🎓 Student Performance & Risk Prediction (OULAD)

An end-to-end Machine Learning project utilizing the Open University Learning Analytics Dataset (OULAD) to predict student academic outcomes and identify at-risk students early in the semester.

🚀 Key Features

• Machine Learning Pipeline: Built and optimized robust predictive models (XGBoost and LightGBM) using Optuna for hyperparameter tuning.
• Explainable AI (XAI): Integrated SHAP (SHapley Additive exPlanations) values to provide transparent, interpretable explanations for model decisions at both global and local levels.
• Interactive Dashboard: Developed a comprehensive web application using Streamlit for Exploratory Data Analysis (EDA), model performance tracking, and real-time student risk prediction.

📁 How to Run Locally

Due to GitHub's file size restrictions, the raw dataset files (.csv, .zip) and heavy model history files are not included in this repository.

To set up and run the project on your local machine, please follow these steps:

1. Download the Data: Get the OULAD dataset from Kaggle.
2. Extract Files: Unzip the downloaded file and place all the .csv files directly into the root directory of this project.
3. Install Dependencies: Open your terminal and install the required Python packages by running:

   pip install -r requirements.txt