The Student Passing Performance Prediction project is an academic initiative designed to predict whether a student will pass their final exam based on a variety of personal, academic, and lifestyle factors. Utilizing a Support Vector Machine (SVM) with a linear kernel, the system provides a probability score and categorizes the likelihood of passing into Low (<40%), Medium (40-70%), or High (>70%). Built with Flask, the web interface allows users to input 30 features and receive real-time predictions. The project leverages the UCI Student Performance Dataset and integrates Python libraries such as scikit-learn, pandas, and matplotlib for modeling and visualization.
This project uses an SVM model to predict student exam success based on personal, academic, and lifestyle factors, offering actionable insights for educators.
- Assist educators and students in identifying at-risk students early.
- Enable targeted interventions to improve academic outcomes.
- Provide a user-friendly web interface for predictions using machine learning.
- Prediction Model: SVM with linear kernel for percentage + category (low < 40%, medium (40-70%), high > 70%).
- Web Interface: Flask-based form to input 30 student features.
- Real-Time Results: Probability scores and category assignments (Low, Medium, High).
- Data Visualization: Plots for feature importance, ROC curves, and confusion matrices.
- Dataset: UCI Student Performance Dataset with 395 students and 31 columns.
- Python 3.8 or higher
- Git (for cloning the repository)
- Internet connection (for installing dependencies)
- Clone the Repository
git clone https://github.com/sohailahmed/student-passing-performance-prediction.git cd student-performance-prediction