Mini project · Semester 5 · Computer Science and Engineering

AI-Based Student Performance Prediction System

Python, Flask, Machine Learning, Scikit-learn, Pandas, NumPy, Joblib, HTML, CSS

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THE IDEA

About this project

AI-Based Student Performance Prediction System is a Machine Learning web application that predicts a student's academic performance using factors such as attendance, study hours, previous marks, assignment score, internal marks, and sleep hours. The system uses a Random Forest Machine Learning model and provides the predicted performance along with prediction confidence.

What this project does

  • Student performance prediction using Machine Learning
  • Simple web-based user interface
  • Attendance-based analysis
  • Study-hours analysis
  • Previous marks consideration
  • Assignment and internal marks analysis
  • Sleep-hours consideration
  • Good / Needs Improvement prediction
  • Prediction confidence percentage
  • Random Forest classification model
  • Sample dataset included
  • Model training script included
  • Beginner-friendly Flask application

What you need

Python 3.9 or above
Flask
Pandas
NumPy
Scikit-learn
Joblib
Web browser
VS Code or any Python IDE

How to run this project

1. Extract the project ZIP file. 2. Open the project folder in VS Code. 3. Open Terminal. 4. Create virtual environment: python -m venv venv 5. Activate virtual environment: venv\Scripts\activate 6. Install required packages: pip install -r requirements.txt 7. Train the Machine Learning model: python train_model.py 8. Start the Flask application: python app.py 9. Open the following URL in your browser: http://127.0.0.1:5000/

Learn, build and make it yours

Read the code, try the features and change the project to match your idea. Make sure you understand how it works before using it for your coursework.

DOWNLOAD & START BUILDING

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