❤️ Heart Disease Prediction Project

Predicting cardiovascular events using clinical data with 86.4% accuracy
ML workflow comparing 6 algorithms with integrated dataset analysis

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📋 Project Overview

This Jupyter notebook demonstrates a complete machine learning workflow using the heart.csv clinical dataset containing 918 patient records with 11 diagnostic features. The project achieves 86.4% accuracy using Random Forest classification.

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🚀 Key Features

• 6 ML Algorithms Tested
  

  • Random Forest Classifier
  • XGBoost Classifier
  • Logistic Regression
  • K-Nearest Neighbors
  • Support Vector Machine
  • Decision Tree

• Dataset Features
  Age,Sex,ChestPainType,RestingBP,Cholesterol,FastingBS,RestingECG,MaxHR,ExerciseAngina,Oldpeak,ST_Slope

Few Features are listed below.

Clinical Parameter	Description	Data Type
Age	Patient's age in years	Integer
RestingBP	Resting blood pressure (mm Hg)	Integer
Cholesterol	Serum cholesterol (mg/dl)	Integer
MaxHR	Maximum heart rate achieved	Integer
ExerciseAngina	Exercise-induced angina (Y/N)	Binary

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🛠️ Installation

1. Clone repository: git clone https://github.com/atharwaaah/heart-disease-prediction.git cd heart-disease-prediction
2. Install requirements: pip install -r requirements.txt
3. Launch Jupyter notebook with dataset: jupyter notebook HeartDiseasePrediction.ipynb heart.csv

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🔍 Data Exploration

The included heart.csv contains:

• 918 patient records
• 11 clinical features
• Binary classification (HeartDisease 0/1)

import pandas as pd df = pd.read_csv('heart.csv') print(f"Dataset shape: {df.shape}") print(f"Positive cases: {df.HeartDisease.value_counts()1} ({(df.HeartDisease.value_counts()1/len(df))*100:.1f}%)")

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📈 Model Performance

Algorithm	Accuracy	Precision	Recall	F1-Score
Random Forest	86.4%	0.87	0.85	0.86
XGBoost	85.2%	0.84	0.83	0.84
Logistic Regression	82.7%	0.81	0.80	0.81

Detailed confusion matrices and ROC curves available in notebook

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🧠 Sample Prediction

from sklearn.preprocessing import StandardScaler import joblib model = joblib.load('heart_disease_model.pkl') scaler = joblib.load('scaler.pkl') Input features: [Age, Sex, ChestPainType, RestingBP...] sample_data = [[40, 'M', 'ATA', 140, 289, 0, 'Normal', 172, 'N', 0, 'Up']] processed_data = preprocess(sample_data) # Custom preprocessing function scaled_data = scaler.transform(processed_data) prediction = model.predict(scaled_data) print('❤️ Healthy Heart' if prediction == 0 else '⚠️ Heart Disease Detected')

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🤝 Contribution Guidelines

1. Fork the repository
2. Create your feature branch: git checkout -b feature/AmazingFeature
3. Commit changes: git commit -m 'Add some AmazingFeature'
4. Push to branch: git push origin feature/AmazingFeature
5. Open a Pull Request

Always keep original heart.csv intact when modifying data

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📜 License

Distributed under the MIT License. See LICENSE for details.

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Made with ❤️ by Atharv Gupta