Student Performance Prediction API

Machine learning based REST API for predicting student academic performance using FastAPI and Scikit-learn.

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Features

• Built a machine learning based REST API using FastAPI

• Predicts student academic performance using structured educational data

• Uses Random Forest Regression for score prediction

• Supports real-time prediction requests

• Returns JSON-based prediction responses

• Includes interactive Swagger API documentation

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Tech Stack

• Python

• FastAPI

• Scikit-learn

• Pandas

• Matplotlib

• Joblib

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API Documentation

Swagger UI

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Prediction Endpoint

POST /predict

Sample Input

{

  "weekly_self_study_hours": 16,

  "attendance_percentage": 85,

  "class_participation": 7

}

Sample Response

{

  "predicted_score": 89.17

}

Prediction Response Screenshot

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Project Structure

student-performance-prediction-api

│

├── notebook

│   └── Student_Performance_Prediction_Project.ipynb

│

├── screenshots

│   ├── api-docs.png

│   └── prediction-response.png

│

├── [app.py](http://app.py)

├── train_[model.py](http://model.py)

├── model.pkl

├── student_performance.csv

├── [README.md](http://README.md)

└── .gitignore

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Run Locally

Clone Repository

git clone [https://github.com/asthasinghcs/student-performance-prediction-api.git](https://github.com/asthasinghcs/student-performance-prediction-api.git)

Install Dependencies

pip install fastapi uvicorn scikit-learn pandas matplotlib joblib

Run API Server

uvicorn app:app --reload

Open API Docs

[http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs)

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Model Information

• Model Used: Random Forest Regressor

• Features:

  • weekly_self_study_hours

  • attendance_percentage

  • class_participation

• Target:

  • total_score

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Future Improvements

• Model deployment on cloud platforms

• Frontend integration

• Better feature engineering

• Hyperparameter tuning

• Authentication and API security