🎓 Student Performance Analysis System

📌 Overview

The Student Performance Analysis System is a full-stack web application designed to analyze and predict student academic performance using machine learning techniques. The system uses a Random Forest classifier to predict student grades based on academic performance indicators, helping educators identify students who may require additional academic support.

The project integrates a Python-based backend for data processing, model training, and prediction with a React-based frontend for interactive visualization and result display.

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🚀 Features

• Predicts student grades based on academic performance indicators
• Machine learning model integration for performance analysis
• Interactive and user-friendly frontend
• Backend handles data preprocessing and prediction logic
• Visual representation of student performance

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🛠️ Tech Stack

Frontend

• React.js
• HTML5
• CSS3
• JavaScript

Backend

• Python
• Flask / FastAPI

Machine Learning & Data Processing

• Random Forest (Scikit-learn)
• Pandas
• NumPy

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📂 Project Structure

Student_Performance_Analysis/
│
├── backend/           # Backend APIs and ML model logic
├── front_end/         # React frontend application
├── README.md          # Project documentation
├── .gitignore

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⚙️ How It Works

1. Academic performance data is submitted through the frontend.
2. The backend preprocesses the data.
3. A Random Forest machine learning model is trained on historical data and used to predict student performance.
4. Results are returned to the frontend.
5. The frontend displays predictions and insights visually.

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▶️ Getting Started

Prerequisites

• Python 3.x
• Node.js and npm
• Git

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Backend Setup

cd backend
pip install -r requirements.txt
python app.py

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Frontend Setup

cd front_end
npm install
npm start

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📊 Use Cases

• Academic performance monitoring

• Early identification of at-risk students

• Educational analytics and reporting

• Machine learning-based decision support

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🔮 Future Enhancements

• User authentication and role-based access

• Advanced ML models for improved accuracy

• Real-time performance analytics

• Cloud deployment

• Enhanced dashboards and visualizations

Clone the Repository

git clone https://github.com/ashwinraj8090/Student_Performance_Analysis.git
cd Student_Performance_Analysis

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📜 License

This project is licensed under the MIT License.