heart_ml_model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a11e91d6-7bd2-473e-933d-501f6a7edc90",
   "metadata": {},
   "source": [
    "<H1 style = 'color:blue' align = 'center'> <b>Heart Diseases Prediction using ML</b></h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b977e46c-4bae-4063-9168-56c0997f908d",
   "metadata": {},
   "source": [
    "This is a classification problem to classify the Heart Disesas..."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1df3cdb6-94fc-4c04-a7b7-22d9e9ab9548",
   "metadata": {},
   "source": [
    "# Steps involved to complete this steps...\n",
    "<p> 1. Importing the depandencies...</p>\n",
    "<p> 2. Data collection and Processing...</p>\n",
    "<p> 3. Model Training ... </p>\n",
    "<p> 4. Model testing...</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a2feb25d-d8a4-4d6f-bb8f-34f2a5f6ec77",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import required moduiles...\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy.stats import skew\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score,confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "db6b1de8-4948-45fb-887b-18c72d398978",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load the csv file to a dataframe...\n",
    "\n",
    "df = pd.read_csv('heart_ds.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cb12ba69-a3ff-4b52-b729-3709fd2516eb",
   "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>cp</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>0</th>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>125</td>\n",
       "      <td>212</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>168</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>203</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>155</td>\n",
       "      <td>1</td>\n",
       "      <td>3.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>145</td>\n",
       "      <td>174</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>125</td>\n",
       "      <td>1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>61</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>148</td>\n",
       "      <td>203</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>161</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>138</td>\n",
       "      <td>294</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>106</td>\n",
       "      <td>0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   52    1   0       125   212    0        1      168      0      1.0      2   \n",
       "1   53    1   0       140   203    1        0      155      1      3.1      0   \n",
       "2   70    1   0       145   174    0        1      125      1      2.6      0   \n",
       "3   61    1   0       148   203    0        1      161      0      0.0      2   \n",
       "4   62    0   0       138   294    1        1      106      0      1.9      1   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   2     3       0  \n",
       "1   0     3       0  \n",
       "2   0     3       0  \n",
       "3   1     3       0  \n",
       "4   3     2       0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print the top 5 data point of the dataset...\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6a433d54-9216-4c24-b9ee-a3a6b4a3aee1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1025 entries, 0 to 1024\n",
      "Data columns (total 14 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   age       1025 non-null   int64  \n",
      " 1   sex       1025 non-null   int64  \n",
      " 2   cp        1025 non-null   int64  \n",
      " 3   trestbps  1025 non-null   int64  \n",
      " 4   chol      1025 non-null   int64  \n",
      " 5   fbs       1025 non-null   int64  \n",
      " 6   restecg   1025 non-null   int64  \n",
      " 7   thalach   1025 non-null   int64  \n",
      " 8   exang     1025 non-null   int64  \n",
      " 9   oldpeak   1025 non-null   float64\n",
      " 10  slope     1025 non-null   int64  \n",
      " 11  ca        1025 non-null   int64  \n",
      " 12  thal      1025 non-null   int64  \n",
      " 13  target    1025 non-null   int64  \n",
      "dtypes: float64(1), int64(13)\n",
      "memory usage: 112.2 KB\n"
     ]
    }
   ],
   "source": [
    "# To get basic knowldage of dataset...\n",
    "\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fc7fac12-112c-4fd1-bd8d-b3c593520dd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    526\n",
       "0    499\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Is the data balanced ?...\n",
    "\n",
    "df['target'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "779f5932-f186-48e6-acf7-ef372b9c9be7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BX9pU1WeS3JNkTZ+xi1nngN1MuSSziPuzj+VwfPayDI7PhVvqi/7L6QH8EVe+ofqhafoE+Cjw4WnWrR+Y/03g8BBrWwl8E9jCD9502j6lz21c+YbVib5jF7nOG4EzwJumtL8MeMXA/JeBnUtY56v4wY1+O4Cnu327rPZn1+/HmbyO/LKl2J8D29zMzG9ULvnx2bPOJT8+h/IzLnUBy+kB/ARwHDjdTVd37T8JfKabfzOTfzI+AjzUPW7t1v0N8Gi37igDYT+k+m5l8tM53wDe17W9C3hXNx8m/5OUb3R1jM82doT7ca46PwJ8Z2D/TXTtr+le2A8DJ5dBne/u6niYyTfW3jTb2KWqs1v+daacTCzB/vw4cB74XybP0vcu0+NzrjqXxfG50IdfPyBJDfLTMpLUIMNdkhpkuEtSgwx3SWqQ4S5JDTLcJalBhrskNej/AWW8VDOxh9FfAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check throught graph...\n",
    "\n",
    "plt.bar([1,0], df['target'].value_counts())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c59636a-1e70-4484-8e9a-9138b292f7c0",
   "metadata": {},
   "source": [
    "1 --> Defective Heart\n",
    "\n",
    "0 --> Healthy Heart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "74ea596c-891e-45c7-b2e7-4d23bd715079",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.24850156, -0.85020237,  0.52868008,  0.7386852 ,  1.07250033,\n",
       "        1.96845247,  0.18017624, -0.513025  ,  0.69164112,  1.20912662,\n",
       "       -0.47843296,  1.25934225, -0.52362206, -0.05270121])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "skew(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "03623695-540c-44fa-9780-c507bca25c63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x19608cfdfd0>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(skew(df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9b1dc4df-4f37-46b9-a313-5abb75aaf6ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting the Feature and Target...\n",
    "\n",
    "X = df.drop(columns = 'target', axis = 1)\n",
    "Y = df['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6f8eaa70-c610-41cc-9ecb-d0c78fbc2c1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting data into train and test part...\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size = 0.25, stratify = Y, random_state = 2 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5e738d16-8c42-4294-9fc7-25c1a10263ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1025, 13) (768, 13) (257, 13)\n"
     ]
    }
   ],
   "source": [
    "print(X.shape, x_train.shape, x_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c7b89966-9570-4dac-98c8-aeddbd7d6f3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train The Model...\n",
    "\n",
    "model = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2d69e9db-0be7-4a7b-a20f-991a0bc83f19",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\M.SAKSHAM\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the model...\n",
    "\n",
    "model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ac3aaeda-da32-431f-b6ca-2fc3c1f126ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check the accouracy score...\n",
    "\n",
    "x_train_prediction = model.predict(x_train)\n",
    "training_data_accuracy = accuracy_score(x_train_prediction, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c4b5680e-921e-40a8-a98f-8af81124b416",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on Trainging data :  0.86328125\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.86328125"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Accuracy on Trainging data : ', training_data_accuracy)\n",
    "training_data_accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "796c56ad-10e7-4079-bd25-2daa41fa1e1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make the prediction using test data...\n",
    "\n",
    "x_test_prediction = model.predict(x_test)\n",
    "test_data_accuracy = accuracy_score(x_test_prediction, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a84d1b18-2e57-4a17-891e-1485467713af",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on Test data :  0.7937743190661478\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy on Test data : ', test_data_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f1dca160-5839-4c08-a83d-3c8668c2327c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\n",
      "The Person does not have a Heart Disease\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\M.SAKSHAM\\anaconda3\\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": [
    "# Building a Predictive System\n",
    "\n",
    "input_data = (62,0,0,140,268,0,0,160,0,3.6,0,2,2)\n",
    "\n",
    "\n",
    "# change the input data to a numpy array\n",
    "\n",
    "input_data_as_numpy_array= np.asarray(input_data)\n",
    "\n",
    "\n",
    "# reshape the numpy array as we are predicting for only on instance\n",
    "\n",
    "input_data_reshaped = input_data_as_numpy_array.reshape(1,-1)\n",
    "\n",
    "\n",
    "prediction = model.predict(input_data_reshaped)\n",
    "\n",
    "print(prediction)\n",
    "\n",
    "if (prediction[0]== 0):\n",
    "  print('The Person does not have a Heart Disease')\n",
    "else:\n",
    "  print('The Person has Heart Disease')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "df3b4772-8cdf-43f3-95ac-0341d1d32c67",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 91  34]\n",
      " [ 19 113]]\n"
     ]
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test,x_test_prediction)\n",
    "print(cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "72f88dd5-e9e3-4911-ac9a-e7b92d7cbbdb",
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
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       "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "249   42    1   2       130   180    0        1      150      0      0.0   \n",
       "523   61    1   0       148   203    0        1      161      0      0.0   \n",
       "563   44    1   0       112   290    0        0      153      0      0.0   \n",
       "742   63    1   0       130   330    1        0      132      1      1.8   \n",
       "103   47    1   2       138   257    0        0      156      0      0.0   \n",
       "..   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...   \n",
       "775   51    1   2       100   222    0        1      143      1      1.2   \n",
       "292   61    1   2       150   243    1        1      137      1      1.0   \n",
       "308   59    1   2       126   218    1        1      134      0      2.2   \n",
       "175   56    0   0       200   288    1        0      133      1      4.0   \n",
       "843   59    1   3       160   273    0        0      125      0      0.0   \n",
       "\n",
       "     slope  ca  thal  \n",
       "249      2   0     2  \n",
       "523      2   1     3  \n",
       "563      2   1     2  \n",
       "742      2   3     3  \n",
       "103      2   0     2  \n",
       "..     ...  ..   ...  \n",
       "775      1   0     2  \n",
       "292      1   0     2  \n",
       "308      1   1     1  \n",
       "175      0   2     3  \n",
       "843      2   0     2  \n",
       "\n",
       "[257 rows x 13 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test"
   ]
  },
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   "execution_count": null,
   "id": "ba19be40-e6fc-4c43-81a8-2d999430b6b1",
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   "outputs": [],
   "source": []
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