Binary Classification of Heart Disease(Logistic Regression and SGD classifier).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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>ChestPainType</th>\n",
       "      <th>RestingBP</th>\n",
       "      <th>Cholesterol</th>\n",
       "      <th>FastingBS</th>\n",
       "      <th>RestingECG</th>\n",
       "      <th>MaxHR</th>\n",
       "      <th>ExerciseAngina</th>\n",
       "      <th>Oldpeak</th>\n",
       "      <th>ST_Slope</th>\n",
       "      <th>HeartDisease</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40</td>\n",
       "      <td>M</td>\n",
       "      <td>ATA</td>\n",
       "      <td>140</td>\n",
       "      <td>289</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>172</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Up</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49</td>\n",
       "      <td>F</td>\n",
       "      <td>NAP</td>\n",
       "      <td>160</td>\n",
       "      <td>180</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>156</td>\n",
       "      <td>N</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Flat</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>M</td>\n",
       "      <td>ATA</td>\n",
       "      <td>130</td>\n",
       "      <td>283</td>\n",
       "      <td>0</td>\n",
       "      <td>ST</td>\n",
       "      <td>98</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Up</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>48</td>\n",
       "      <td>F</td>\n",
       "      <td>ASY</td>\n",
       "      <td>138</td>\n",
       "      <td>214</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>108</td>\n",
       "      <td>Y</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Flat</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>54</td>\n",
       "      <td>M</td>\n",
       "      <td>NAP</td>\n",
       "      <td>150</td>\n",
       "      <td>195</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>122</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Up</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age Sex ChestPainType  RestingBP  Cholesterol  FastingBS RestingECG  MaxHR  \\\n",
       "0   40   M           ATA        140          289          0     Normal    172   \n",
       "1   49   F           NAP        160          180          0     Normal    156   \n",
       "2   37   M           ATA        130          283          0         ST     98   \n",
       "3   48   F           ASY        138          214          0     Normal    108   \n",
       "4   54   M           NAP        150          195          0     Normal    122   \n",
       "\n",
       "  ExerciseAngina  Oldpeak ST_Slope  HeartDisease  \n",
       "0              N      0.0       Up             0  \n",
       "1              N      1.0     Flat             1  \n",
       "2              N      0.0       Up             0  \n",
       "3              Y      1.5     Flat             1  \n",
       "4              N      0.0       Up             0  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "data = pd.read_csv(\"heart.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>RestingBP</th>\n",
       "      <th>Cholesterol</th>\n",
       "      <th>FastingBS</th>\n",
       "      <th>MaxHR</th>\n",
       "      <th>Oldpeak</th>\n",
       "      <th>HeartDisease</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>918.000000</td>\n",
       "      <td>918.000000</td>\n",
       "      <td>918.000000</td>\n",
       "      <td>918.000000</td>\n",
       "      <td>918.000000</td>\n",
       "      <td>918.000000</td>\n",
       "      <td>918.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>53.510893</td>\n",
       "      <td>132.396514</td>\n",
       "      <td>198.799564</td>\n",
       "      <td>0.233115</td>\n",
       "      <td>136.809368</td>\n",
       "      <td>0.887364</td>\n",
       "      <td>0.553377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.432617</td>\n",
       "      <td>18.514154</td>\n",
       "      <td>109.384145</td>\n",
       "      <td>0.423046</td>\n",
       "      <td>25.460334</td>\n",
       "      <td>1.066570</td>\n",
       "      <td>0.497414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>-2.600000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>47.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>173.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>54.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>223.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>138.000000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>60.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>267.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>77.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>603.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age   RestingBP  Cholesterol   FastingBS       MaxHR  \\\n",
       "count  918.000000  918.000000   918.000000  918.000000  918.000000   \n",
       "mean    53.510893  132.396514   198.799564    0.233115  136.809368   \n",
       "std      9.432617   18.514154   109.384145    0.423046   25.460334   \n",
       "min     28.000000    0.000000     0.000000    0.000000   60.000000   \n",
       "25%     47.000000  120.000000   173.250000    0.000000  120.000000   \n",
       "50%     54.000000  130.000000   223.000000    0.000000  138.000000   \n",
       "75%     60.000000  140.000000   267.000000    0.000000  156.000000   \n",
       "max     77.000000  200.000000   603.000000    1.000000  202.000000   \n",
       "\n",
       "          Oldpeak  HeartDisease  \n",
       "count  918.000000    918.000000  \n",
       "mean     0.887364      0.553377  \n",
       "std      1.066570      0.497414  \n",
       "min     -2.600000      0.000000  \n",
       "25%      0.000000      0.000000  \n",
       "50%      0.600000      1.000000  \n",
       "75%      1.500000      1.000000  \n",
       "max      6.200000      1.000000  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========================Age=======================\n",
      "[40 49 37 48 54 39 45 58 42 38 43 60 36 44 53 52 51 56 41 32 65 35 59 50\n",
      " 47 31 46 57 55 63 66 34 33 61 29 62 28 30 74 68 72 64 69 67 73 70 77 75\n",
      " 76 71]\n",
      "\n",
      "\n",
      "\n",
      "========================Sex=======================\n",
      "['M' 'F']\n",
      "\n",
      "\n",
      "\n",
      "========================ChestPainType=======================\n",
      "['ATA' 'NAP' 'ASY' 'TA']\n",
      "\n",
      "\n",
      "\n",
      "========================RestingBP=======================\n",
      "[140 160 130 138 150 120 110 136 115 100 124 113 125 145 112 132 118 170\n",
      " 142 190 135 180 108 155 128 106  92 200 122  98 105 133  95  80 137 185\n",
      " 165 126 152 116   0 144 154 134 104 139 131 141 178 146 158 123 102  96\n",
      " 143 172 156 114 127 101 174  94 148 117 192 129 164]\n",
      "\n",
      "\n",
      "\n",
      "========================Cholesterol=======================\n",
      "[289 180 283 214 195 339 237 208 207 284 211 164 204 234 273 196 201 248\n",
      " 267 223 184 288 215 209 260 468 188 518 167 224 172 186 254 306 250 177\n",
      " 227 230 294 264 259 175 318 216 340 233 205 245 194 270 213 365 342 253\n",
      " 277 202 297 225 246 412 265 182 218 268 163 529 100 206 238 139 263 291\n",
      " 229 307 210 329 147  85 269 275 179 392 466 129 241 255 276 282 338 160\n",
      " 156 272 240 393 161 228 292 388 166 247 331 341 243 279 198 249 168 603\n",
      " 159 190 185 290 212 231 222 235 320 187 266 287 404 312 251 328 285 280\n",
      " 192 193 308 219 257 132 226 217 303 298 256 117 295 173 315 281 309 200\n",
      " 336 355 326 171 491 271 274 394 221 126 305 220 242 347 344 358 169 181\n",
      "   0 236 203 153 316 311 252 458 384 258 349 142 197 113 261 310 232 110\n",
      " 123 170 369 152 244 165 337 300 333 385 322 564 239 293 407 149 199 417\n",
      " 178 319 354 330 302 313 141 327 304 286 360 262 325 299 409 174 183 321\n",
      " 353 335 278 157 176 131]\n",
      "\n",
      "\n",
      "\n",
      "========================FastingBS=======================\n",
      "[0 1]\n",
      "\n",
      "\n",
      "\n",
      "========================RestingECG=======================\n",
      "['Normal' 'ST' 'LVH']\n",
      "\n",
      "\n",
      "\n",
      "========================MaxHR=======================\n",
      "[172 156  98 108 122 170 142 130 120  99 145 140 137 150 166 165 125 160\n",
      " 164 138 178 112 118 127 114 154 155  87 148 100 168 184 121 153 134  96\n",
      " 174 175 144  82 135 115 128 116  94 110  92 180 152 124 106 185 139 190\n",
      " 146 158 132 176 119 188 162 105  90 136 167 129 102 143 103  91 126  93\n",
      " 131 149 123 182 141  77 109 133 179 113 104  95  72  97 117  86  63 157\n",
      "  83  60  70 163  67  78  84 111  80 107 161  69  88  73 159 151 181 186\n",
      " 177 173 169 171 147  71 192 195 194 187 202]\n",
      "\n",
      "\n",
      "\n",
      "========================ExerciseAngina=======================\n",
      "['N' 'Y']\n",
      "\n",
      "\n",
      "\n",
      "========================Oldpeak=======================\n",
      "[ 0.   1.   1.5  2.   3.   4.   0.5  2.5  5.   0.8  0.7  1.4  2.1  0.4\n",
      "  0.2  1.7  2.2  0.1  1.6  1.3  0.3  1.8  2.6 -0.9  2.8 -2.6 -1.5 -0.1\n",
      "  0.9  1.1  2.4 -1.  -1.1 -0.7 -0.8  3.7  1.2 -0.5 -2.   1.9  3.5  0.6\n",
      "  3.1  2.3  3.4  3.6  4.2  3.2  5.6  3.8  2.9  6.2  4.4]\n",
      "\n",
      "\n",
      "\n",
      "========================ST_Slope=======================\n",
      "['Up' 'Flat' 'Down']\n",
      "\n",
      "\n",
      "\n",
      "========================HeartDisease=======================\n",
      "[0 1]\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for col in data:\n",
    "    print(\"========================\"+col+\"=======================\")\n",
    "    print(data[col].unique())\n",
    "    print(\"\\n\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "fig = plt.figure(figsize = (20,10))\n",
    "plt.hist(data[\"HeartDisease\"], color='seagreen')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder \n",
    "labelencoder= LabelEncoder()\n",
    "data['Sex'] = labelencoder.fit_transform(data['Sex'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['ChestPainType'] = labelencoder.fit_transform(data['ChestPainType'])\n",
    "data['RestingECG'] = labelencoder.fit_transform(data['RestingECG'])\n",
    "data['ExerciseAngina'] = labelencoder.fit_transform(data['ExerciseAngina'])\n",
    "data['ST_Slope'] = labelencoder.fit_transform(data['ST_Slope'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>ChestPainType</th>\n",
       "      <th>RestingBP</th>\n",
       "      <th>Cholesterol</th>\n",
       "      <th>FastingBS</th>\n",
       "      <th>RestingECG</th>\n",
       "      <th>MaxHR</th>\n",
       "      <th>ExerciseAngina</th>\n",
       "      <th>Oldpeak</th>\n",
       "      <th>ST_Slope</th>\n",
       "      <th>HeartDisease</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>140</td>\n",
       "      <td>289</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>160</td>\n",
       "      <td>180</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>156</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>283</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>138</td>\n",
       "      <td>214</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>108</td>\n",
       "      <td>1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>150</td>\n",
       "      <td>195</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>122</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Sex  ChestPainType  RestingBP  Cholesterol  FastingBS  RestingECG  \\\n",
       "0   40    1              1        140          289          0           1   \n",
       "1   49    0              2        160          180          0           1   \n",
       "2   37    1              1        130          283          0           2   \n",
       "3   48    0              0        138          214          0           1   \n",
       "4   54    1              2        150          195          0           1   \n",
       "\n",
       "   MaxHR  ExerciseAngina  Oldpeak  ST_Slope  HeartDisease  \n",
       "0    172               0      0.0         2             0  \n",
       "1    156               0      1.0         1             1  \n",
       "2     98               0      0.0         2             0  \n",
       "3    108               1      1.5         1             1  \n",
       "4    122               0      0.0         2             0  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>ChestPainType</th>\n",
       "      <th>RestingBP</th>\n",
       "      <th>Cholesterol</th>\n",
       "      <th>FastingBS</th>\n",
       "      <th>RestingECG</th>\n",
       "      <th>MaxHR</th>\n",
       "      <th>ExerciseAngina</th>\n",
       "      <th>Oldpeak</th>\n",
       "      <th>ST_Slope</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>140</td>\n",
       "      <td>289</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>160</td>\n",
       "      <td>180</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>156</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>283</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>138</td>\n",
       "      <td>214</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>108</td>\n",
       "      <td>1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>150</td>\n",
       "      <td>195</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>122</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Sex  ChestPainType  RestingBP  Cholesterol  FastingBS  RestingECG  \\\n",
       "0   40    1              1        140          289          0           1   \n",
       "1   49    0              2        160          180          0           1   \n",
       "2   37    1              1        130          283          0           2   \n",
       "3   48    0              0        138          214          0           1   \n",
       "4   54    1              2        150          195          0           1   \n",
       "\n",
       "   MaxHR  ExerciseAngina  Oldpeak  ST_Slope  \n",
       "0    172               0      0.0         2  \n",
       "1    156               0      1.0         1  \n",
       "2     98               0      0.0         2  \n",
       "3    108               1      1.5         1  \n",
       "4    122               0      0.0         2  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target_data = data[\"HeartDisease\"]\n",
    "data = data.drop(['HeartDisease'],axis=1)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(target_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "normalization = MinMaxScaler()\n",
    "data = normalization.fit_transform(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.2244898 , 1.        , 0.66666667, 0.6       , 0.56218905,\n",
       "        0.        , 0.5       , 0.77464789, 0.        , 0.29545455,\n",
       "        1.        ],\n",
       "       [0.34693878, 0.        , 0.33333333, 0.65      , 0.39303483,\n",
       "        0.        , 0.5       , 0.77464789, 0.        , 0.29545455,\n",
       "        1.        ],\n",
       "       [0.53061224, 1.        , 0.33333333, 0.55      , 0.34494196,\n",
       "        0.        , 0.5       , 0.57746479, 0.        , 0.29545455,\n",
       "        1.        ],\n",
       "       [0.18367347, 1.        , 0.        , 0.7       , 0.34328358,\n",
       "        0.        , 0.5       , 0.49295775, 1.        , 0.46590909,\n",
       "        0.5       ],\n",
       "       [0.40816327, 0.        , 0.33333333, 0.6       , 0.47097844,\n",
       "        0.        , 0.5       , 0.42253521, 0.        , 0.29545455,\n",
       "        1.        ]])"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[5:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(data, target_data.to_numpy(), test_size=0.23, shuffle=True, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "model = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Rahu16\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='warn', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='warn', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8597733711048159\n"
     ]
    }
   ],
   "source": [
    "print(model.score(X_train,y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8537735849056604\n"
     ]
    }
   ],
   "source": [
    "score = model.score(X_test,y_test)\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import SGDClassifier\n",
    "model2 = SGDClassifier(alpha = 0.0009, loss='log',max_iter=7000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SGDClassifier(alpha=0.0009, average=False, class_weight=None,\n",
       "              early_stopping=False, epsilon=0.1, eta0=0.0, fit_intercept=True,\n",
       "              l1_ratio=0.15, learning_rate='optimal', loss='log', max_iter=7000,\n",
       "              n_iter_no_change=5, n_jobs=None, penalty='l2', power_t=0.5,\n",
       "              random_state=None, shuffle=True, tol=0.001,\n",
       "              validation_fraction=0.1, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8555240793201133\n"
     ]
    }
   ],
   "source": [
    "print(model2.score(X_train,y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.839622641509434\n"
     ]
    }
   ],
   "source": [
    "score = model2.score(X_test,y_test)\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8555240793201133\n"
     ]
    }
   ],
   "source": [
    "model3 = SGDClassifier(alpha = 0.003, loss='log',max_iter=5000)\n",
    "model3.fit(X_train,y_train)\n",
    "print(model3.score(X_train,y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8537735849056604\n"
     ]
    }
   ],
   "source": [
    "score = model3.score(X_test,y_test)\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics\n",
    "predictions = model3.predict(X_test)\n",
    "cf_matrix = metrics.confusion_matrix(y_test, predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Rahu16\\Anaconda3\\lib\\site-packages\\statsmodels\\tools\\_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
      "  import pandas.util.testing as tm\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "plt.figure(figsize=(9,9))\n",
    "sns.heatmap(cf_matrix, annot=True, fmt=\".3f\", linewidths=.5, square = True, cmap = 'Blues_r');\n",
    "plt.ylabel('Actual label');\n",
    "plt.xlabel('Predicted label');\n",
    "all_sample_title = 'Accuracy Score: {0}'.format(score)\n",
    "plt.title(all_sample_title, size = 15);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 1 1 0 1 1 0 1 1 0 0 0 0 1 1 0 1 1 0 1 1 0 1 1 1 0 1 0 0 1 0 1 1 1 1 1\n",
      " 0 1 1 1 1 1 0 0 1 0 1 1 0 0 0 1 1 1 0 0 1 1 1 0 0 0 1 0 1 1 1 1 0 0 0 1 1\n",
      " 0 1 0 1 0 1 0 1 1 0 1 0 0 1 0 0 0 1 1 0 1 0 1 0 1 1 0 1 0 0 0 1 0 1 1 0 1\n",
      " 1 0 0 1 0 1 0 1 0 1 1 0 1 0 0 0 0 0 0 1 1 1 0 1 0 1 1 0 1 0 0 1 1 1 1 1 0\n",
      " 0 0 0 1 1 0 1 0 0 0 0 1 0 0 1 0 1 1 1 1 0 1 1 1 0 1 0 1 1 1 0 0 1 1 0 1 1\n",
      " 0 1 0 1 1 1 1 1 0 1 0 0 0 1 1 1 0 1 1 0 1 0 1 1 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "print(predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Manual Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs=[]\n",
    "inputs[0] = int(input(\"Enter Age : \"))\n",
    "inputs[1] = input(\"Enter Gender(Male/Female) : \")\n",
    "inputs[2] = input(\"Enter ChestPainType(ATA/NAP/ASY/TA) : \")\n",
    "inputs[3] = int(input(\"Enter Resting BP : \"))\n",
    "inputs[4] = int(input(\"Enter Cholesterol Level : \"))\n",
    "inputs[5] = int(input(\"Enter FastingBS : \"))\n",
    "inputs[6] = input(\"Enter Resting ECG(Normal/ST/LVH) : \")\n",
    "inputs[7] = int(input(\"Enter MaxHR : \"))\n",
    "inputs[8] = input(\"Enter ExerciseAngina(N/Y) : \")\n",
    "inputs[9] = float(input(\"Enter Oldpeak : \"))\n",
    "inputs[10]= input(\"Enter ST_Slope(Up/Flat/Down) : \")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}