End-to-End ML Project: Student Exam Performance

Predict student math scores from demographic and academic inputs using a trained model and a simple Flask web UI.

What is in this repository

• A Flask app that serves a prediction form and result page.
• A trained model and preprocessor in artifacts/ used for inference.
• Modular pipeline code in src/ for data processing and prediction.
• Notebooks for EDA and model training in notebook/.

Project structure

• application.py - Flask app entry point (default port 5000).
• app.py - Flask app entry point with debug on port 8000.
• templates/ - HTML templates for the UI.
• src/ - Pipeline, components, utilities, and exception handling.
• artifacts/ - Model, preprocessor, and sample datasets.
• notebook/ - EDA and training notebooks.

Quickstart

1. Create and activate a virtual environment (optional).
2. Install dependencies:

   pip install -r requirements.txt

3. Run the web app:

   python application.py

   Then open http://localhost:5000 in your browser.

If you prefer debug mode and port 8000:

python app.py

Prediction inputs

The app expects the following fields:

• Gender
• Race or ethnicity
• Parental level of education
• Lunch type
• Test preparation course
• Reading score (0-100)
• Writing score (0-100)

Notes

• Prediction uses artifacts/model.pkl and artifacts/preprocessor.pkl via src/pipeline/predict_pipeline.py.
• If you retrain or replace artifacts, keep the filenames the same or update the pipeline accordingly.

Notebooks

The notebooks in notebook/ cover exploratory data analysis and model training. They can be used to reproduce or refine the model.

https://github.com/JavierPachas/e2eproject/blob/main/img/home.png