Interactive Student Performance Dashboard & Prediction

📌 Project Overview

This project is an end-to-end machine learning web application built with Streamlit to analyze and predict student academic performance. Using the xAPI-Edu-Data dataset, the app identifies key behavioral factors (such as class participation and resource visitation) that influence student grades. It combines unsupervised learning for student segmentation with supervised learning for grade prediction.

🛠 Tech Stack & Tools

• Framework: Streamlit (for web app interface)
• Data Analysis: Pandas, NumPy
• Visualization: Plotly, Seaborn, Matplotlib
• Machine Learning: Scikit-learn (Decision Tree, Random Forest, K-Means, PCA)
• Statistical Analysis: Statsmodels (VIF for feature selection)

⚙️ Key Features & Methodology

1. Interactive Data Exploration (EDA):
   • Real-time visualization of feature distributions (Histograms, Boxplots) and correlations.
   • Automated handling of missing values and outlier detection/capping.
2. Feature Engineering:
   • Applied Variance Inflation Factor (VIF) analysis to remove multicollinear features.
   • Encoded categorical variables using One-Hot and Label Encoding.
3. Unsupervised Learning (Clustering):
   • Implemented K-Means Clustering to group students based on behavioral patterns.
   • Utilized PCA (Principal Component Analysis) for dimensionality reduction and 2D visualization of clusters.
4. Predictive Modeling:
   • Trained Decision Tree and Random Forest classifiers to predict student grades (Class L, M, H).
   • Evaluated models using Accuracy, Classification Reports, and Confusion Matrices.

🚀 How to Run

pip install -r requirements.txt
streamlit run app.py