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https://github.com/ad1tyaraj/heart-attack-model
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
ai code flask-application machine-learning model python
Last synced: 5 days ago
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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
- Owner: Ad1tyaRaj
- Created: 2025-01-18T18:58:31.000Z (6 days ago)
- Default Branch: main
- Last Pushed: 2025-01-18T19:17:03.000Z (6 days ago)
- Last Synced: 2025-01-18T20:23:02.540Z (6 days ago)
- Topics: ai, code, flask-application, machine-learning, model, python
- Language: Jupyter Notebook
- Homepage: https://ad1tyaraj.github.io/Portfolio/
- Size: 687 KB
- 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
1. Improve the model's accuracy and robustness.
2. Optimize feature selection and scaling techniques.
3. Provide a user-friendly interface and detailed documentation.
## Project Structure
```
HeartAttackPrediction/
│
├── data/ # Dataset files
├── notebooks/ # Jupyter notebooks for data analysis and modeling
├── Heart_Attack_test1/ # Source code for data processing and model training
├── Heart_Attack_70/ # Saved trained models
├── README.md # Project documentation
└── requirements.txt # Python dependencies
```
## Feature Engineering and Scaling
1. **Current Focus**:
- Identifying redundant or irrelevant features.
- Transforming features (e.g., normalization, log transformation).
- Encoding categorical variables.
2. **Scaling**:
- StandardScaler for numerical features.
- RobustScaler to handle outliers.
## Future Goals
- Improve accuracy to 90% or higher.
- Experiment with ensemble methods (e.g., Random Forest, Gradient Boosting).
- Deploy the model using a web app (e.g., Flask, FastAPI).
- Conduct hyperparameter tuning for further optimization.
## Installation
1. Clone this repository:
```bash
git clone https://github.com/yourusername/HeartAttackPrediction.git
cd HeartAttackPrediction
```
2. Install dependencies:
```bash
pip install -r requirements.txt
```
3. Run the model:
```bash
python src/train_model.py
```
## Usage
1. Add your dataset to the `data/` directory.
2. Use the provided Jupyter notebooks for data analysis and feature engineering.
3. Train the model with the command above or customize the pipeline as needed.
## Acknowledgments
- **Dataset Source**: [Include dataset link/source if applicable].
- **Contributions**: Feel free to contribute via pull requests or open issues for feedback and suggestions.
## License
This project is licensed under the [MIT License](LICENSE).