Framingham Heart Disease Prediction

📌 Project Overview

This project predicts whether a person is at risk of developing heart disease within the next 10 years using the Framingham Heart Study dataset. Various Machine Learning classification algorithms were trained and compared to identify the best-performing model.

🎯 Objectives

• Predict the 10-year risk of heart disease.
• Perform Exploratory Data Analysis (EDA).
• Handle missing values and preprocess the dataset.
• Train multiple Machine Learning models.
• Compare model performance using evaluation metrics.

📂 Dataset

• Dataset: Framingham Heart Study Dataset
• Target Variable: TenYearCHD

🛠️ Technologies Used

• Python
• Jupyter Notebook
• Pandas
• NumPy
• Matplotlib
• Seaborn
• Scikit-learn

📊 Machine Learning Models

• Logistic Regression
• Decision Tree
• Random Forest
• K-Nearest Neighbors (KNN)
• Support Vector Machine (SVM)

📈 Evaluation Metrics

• Accuracy
• Confusion Matrix
• Classification Report

📷 Visualizations

• Count Plot
• Histograms
• Box Plots
• Correlation Heatmap
• Confusion Matrix Heatmap
• Model Accuracy Comparison

📁 Project Structure

Machinelearning-project/
│── Framingham.ipynb
│── framingham.csv
│── README.md

🚀 How to Run

1. Clone the repository.
2. Install the required libraries:

   pip install pandas numpy matplotlib seaborn scikit-learn

3. Open Framingham.ipynb in Jupyter Notebook.
4. Run all cells.

📌 Results

The models were trained and evaluated using accuracy, confusion matrix, and classification report. Random Forest and SVM provided strong performance on the dataset.

👨‍💻 Author

Medidhi V Naga Sri Surya Satya Santhoshi

GitHub: https://github.com/SANTHU-576