https://github.com/ad1tyaraj/heart-attack-model-webapps
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.
https://github.com/ad1tyaraj/heart-attack-model-webapps
django project python webapp website
Last synced: about 2 months ago
JSON representation
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.
- Host: GitHub
- URL: https://github.com/ad1tyaraj/heart-attack-model-webapps
- Owner: Ad1tyaRaj
- Created: 2025-02-09T09:35:16.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-02-09T09:51:01.000Z (over 1 year ago)
- Last Synced: 2025-02-09T10:28:33.623Z (over 1 year ago)
- Topics: django, project, python, webapp, website
- Language: HTML
- Homepage: https://ad1tyaraj.github.io/Portfolio/
- Size: 0 Bytes
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# 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:
```bash
# 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:
```bash
python train.py
```
To make predictions using the trained model:
```bash
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.