Heart Disease Prediction Model

Overview

This project is a heart disease prediction model developed using Jupyter Notebook. The model follows a comprehensive data analytics process and is trained on over 300 models to achieve high accuracy in predicting heart disease.

Features

• Predictive Capability: Accurately predicts the likelihood of heart disease in patients.
• High Accuracy: Achieves over 90% accuracy in predictions.
• Model Training: Utilizes a robust training process involving over 300 models.

Requirements

To run this model, you will need the following libraries:

• Python 3.x
• Jupyter Notebook
• Pandas
• NumPy
• Scikit-learn
• Matplotlib
• Seaborn (optional, for enhanced visualization)

You can install the necessary libraries using pip:

pip install pandas numpy scikit-learn matplotlib seaborn