Heart Disease Prediction Details 

Predicts the likelihood of heart disease using multiple machine learning models trained on real clinical data. 

Project Description 

This project uses supervised machine learning techniques to predict whether a person is at risk of heart disease based on medical parameters such as age, cholesterol level, blood pressure, and other clinical features. Multiple models are implemented and compared to ensure accurate and reliable predictions. The system also provides a user-friendly web interface for easy interaction and result visualization.

Features 

• Multiple ML models for better prediction accuracy

• Comparison of model performance

• Clean and interactive web interface

• Real-time prediction based on user input

• Data visualization using charts

• Well-structured and documented source code

• Suitable for academic and final-year projects

Tech Stack

• Python

• Flask (Backend)

• Pandas

• NumPy

• Scikit-learn

• Matplotlib (Data Visualization)

• HTML, JavaScript (Frontend)

System Requirements

• Python 3.8+

• Anaconda

• Libraries: numpy, pandas, scikit-learn, matplotlib

• No GPU required

Deliverables 

• Jupyter Notebook

• Dataset

• Setup over video call

• WhatsApp support