📘 AI-powered EduPredict – Student Performance and Analytics System

📌 Introduction

EduPredict is an AI-powered system designed to analyze student academic and behavioral data to predict performance outcomes such as Pass/Fail classification, dropout risk, and overall academic trends. By leveraging Machine Learning models, the system provides institutions and educators with early intervention insights, helping improve student success rates.

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🎯 Objectives

• Predict student performance based on academic and behavioral attributes.
• Identify patterns and risk factors that affect academic success.
• Provide educators with actionable insights to support struggling students.
• Develop a user-friendly analytics dashboard for visualization.

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✨ Features

• 📊 Performance Prediction – Predicts student outcomes (pass/fail or grades).
• 🧠 Machine Learning Models – Uses classification algorithms like Random Forest, Logistic Regression, and XGBoost.
• 🔍 Analytics Dashboard – Visualizes trends, failure risk, and subject-wise performance.
• 📈 SHAP-based Explainability – Explains why a prediction was made.
• 📂 Custom Dataset Handling – Works with student academic and behavioral datasets.

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📂 Dataset

The dataset includes:

• Student academic scores (subject-wise marks, GPA, previous performance).
• Attendance records.
• Behavioral attributes (participation, assignments, activities).
• Target label: Pass/Fail or Grade classification.

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🛠️ Tech Stack

• Programming Language: Python 🐍
• Libraries: Pandas, NumPy, Scikit-learn, XGBoost, SHAP, Matplotlib, Seaborn
• Visualization: Streamlit (interactive dashboard)
• Database: SQLite / CSV for dataset storage
• Version Control: Git & GitHub

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🚀 Installation & Usage

1️⃣ Clone the repository

git clone https://github.com/Vivekchary2607/AI-Powered-EduPredict-Student-Performance-Analytics-System.git)
cd edupredict

2️⃣ Create virtual environment & install dependencies

python -m venv venv
# Activate environment
source venv/bin/activate   # for Linux/Mac
venv\Scripts\activate      # for Windows

# Install requirements
pip install -r requirements.txt

3️⃣ Run the Streamlit app

streamlit run app.py

4️⃣ Upload dataset & start predictions 🎓

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📊 Results

• Achieved 96% accuracy using Random Forest.

• SHAP values highlighted key factors: subject performance, attendance, and assignment completion.

• Provided interpretable insights for educators to take timely action.

Copyright (c) 2025 G Vivek chary

All rights reserved.

This project is proprietary and confidential. Unauthorized copying, modification, distribution, or use of any part of this codebase is strictly prohibited without express written permission from the author.

You may view the code for educational purposes only. Commercial use, redistribution, or publication is not allowed.

Contact the me for collaboration or licensing inquiries.