Heart Disease Prediction

Overview

This repository contains the code for a Heart Disease Prediction project. The goal of this project is to develop a machine learning model capable of predicting the likelihood of an individual having heart disease based on various health-related features.

This project includes the following files:

• app.py: Contains the main application logic and user interface, likely built using a framework like Streamlit or Flask, allowing users to interact with the prediction model.
• ml_code.py: Implements the machine learning model used for heart disease prediction, including data preprocessing, model training, and prediction functionalities.

Table of Contents

• Overview
• Installation
• Usage
• File Descriptions

Installation

1. Clone the repository:

   git clone https://github.com/AkshithSai-24/HeartDiseasePrediction.git

2. Navigate to the project directory:

   cd HeartDiseasePrediction

3. It is highly recommended to create and activate a virtual environment:

   python app.py

4. Install the necessary dependencies. While a requirements.txt file provided in the context,

   use:

   pip install -r requirements.txt

Usage

1. Ensure all dependencies are installed as mentioned in the requirements.txt section .
2. Run the application by executing the app.py file. This likely launches a web application if Streamlit or Flask is used:

   python app.py         # If using Flask or another framework (check the file for run instructions)

3. Interact with the user interface provided by the application. You will likely be able to input various health features, and the model will output a prediction regarding the likelihood of heart disease.

File Descriptions

• app.py: This file serves as the main entry point for the Heart Disease Prediction application. It likely handles user input through a graphical or command-line interface and uses the functionalities provided by ml_code.py to make predictions.
• ml_code.py: This file contains the core logic for the heart disease prediction model. It likely includes steps for data loading and preprocessing, the implementation of a machine learning algorithm ), model training on relevant datasets, and the function to generate predictions based on input features.