Heart Disease-Solution.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('/home/sudeepgarg865660/iitr-deep-learning-spl-tf2/projects/heart.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <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",
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       "      <td>180</td>\n",
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       "      <th>3</th>\n",
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       "      <td>ASY</td>\n",
       "      <td>138</td>\n",
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       "      <td>TA</td>\n",
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       "      <td>F</td>\n",
       "      <td>ATA</td>\n",
       "      <td>130</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>LVH</td>\n",
       "      <td>174</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>NAP</td>\n",
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       "      <td>Up</td>\n",
       "      <td>0</td>\n",
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       "<p>918 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age Sex ChestPainType  RestingBP  Cholesterol  FastingBS RestingECG  \\\n",
       "0     40   M           ATA        140          289          0     Normal   \n",
       "1     49   F           NAP        160          180          0     Normal   \n",
       "2     37   M           ATA        130          283          0         ST   \n",
       "3     48   F           ASY        138          214          0     Normal   \n",
       "4     54   M           NAP        150          195          0     Normal   \n",
       "..   ...  ..           ...        ...          ...        ...        ...   \n",
       "913   45   M            TA        110          264          0     Normal   \n",
       "914   68   M           ASY        144          193          1     Normal   \n",
       "915   57   M           ASY        130          131          0     Normal   \n",
       "916   57   F           ATA        130          236          0        LVH   \n",
       "917   38   M           NAP        138          175          0     Normal   \n",
       "\n",
       "     MaxHR ExerciseAngina  Oldpeak ST_Slope  HeartDisease  \n",
       "0      172              N      0.0       Up             0  \n",
       "1      156              N      1.0     Flat             1  \n",
       "2       98              N      0.0       Up             0  \n",
       "3      108              Y      1.5     Flat             1  \n",
       "4      122              N      0.0       Up             0  \n",
       "..     ...            ...      ...      ...           ...  \n",
       "913    132              N      1.2     Flat             1  \n",
       "914    141              N      3.4     Flat             1  \n",
       "915    115              Y      1.2     Flat             1  \n",
       "916    174              N      0.0     Flat             1  \n",
       "917    173              N      0.0       Up             0  \n",
       "\n",
       "[918 rows x 12 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 918 entries, 0 to 917\n",
      "Data columns (total 12 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   Age             918 non-null    int64  \n",
      " 1   Sex             918 non-null    object \n",
      " 2   ChestPainType   918 non-null    object \n",
      " 3   RestingBP       918 non-null    int64  \n",
      " 4   Cholesterol     918 non-null    int64  \n",
      " 5   FastingBS       918 non-null    int64  \n",
      " 6   RestingECG      918 non-null    object \n",
      " 7   MaxHR           918 non-null    int64  \n",
      " 8   ExerciseAngina  918 non-null    object \n",
      " 9   Oldpeak         918 non-null    float64\n",
      " 10  ST_Slope        918 non-null    object \n",
      " 11  HeartDisease    918 non-null    int64  \n",
      "dtypes: float64(1), int64(6), object(5)\n",
      "memory usage: 86.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex               2\n",
       "ChestPainType     4\n",
       "RestingECG        3\n",
       "ExerciseAngina    2\n",
       "ST_Slope          3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.select_dtypes(exclude=\"number\").nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    508\n",
       "0    410\n",
       "Name: HeartDisease, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['HeartDisease'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "le = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Sex']=le.fit_transform(df['Sex'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ExerciseAngina']=le.fit_transform(df['ExerciseAngina'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ST_Slope']=le.fit_transform(df['ST_Slope'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    460\n",
       "2    395\n",
       "0     63\n",
       "Name: ST_Slope, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['ST_Slope'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['RestingECG']=le.fit_transform(df['RestingECG'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    552\n",
       "0    188\n",
       "2    178\n",
       "Name: RestingECG, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['RestingECG'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ChestPainType']=le.fit_transform(df['ChestPainType'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    496\n",
       "2    203\n",
       "1    173\n",
       "3     46\n",
       "Name: ChestPainType, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['ChestPainType'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop('HeartDisease',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>918 rows × 11 columns</p>\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",
       "913   45    1              3        110          264          0           1   \n",
       "914   68    1              0        144          193          1           1   \n",
       "915   57    1              0        130          131          0           1   \n",
       "916   57    0              1        130          236          0           0   \n",
       "917   38    1              2        138          175          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  \n",
       "..     ...             ...      ...       ...  \n",
       "913    132               0      1.2         1  \n",
       "914    141               0      3.4         1  \n",
       "915    115               1      1.2         1  \n",
       "916    174               0      0.0         1  \n",
       "917    173               0      0.0         2  \n",
       "\n",
       "[918 rows x 11 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['HeartDisease']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0\n",
       "1      1\n",
       "2      0\n",
       "3      1\n",
       "4      0\n",
       "      ..\n",
       "913    1\n",
       "914    1\n",
       "915    1\n",
       "916    1\n",
       "917    0\n",
       "Name: HeartDisease, Length: 918, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3,stratify=y, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder,StandardScaler\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(642, 11)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(276, 11)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(642,)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(276,)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc=StandardScaler()\n",
    "X_train=sc.fit_transform(X_train)\n",
    "X_test=sc.fit_transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.85606442,  0.52085231,  2.29813761, ..., -0.84109857,\n",
       "        -1.5632712 , -0.57786415],\n",
       "       [ 0.33134286,  0.52085231, -0.79928229, ...,  1.18892129,\n",
       "         1.0375028 , -0.57786415],\n",
       "       [ 0.33134286,  0.52085231, -0.79928229, ...,  1.18892129,\n",
       "         1.96635066, -0.57786415],\n",
       "       ...,\n",
       "       [-0.61315593,  0.52085231, -0.79928229, ...,  1.18892129,\n",
       "         0.57307887, -0.57786415],\n",
       "       [ 0.64617579, -1.91993005, -0.79928229, ..., -0.84109857,\n",
       "         1.59481152, -0.57786415],\n",
       "       [-0.92798886,  0.52085231, -0.79928229, ..., -0.84109857,\n",
       "        -0.82019291,  1.0709749 ]])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
       "    decision_function_shape='ovr', degree=3, gamma='scale', kernel='rbf',\n",
       "    max_iter=-1, probability=False, random_state=42, shrinking=True, tol=0.001,\n",
       "    verbose=False)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "classifier = SVC(kernel='rbf', random_state=42)\n",
    "classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[105  18]\n",
      " [ 11 142]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "print(cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 84.58 %\n",
      "Standard Deviation: 4.55 %\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "accuracies = cross_val_score(estimator = classifier, X = X_train, y = y_train, cv = 10)\n",
    "print(\"Accuracy: {:.2f} %\".format(accuracies.mean()*100))\n",
    "print(\"Standard Deviation: {:.2f} %\".format(accuracies.std()*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "#logistic Regression\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "log_reg = LogisticRegression()\n",
    "log_reg.fit(X_train,y_train)\n",
    "y_pred = log_reg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.855072463768116\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      0.82      0.83       123\n",
      "           1       0.86      0.88      0.87       153\n",
      "\n",
      "    accuracy                           0.86       276\n",
      "   macro avg       0.85      0.85      0.85       276\n",
      "weighted avg       0.85      0.86      0.85       276\n",
      "\n",
      "[[101  22]\n",
      " [ 18 135]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report , confusion_matrix\n",
    "print(log_reg.score(X_test,y_test))\n",
    "print(classification_report(y_test,y_pred))\n",
    "print(confusion_matrix(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score=nan,\n",
       "             estimator=KNeighborsClassifier(algorithm='auto', leaf_size=30,\n",
       "                                            metric='minkowski',\n",
       "                                            metric_params=None, n_jobs=None,\n",
       "                                            n_neighbors=5, p=2,\n",
       "                                            weights='uniform'),\n",
       "             iid='deprecated', n_jobs=None,\n",
       "             param_grid={'n_neighbors': array([ 1,  3,  5,  7,  9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31, 33,\n",
       "       35, 37, 39, 41, 43, 45, 47, 49])},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring=None, verbose=0)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#KNN\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "knn = KNeighborsClassifier()\n",
    "param_grid = {\"n_neighbors\": np.arange(1,50,2)}\n",
    "knn_gscv = GridSearchCV(knn, param_grid, cv=5)\n",
    "knn_gscv.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_neighbors': 15}"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_gscv.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8731884057971014\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      0.86      0.86       123\n",
      "           1       0.89      0.88      0.89       153\n",
      "\n",
      "    accuracy                           0.87       276\n",
      "   macro avg       0.87      0.87      0.87       276\n",
      "weighted avg       0.87      0.87      0.87       276\n",
      "\n",
      "[[106  17]\n",
      " [ 18 135]]\n"
     ]
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=15)\n",
    "knn.fit(X_train,y_train)\n",
    "y_pred = knn.predict(X_test)\n",
    "\n",
    "print(knn.score(X_test,y_test))\n",
    "print(classification_report(y_test,y_pred))\n",
    "print(confusion_matrix(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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