Predicting Student Performance

Overview

This project focuses on predicting student performance based on various factors such as demographics, study habits, and socioeconomic background. The model utilizes machine learning techniques to analyze student data and provide insights into their academic outcomes.

Features

• Data preprocessing and feature engineering
• Machine learning model training and evaluation
• Performance metrics visualization
• Predictive analysis based on student data

Installation

1. Clone this repository:

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

2. Navigate to the project directory:

   cd predicting-student-performance

3. Install the required dependencies:

   pip install -r requirements.txt

Usage

1. Run the notebook to preprocess data and train the model.
2. Modify parameters as needed to improve model accuracy.
3. Analyze results and generate insights.

Technologies Used

• Python
• Jupyter Notebook
• Scikit-learn
• Pandas
• NumPy
• Matplotlib/Seaborn

License

This project is licensed under the MIT License. See the LICENSE file for details.

Contributing

Contributions are welcome! Feel free to fork the repository and submit pull requests with improvements.

Author

Mariam Badr - GitHub Profile