Heart Disease Prediction Model

This repository contains a Jupyter Notebook for predicting heart disease using a machine learning model. The dataset used for this project is from Kaggle: Heart Disease Dataset.

Table of Contents

• Overview
• Dataset
• Installation
• Usage
• Model
• Results
• Contributing
• License

Overview

Heart disease is a leading cause of death worldwide. Early detection can help in taking preventive measures and treatment to reduce the risk. This project aims to predict the presence of heart disease in a patient based on various medical attributes using machine learning techniques.

Dataset

The dataset used in this project is publicly available on Kaggle and contains several medical predictors such as age, sex, chest pain type, resting blood pressure, cholesterol level, fasting blood sugar, and more.

• Dataset Link: Heart Disease Dataset

Installation

To run this project, you need to have Python and Jupyter Notebook installed on your system. Additionally, install the required Python packages by running:

pip install -r requirements.txt

Usage

1. Clone the repository:

   git clone https://github.com/your-username/heart-disease-prediction.git
   cd heart-disease-prediction

2. Install the required packages:

   pip install -r requirements.txt

3. Open the Jupyter Notebook:

   jupyter notebook heart_disease_prediction.ipynb

4. Follow the steps in the notebook to preprocess the data, train the model, and evaluate its performance.

Model

The notebook covers the following steps:

1. Data Exploration: Understanding the dataset and visualizing the features.
2. Data Preprocessing: Cleaning the data and preparing it for the model.
3. Model Training: Training various machine learning models such as Logistic Regression, Decision Tree, Random Forest, and others.
4. Model Evaluation: Evaluating the performance of the models using metrics such as accuracy, precision, recall, and F1 score.

Results

The model's performance is evaluated, and the best-performing model is selected based on the evaluation metrics. Details of the results can be found in the notebook.

Contributing

Contributions are welcome! Please fork the repository and submit a pull request for any enhancements or bug fixes.

License

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