Heart Disease Prediction with Machine Learning

Introduction

This project aims to predict heart disease using machine learning techniques. A predictive model have been developed that can help assess the likelihood of a person having heart disease based on various medical attributes.

Features

• Data preprocessing and exploratory data analysis.
• Machine learning model development and evaluation.
• Deployment using Streamlit for a user-friendly interface.

Getting Started

To get started with this project, follow these steps:

Prerequisites

• Python 3.x
• Spyder (for development)
• Google Colab (for cloud-based development)
• Streamlit (for deployment)
• Required Python libraries listed in requirements.txt

Installation

• Sequentially execute this code

  - git clone https://github.com/yourusername/heart-disease-prediction.git
  - python -m venv venv
  - venv\Scripts\activate
  - pip install -r requirements.txt

Usage

• Data Exploration and Model Development: Use Google Collab to explore the dataset, preprocess the data, and develop machine learning models.
• To deploy the project locally, run the following command: streamlit run "location_of_file_with_.py".
• Project have been deployed using Streamlit, you can access the live demo here.

Acknowledgments

Special thanks to the contributors and open-source projects that provided inspiration, guidance, and tools for this project.