Predict student outcomes using regression and classification models.
This project analyzes student data to predict grades, performance categories, and identify factors affecting success.
- Regression: Predicts numerical grades (e.g., final exam score).
- Classification: Predicts categorical performance levels (e.g., High, Medium, Low).
- Handles dataset preprocessing, feature engineering, and model evaluation.
- Supports multiple algorithms (e.g., Linear Regression, Decision Trees, Random Forest, Logistic Regression).
- Generates visualizations for data insights.
- Python 3.8+
- Packages in
requirements.txt(pandas, numpy, scikit-learn, matplotlib, seaborn, etc.)
# Clone the repository
git clone <your-repo-link>
cd StudentPerformance
# Create virtual environment
python -m venv venv
source venv/bin/activate # Windows: venv\Scripts\activate
# Install dependencies
pip install -r requirements.txt