Heart Disease Prediction

A machine learning project that predicts whether a patient has heart disease based on their medical attributes using various classification algorithms.

📋 Project Overview

This project uses Python-based machine learning libraries to build a predictive model capable of determining whether a patient has heart disease. The analysis follows a structured approach from problem definition through to model experimentation.

🎯 Problem Statement

Given clinical parameters about a patient, can we predict whether or not they have heart disease?

Success Metric: Achieve 95% accuracy in predicting heart disease during the proof of concept phase.

📊 Dataset

The dataset originates from the Cleveland data from the UCI Machine Learning Repository:

• Source: UCI Machine Learning Repository - Heart Disease
• Alternative: Kaggle - Heart Disease Classification Dataset

Features

The dataset includes 14 medical attributes:

1. age - Age in years
2. sex - Sex (1 = male; 0 = female)
3. cp - Chest pain type (0-3)
4. trestbps - Resting blood pressure (mm Hg)
5. chol - Serum cholesterol (mg/dl)
6. fbs - Fasting blood sugar > 120 mg/dl (1 = true; 0 = false)
7. restecg - Resting electrocardiographic results (0-2)
8. thalach - Maximum heart rate achieved
9. exang - Exercise induced angina (1 = yes; 0 = no)
10. oldpeak - ST depression induced by exercise
11. slope - Slope of peak exercise ST segment (0-2)
12. ca - Number of major vessels colored by fluoroscopy (0-3)
13. thal - Thalium stress test result
14. target - Heart disease present (1 = yes, 0 = no)

🛠️ Technologies Used

• Python 3.x
• Pandas - Data manipulation and analysis
• NumPy - Numerical computing
• Matplotlib & Seaborn - Data visualization
• Scikit-learn - Machine learning algorithms
  • Logistic Regression
  • K-Nearest Neighbors (KNN)
  • Random Forest Classifier

📁 Project Structure

Heart_disease_prediction/
│
├── end-to-end-heart-disease-classification.ipynb  # Main analysis notebook
├── heart-disease.csv                               # Dataset
├── README.md                                       # Project documentation
└── LICENSE                                         # MIT License

🚀 Getting Started

Prerequisites

pip install numpy pandas matplotlib seaborn scikit-learn jupyter

Installation

1. Clone the repository:

git clone https://github.com/Krishal-Modi/Heart_disease_prediction.git
cd Heart_disease_prediction

2. Install required packages:

pip install -r requirements.txt

3. Launch Jupyter Notebook:

jupyter notebook end-to-end-heart-disease-classification.ipynb

📈 Methodology

1. Problem Definition - Define the prediction goal
2. Data Analysis - Explore and understand the dataset
3. Evaluation Metrics - Define success criteria
4. Feature Engineering - Understand feature importance
5. Modeling - Train multiple classification models
6. Experimentation - Hyperparameter tuning and optimization

🔍 Model Evaluation

The project evaluates models using:

• Cross-validation scores
• Confusion matrix
• Precision, Recall, and F1-Score
• Classification reports

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

👤 Author

Krishal Modi

🤝 Contributing

Contributions, issues, and feature requests are welcome!

⭐ Show your support

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