Student Performance Predictor

A Flask web application that predicts student exam scores based on various academic and personal factors using machine learning. Live at https://studentsmarksprediction-x.onrender.com/

Features

• Predict exam scores based on comprehensive student data
• User-friendly web interface for input
• Machine learning model trained on extensive student performance dataset
• Real-time predictions with detailed results

Input Features

The model considers the following factors:

• Age
• Gender
• Study Hours per Week
• Preferred Learning Style
• Online Courses Completed
• Participation in Discussions
• Assignment Completion Rate
• Attendance Rate
• Use of Educational Technology
• Self-Reported Stress Level
• Time Spent on Social Media
• Sleep Hours per Night

Local Development

1. Clone the repository
2. Create a virtual environment: python -m venv venv
3. Activate the virtual environment: venv\Scripts\activate (Windows)
4. Install dependencies: pip install -r requirements.txt
5. Run the app: python app.py

Deployment on Render

1. Connect your GitHub repository to Render
2. Create a new Web Service
3. Set the following:
   • Runtime: Python 3 (Render will use runtime.txt)
   • Build Command: pip install -r requirements.txt
   • Start Command: gunicorn app:app
4. Deploy!

Note: The runtime.txt file specifies Python 3.11.9 for compatibility.

Technologies Used

• Flask - Web framework
• scikit-learn - Machine learning
• pandas - Data manipulation
• joblib - Model serialization

Model Performance

The linear regression model provides accurate predictions based on historical student performance data.