Heart Attack Prediction Model

Overview

This repository contains a machine learning project that predicts the likelihood of a heart attack based on a dataset of 170,501 rows and 25 features. The current model achieves an accuracy of 75%, with ongoing improvements through feature engineering and scaling.

Features

• Dataset Size: 170,501 rows and 25 columns.
• Model Accuracy: 75%.
• Techniques Used:
  • Feature Engineering: Enhancing feature selection and transformation.
  • Scaling: Standardizing feature values for better model performance.

Objectives

• Improve the model's accuracy and robustness.
• Optimize feature selection and scaling techniques.
• Provide a user-friendly interface and detailed documentation.

Installation

To set up the project locally, follow these steps:

# Clone the repository
https://github.com/Ad1tyaRaj/Heart-Attack-Model-webapps.git

# Navigate to the project directory
cd heart-attack-prediction

# Install dependencies
pip install -r requirements.txt

Usage

To train and test the model, run:

python train.py

To make predictions using the trained model:

python predict.py --input data/sample_input.csv

Dataset

The dataset contains 170,501 records with 25 features, including patient demographics, medical history, and clinical measurements. Data preprocessing includes handling missing values, feature selection, and scaling.

Model Details

The model is built using machine learning algorithms, with improvements through feature engineering and scaling techniques. The goal is to enhance prediction accuracy beyond 75%.

Contributing

We welcome contributions! Feel free to fork the repository and submit pull requests.

License

This project is licensed under the MIT License.