🎓 Student Performance Prediction

This is a machine learning project to predict students' CGPA using a trained regression model. It allows data entry through a Streamlit interface and gives users performance predictions based on a variety of academic and personal input factors.

✅ Features

• Linear Regression and Random Forest model options
• User authentication (Sign up / Log in)
• Visual analytics (bar chart, pair plots, line/area plots)
• Prediction results with class category
• Error evaluation (MAE, MSE)
• Clean and interactive interface with Streamlit

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🖼️ Screenshots

🔷 Home Page

A simple entry interface on Streamlit

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🔐 Authentication

Login and signup options

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📊 Dashboard with Dataset View

Browse raw GPA and background data

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📉 Pairplots and Distributions

Relationships between various features

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📝 Input Form for Prediction

Users enter academic and personal data

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📋 Output Prediction

Final CGPA prediction with interpretation

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📈 Additional Visualizations

Bar chart of CGPA distribution

Area chart showing multiple variable trends

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🧠 Core Logic & Model Code

Model Imports & Setup

Feature Dictionaries

Used to map text input to numeric values

Prediction Input Section

Streamlit form that captures prediction inputs

Random Forest Prediction Logic

Model training, evaluation, and output

User Login System Using SQLite

Authentication backend

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⚙️ Tech Stack

• Frontend: Streamlit
• Backend: Python (scikit-learn, pandas, SQLite)
• Models: Linear Regression, Random Forest Classifier
• Visualization: Matplotlib, Seaborn

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🧪 Model Details

• Inputs: GPA from previous years, exam results, personal habits, access to resources, etc.
• Output: Predicted CGPA (out of 5.0)
• Target Label Classes:
  • First Class
  • Second Class Upper
  • Second Class Lower
  • Pass
  • Fail

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📌 Conclusion

This was my first machine learning project. I improved it bit by bit. I plan to share more updates and refinements soon.