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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