OlympiVista is a data-driven web application that aims to identify and predict the rising stars among Olympic athletes. The project utilizes advanced data science techniques, including Logistic Regression, data preprocessing with pandas, and the power of web technologies like React, Node.js, and MongoDB to create an efficient and user-friendly platform.
- Python with Logistic Regression for prediction.
- Pandas for data preprocessing.
- Flask API for integrating the prediction model.
- React and Node.js for building the website's frontend and backend.
- MongoDB for storing and managing athlete data.
- Numpy for efficient mathematical operations.
- Display athlete information, achievements, and potential on the website.
- Allow users to input specific criteria to find potential rising stars.
- Predict and rank athletes using the Logistic Regression model.
- Efficient data retrieval and storage with MongoDB.
If you are interested in contributing to this project, you can:
Fork the repository and create a new branch for your work. Submit pull requests for any improvements or bug fixes. Open issues for any new features or enhancements you'd like to see.
Ashok: Computer Engineering student at GEC Bhavnagar, pursuing a career in data science. Satyam: Information Technology student at GEC Bhavnagar, pursuing a career in full-stack development.
 Satyam |
 Ashok |