rahulg-101 / end-to-end-student-performance-analysis-and-prediction

End to End Student Performance Analysis and Prediction

This project aims to analyze and predict student performance based on various factors such as gender, ethnicity, parental education level, lunch type, and test preparation course. The project employs machine learning techniques to build predictive models and provide insights into the factors influencing student performance.

Dataset

The dataset used in this project is obtained from Kaggle and contains information about students' gender, ethnicity, parental education level, lunch type, test preparation course, and scores in math, reading, and writing.

Getting Started

To run this project locally, follow these steps:

1. Clone the repository:

git clone https://github.com/your-username/student-performance-prediction.git

2. Install the required dependencies:

pip install -r requirements.txt

3. Run the Flask app:

python app.py

Access the web application at http://localhost:5000.

Deployment The Flask app is deployed on the following platforms:

• AWS EC2
• Azure

Contributing

Contributions to this project are welcome. If you find any issues or have suggestions for improvements, please open an issue or submit a pull request. License

This project is licensed under the MIT License.