This project focuses on predicting the performance of high school students in mathematics based on various factors, such as demographic information and educational background. The dataset used in this project contains information collected from three high schools in the United States.

The motivation behind this project is to explore the relationship between various factors and student performance. This project also aims to build a model that can predict the performance of a student based on these factors.

• Python 3.8.5
• Jupyter Notebook
• NumPy
• Pandas
• Matplotlib
• Seaborn
• scikit-learn

The dataset used in this project is named exams.csv and is located in the data/ directory. It contains the following columns:

• Gender: The gender of the student (male/female)
• Race/ethnicity: The student's racial or ethnic background (Asian, African-American, Hispanic, etc.)
• Parental level of education: The highest level of education attained by the student's parent(s) or guardian(s)
• Lunch: Whether the student receives free or reduced-price lunch (yes/no)
• Test preparation course: Whether the student completed a test preparation course (yes/no)
• Math score: The student's score on a standardized mathematics test
• Reading score: The student's score on a standardized reading test
• Writing score: The student's score on a standardized writing test

The project follows the following directory structure:

student-performance-prediction/
├── data/
│   └── exams.csv
├── notebooks/
│   ├── exploratory_analysis.ipynb
│   └── model_training.ipynb
├── .gitignore
├── README.md
└── requirements.txt

1. Start by cloning the repository:

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

2. Create a virtual environment and activate it:

python3 -m venv env
source env/bin/activate

3. Install the required dependencies:

pip install -r requirements.txt

4. Start the Jupyter server:

jupyter notebook

5. Open the notebooks/ directory and run the notebooks in order.

Contributions to this project are welcome! If you have any suggestions, bug reports, or feature requests, please open an issue or submit a pull request.

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