Heart attack Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fe08f2cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "79669b46",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset=pd.read_csv('Heart Attack Data Set.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "04833ff8",
   "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>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
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       "<p>303 rows × 14 columns</p>\n",
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       "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "0     63    1   3       145   233    1        0      150      0      2.3   \n",
       "1     37    1   2       130   250    0        1      187      0      3.5   \n",
       "2     41    0   1       130   204    0        0      172      0      1.4   \n",
       "3     56    1   1       120   236    0        1      178      0      0.8   \n",
       "4     57    0   0       120   354    0        1      163      1      0.6   \n",
       "..   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...   \n",
       "298   57    0   0       140   241    0        1      123      1      0.2   \n",
       "299   45    1   3       110   264    0        1      132      0      1.2   \n",
       "300   68    1   0       144   193    1        1      141      0      3.4   \n",
       "301   57    1   0       130   131    0        1      115      1      1.2   \n",
       "302   57    0   1       130   236    0        0      174      0      0.0   \n",
       "\n",
       "     slope  ca  thal  target  \n",
       "0        0   0     1       1  \n",
       "1        0   0     2       1  \n",
       "2        2   0     2       1  \n",
       "3        2   0     2       1  \n",
       "4        2   0     2       1  \n",
       "..     ...  ..   ...     ...  \n",
       "298      1   0     3       0  \n",
       "299      1   0     3       0  \n",
       "300      1   2     3       0  \n",
       "301      1   1     3       0  \n",
       "302      1   1     2       0  \n",
       "\n",
       "[303 rows x 14 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "dataset"
   ]
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  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f0b398ca",
   "metadata": {},
   "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",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   age       303 non-null    int64  \n",
      " 1   sex       303 non-null    int64  \n",
      " 2   cp        303 non-null    int64  \n",
      " 3   trestbps  303 non-null    int64  \n",
      " 4   chol      303 non-null    int64  \n",
      " 5   fbs       303 non-null    int64  \n",
      " 6   restecg   303 non-null    int64  \n",
      " 7   thalach   303 non-null    int64  \n",
      " 8   exang     303 non-null    int64  \n",
      " 9   oldpeak   303 non-null    float64\n",
      " 10  slope     303 non-null    int64  \n",
      " 11  ca        303 non-null    int64  \n",
      " 12  thal      303 non-null    int64  \n",
      " 13  target    303 non-null    int64  \n",
      "dtypes: float64(1), int64(13)\n",
      "memory usage: 33.3 KB\n"
     ]
    }
   ],
   "source": [
    "dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "fac78dcf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age         0\n",
       "sex         0\n",
       "cp          0\n",
       "trestbps    0\n",
       "chol        0\n",
       "fbs         0\n",
       "restecg     0\n",
       "thalach     0\n",
       "exang       0\n",
       "oldpeak     0\n",
       "slope       0\n",
       "ca          0\n",
       "thal        0\n",
       "target      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "401e7481",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach',\n",
       "       'exang', 'oldpeak', 'slope', 'ca', 'thal', 'target'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "37795436",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\" This is a heart dataset.\\n\\nYou can perform classification on this dataset.\\n\\nAbout DataSet There are 13 attributes: \\n\\n1. Age: Age (in years) \\n2. Sex: gender (1 = male; 0 = female)\\n3.ChestPain: Chest Pain type -- 1: typical angina (all criteria present) -- 2: atypical angina (two of three criteria satisfied) \\n-- 3: non-anginal pain (less than one criteria satisfied) -- 4: asymptomatic (none of the criteria are satisfied)\\n4. Restbps: Resting Blood pressure (in mmHg, upon admission to the hospital)\\n5. Chol: serum cholesterol in mg/dL 6. Fbs: fasting blood sugar > 120 mg/dL (likely to be diabetic) 1 = true; 0 = false\\n7. RestECG: Resting electrocardiogram results -- Value 0: normal -- Value 1: having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV) -- Value 2: showing probable or definite left ventricular hypertrophy by Estes' criteria\\n8. MaxHR: Greatest number of beats per minute your heart can possibly reach during all-out strenuous exercise.\\n9.Exang: exercise induced angina (1 = yes; 0 = no)\\n10. Oldpeak: ST depression induced by exercise relative to rest (in mm, achieved by subtracting the lowest ST segment points during exercise and rest)\\n11. Slope: the slope of the peak exercise ST segment, ST-T abnormalities are considered to be a crucial indicator for identifying presence of ischaemia -- Value 1: upsloping -- Value 2: flat -- Value 3: downsloping \\n12.Ca: number of major vessels (0-3) colored by fluoroscopy. Major cardial vessels are as goes: aorta, superior vena cava, inferior vena cava, pulmonary artery (oxygen-poor blood --> lungs), pulmonary veins (oxygen-rich blood --> heart), and coronary arteries (supplies blood to heart tissue).\\n13. AHD: 0 = normal; 1 = fixed defect (heart tissue can't absorb thallium both under stress and in rest); 2 = reversible defect (heart tissue is unable to absorb thallium only under the exercise portion of the test) \\n14.AHD: 0 = no disease, 1 = disease\""
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "''' This is a heart dataset.\n",
    "\n",
    "You can perform classification on this dataset.\n",
    "\n",
    "About DataSet There are 13 attributes: \n",
    "\n",
    "1. Age: Age (in years) \n",
    "2. Sex: gender (1 = male; 0 = female)\n",
    "3.ChestPain: Chest Pain type -- 1: typical angina (all criteria present) -- 2: atypical angina (two of three criteria satisfied) \n",
    "-- 3: non-anginal pain (less than one criteria satisfied) -- 4: asymptomatic (none of the criteria are satisfied)\n",
    "4. Restbps: Resting Blood pressure (in mmHg, upon admission to the hospital)\n",
    "5. Chol: serum cholesterol in mg/dL 6. Fbs: fasting blood sugar > 120 mg/dL (likely to be diabetic) 1 = true; 0 = false\n",
    "7. RestECG: Resting electrocardiogram results -- Value 0: normal -- Value 1: having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV) -- Value 2: showing probable or definite left ventricular hypertrophy by Estes' criteria\n",
    "8. MaxHR: Greatest number of beats per minute your heart can possibly reach during all-out strenuous exercise.\n",
    "9.Exang: exercise induced angina (1 = yes; 0 = no)\n",
    "10. Oldpeak: ST depression induced by exercise relative to rest (in mm, achieved by subtracting the lowest ST segment points during exercise and rest)\n",
    "11. Slope: the slope of the peak exercise ST segment, ST-T abnormalities are considered to be a crucial indicator for identifying presence of ischaemia -- Value 1: upsloping -- Value 2: flat -- Value 3: downsloping \n",
    "12.Ca: number of major vessels (0-3) colored by fluoroscopy. Major cardial vessels are as goes: aorta, superior vena cava, inferior vena cava, pulmonary artery (oxygen-poor blood --> lungs), pulmonary veins (oxygen-rich blood --> heart), and coronary arteries (supplies blood to heart tissue).\n",
    "13. AHD: 0 = normal; 1 = fixed defect (heart tissue can't absorb thallium both under stress and in rest); 2 = reversible defect (heart tissue is unable to absorb thallium only under the exercise portion of the test) \n",
    "14.AHD: 0 = no disease, 1 = disease'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "cc523fbd",
   "metadata": {},
   "outputs": [],
   "source": [
    "y=dataset['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2d79f399",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1\n",
       "1      1\n",
       "2      1\n",
       "3      1\n",
       "4      1\n",
       "      ..\n",
       "298    0\n",
       "299    0\n",
       "300    0\n",
       "301    0\n",
       "302    0\n",
       "Name: target, Length: 303, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "10cf4e26",
   "metadata": {},
   "outputs": [],
   "source": [
    "#x = dataset[['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach','exang', 'oldpeak', 'slope', 'ca', 'thal']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d2a3420a",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = dataset[['age','sex','chol','thalach']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5821f9d2",
   "metadata": {},
   "outputs": [
    {
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       "      <td>174</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>303 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     age  sex  chol  thalach\n",
       "0     63    1   233      150\n",
       "1     37    1   250      187\n",
       "2     41    0   204      172\n",
       "3     56    1   236      178\n",
       "4     57    0   354      163\n",
       "..   ...  ...   ...      ...\n",
       "298   57    0   241      123\n",
       "299   45    1   264      132\n",
       "300   68    1   193      141\n",
       "301   57    1   131      115\n",
       "302   57    0   236      174\n",
       "\n",
       "[303 rows x 4 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d9eae499",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "bce99768",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>chol</th>\n",
       "      <th>thalach</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
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       "      <td>233</td>\n",
       "      <td>150</td>\n",
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       "      <td>163</td>\n",
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       "      <td>141</td>\n",
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       "      <td>57</td>\n",
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       "      <td>57</td>\n",
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       "</table>\n",
       "<p>303 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     age  sex  chol  thalach\n",
       "0     63    1   233      150\n",
       "1     37    1   250      187\n",
       "2     41    0   204      172\n",
       "3     56    1   236      178\n",
       "4     57    0   354      163\n",
       "..   ...  ...   ...      ...\n",
       "298   57    0   241      123\n",
       "299   45    1   264      132\n",
       "300   68    1   193      141\n",
       "301   57    1   131      115\n",
       "302   57    0   236      174\n",
       "\n",
       "[303 rows x 4 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "80273c89",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "5ee22daf",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,stratify=y,random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "68edcc6a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(242, 4) (61, 4) (303, 4)\n"
     ]
    }
   ],
   "source": [
    "print(x_train.shape,x_test.shape,x.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "46654e1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1f796eb7",
   "metadata": {},
   "outputs": [],
   "source": [
    "model=LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "7cda4cc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "db099f54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
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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>chol</th>\n",
       "      <th>thalach</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "      <td>309</td>\n",
       "      <td>156</td>\n",
       "    </tr>\n",
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       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>244</td>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>234</td>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>68</td>\n",
       "      <td>1</td>\n",
       "      <td>193</td>\n",
       "      <td>141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>185</td>\n",
       "      <td>155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>271</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>242 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     age  sex  chol  thalach\n",
       "61    54    1   309      156\n",
       "238   77    1   304      162\n",
       "160   56    1   240      169\n",
       "158   58    1   220      144\n",
       "289   55    0   205      130\n",
       "..   ...  ...   ...      ...\n",
       "100   42    1   244      178\n",
       "49    53    0   234      160\n",
       "300   68    1   193      141\n",
       "194   60    1   185      155\n",
       "131   49    0   271      162\n",
       "\n",
       "[242 rows x 4 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "ee412d68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "61     1\n",
       "238    0\n",
       "160    1\n",
       "158    1\n",
       "289    0\n",
       "      ..\n",
       "100    1\n",
       "49     1\n",
       "300    0\n",
       "194    0\n",
       "131    1\n",
       "Name: target, Length: 242, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e997999e",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "c4ba890b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "79c7ccb8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# To know the accurecy of train data\n",
    "x_train_prediction=model.predict(x_train)\n",
    "training_data_accuracy = accuracy_score(x_train_prediction,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "38fa2cfa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accurracy on Test data : 0.7148760330578512\n"
     ]
    }
   ],
   "source": [
    "print(\"Accurracy on Test data :\",training_data_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "3cb0ffe7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TO know the Accurecy of test data\n",
    "x_test_prediction=model.predict(x_test)\n",
    "test_data_accuracy = accuracy_score(x_test_prediction,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "9f128d16",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accurracy on Test data : 0.7213114754098361\n"
     ]
    }
   ],
   "source": [
    "print(\"Accurracy on Test data :\",test_data_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f3a010df",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "169036de",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\bhari\\OneDrive\\Desktop\\Heart_attack_prediction\\Project\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "prep=model.predict([[57,0,354,163]])\n",
    "print(prep)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "2db3eda9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "f55351da",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('model.pkl','wb') as files:\n",
    "    pickle.dump(model,files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5912eddf",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b86d0251",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e2e68ddf",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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