI7OX+P/EXu0fA67ZfSN7kUYRKYAKipSqXwLlZrYa+EdgGdACfBNYDvwnmUvDb822\nnwfUmdlqM/st8NeHPmWRvk0H5aVfMbNB7r4t+wnlIeAH7v7Q/h4nIvunTyjS3zSY2fNkfqDrZWBx\nkfMRKRn6hCIiIkHoE4qIiAShgiIiIkGooIiISBAqKCIiEoQKioiIBPH/AX2BWK0taZ/cAAAAAElF\nTkSuQmCC\n",
      "text/plain": [
       "<Figure size 411.875x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.FacetGrid(df, hue=\"target\", height=5) \\\n",
    "   .map(plt.scatter, \"age\", \"oldpeak\") \\\n",
    "   .add_legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 267.875x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.FacetGrid(df, hue=\"target\", height=3) \\\n",
    "   .map(plt.scatter, \"thal\", \"ca\") \\\n",
    "   .add_legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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+240vd/xFoIwSvQtJf/FqWbJyIx1dPatHR5cHv5wUa8cAyTdLzrL4vyQn2x4G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\n",
      "text/plain": [
       "<Figure size 411.875x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.FacetGrid(df, hue=\"target\", height=5) \\\n",
    "   .map(plt.scatter, \"oldpeak\", \"thalach\") \\\n",
    "   .add_legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQEAAADQCAYAAAAH+dTDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYJY4h4BZ4hwCZolzCJglziFglrj/B1mTPtV5W6S6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 267.875x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.FacetGrid(df, hue=\"target\", height=3) \\\n",
    "   .map(plt.scatter, \"restecg\", \"ca\") \\\n",
    "   .add_legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import precision_recall_fscore_support\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# For 'cp' column, it records chest pain type. Number 3 mean no chest pain, number 0-2 means different tyoe of angina. \n",
    "\n",
    "# To simplify it, I will group the number 0-2 together as disease positive, number 3 as disease negative"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "number=[0,1,2]\n",
    "for col in df.itertuples():\n",
    "\n",
    "    if col.cp in number:\n",
    "        df['cp'].replace(to_replace=col.cp, value=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing the accuracy when the top 8 features are used for fitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_top8 = df.loc[:,['cp','oldpeak','thal','ca','thalach','age','chol','trestbps','exang']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  0.828947368421\n",
      "\n",
      "\n",
      "Confusion Matrix:  [[26  6]\n",
      " [ 7 37]]\n",
      "\n",
      "\n",
      "Precision:  [ 0.78787879  0.86046512]\n",
      "Recall:     [ 0.8125      0.84090909]\n",
      "Fscore:     [ 0.8         0.85057471]\n",
      "Support:    [32 44]\n"
     ]
    }
   ],
   "source": [
    "x_train,x_test,y_train,y_test = train_test_split(df_top8,y,test_size=0.25,random_state=0)\n",
    "clf = AdaBoostClassifier()\n",
    "clf.fit(x_train,y_train)\n",
    "prediction = clf.predict(x_test)\n",
    "accuracy = accuracy_score(prediction,y_test)\n",
    "cm = confusion_matrix(prediction,y_test)\n",
    "prfs = precision_recall_fscore_support(prediction,y_test)\n",
    "print('Accuracy: ',accuracy)\n",
    "print('\\n')\n",
    "print('Confusion Matrix: ',cm)\n",
    "print('\\n')\n",
    "print('Precision: ', prfs[0])\n",
    "print('Recall:    ', prfs[1])\n",
    "print('Fscore:    ', prfs[2])\n",
    "print('Support:   ', prfs[3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Maybe, Standardization could hep ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = df.iloc[:,:-1]\n",
    "x_std = StandardScaler().fit_transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  0.842105263158\n",
      "\n",
      "\n",
      "Confusion Matrix:  [[28  7]\n",
      " [ 5 36]]\n",
      "\n",
      "\n",
      "Precision:  [ 0.84848485  0.8372093 ]\n",
      "Recall:     [ 0.8         0.87804878]\n",
      "Fscore:     [ 0.82352941  0.85714286]\n",
      "Support:    [35 41]\n"
     ]
    }
   ],
   "source": [
    "x_train,x_test,y_train,y_test = train_test_split(x_std,y,test_size=0.25,random_state=0)\n",
    "clf = AdaBoostClassifier()\n",
    "clf.fit(x_train,y_train)\n",
    "prediction = clf.predict(x_test)\n",
    "accuracy = accuracy_score(prediction,y_test)\n",
    "cm = confusion_matrix(prediction,y_test)\n",
    "prfs = precision_recall_fscore_support(prediction,y_test)\n",
    "print('Accuracy: ',accuracy)\n",
    "print('\\n')\n",
    "print('Confusion Matrix: ',cm)\n",
    "print('\\n')\n",
    "print('Precision: ', prfs[0])\n",
    "print('Recall:    ', prfs[1])\n",
    "print('Fscore:    ', prfs[2])\n",
    "print('Support:   ', prfs[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Not much of a difference using Standardization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Accuracy with different test sizes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>0.769737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.40</th>\n",
       "      <td>0.778689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.30</th>\n",
       "      <td>0.846154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>0.828947</td>\n",
       "    </tr>\n",
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       "      <th>0.20</th>\n",
       "      <td>0.737705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.15</th>\n",
       "      <td>0.782609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.10</th>\n",
       "      <td>0.774194</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             0\n",
       "0.50  0.769737\n",
       "0.40  0.778689\n",
       "0.30  0.846154\n",
       "0.25  0.828947\n",
       "0.20  0.737705\n",
       "0.15  0.782609\n",
       "0.10  0.774194"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testSize = [0.5,0.4,0.3,0.25,0.2,0.15,0.1]\n",
    "\n",
    "acc = []\n",
    "for i in testSize:\n",
    "    x_train,x_test,y_train,y_test = train_test_split(x_std,y,test_size=i)\n",
    "    clf = AdaBoostClassifier()\n",
    "    clf.fit(x_train,y_train)\n",
    "    prediction=clf.predict(x_test)\n",
    "    acc.append(accuracy_score(prediction,y_test))\n",
    "\n",
    "models_dataframe=pd.DataFrame(acc,index=testSize)   \n",
    "models_dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We can conclude that training size in the range 70% to 75% gives the optimal result, otherwise the data faces the issues of either under fitting or over fitting"
   ]
  }
 ],
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