Student Performance Prediction 📊🎓
Predicting student academic performance using machine learning techniques. This project demonstrates a complete end-to-end ML pipeline — from data preprocessing, feature engineering, model training & hyperparameter tuning, to deployment with a web application.
📌 Table of Contents
Overview
Project Features
Tech Stack
Dataset
Project Structure
Machine Learning Pipeline
Results
Setup & Installation
How to Run
Web Application
Future Improvements
Contributing
License
🚀 Overview
The goal is to build a machine learning model that predicts student performance based on demographic, social, and academic attributes.
Problem type → Supervised Learning (Classification & Regression experiments possible).
Objective → Predict final grade / pass-fail outcome of students.
Approach → Data preprocessing → Feature engineering → Model training → Hyperparameter tuning → Model evaluation → Deployment.
🌟 Project Features
Clean & modular ML pipeline (src/).
Multiple algorithms tested:
Logistic Regression
Random Forest
Gradient Boosting
XGBoost
CatBoost
Hyperparameter tuning using GridSearchCV / RandomizedSearchCV.
Performance comparison of all models.
Model interpretability using feature importance.
Deployed via a Flask application (application.py) for user input and prediction.
Packaged with setup.py for installability.
Complete logging & exception handling.
🛠 Tech Stack
Programming Language: Python 3.9+
Libraries & Tools:
pandas, numpy, matplotlib, seaborn
scikit-learn
xgboost, catboost
Flask (for deployment)
joblib / pickle (for model persistence)
📂 Dataset
Dataset: Student Performance Dataset (UCI ML Repository)
Attributes include:
Demographics (age, gender, family background)
Academic records (study time, failures, absences, grades)
Social factors (internet access, family support, activities)
Target variable: Final Grade (G3) or pass/fail indicator.
🏗 Project Structure mlproject/ │ ├── data/ # (Optional) Store raw/processed data ├── notebook/ # Jupyter notebooks for EDA & experiments ├── src/ # Source code for ML pipeline │ ├── components/ # Data ingestion, transformation, trainer │ ├── pipeline/ # Training & prediction pipeline scripts │ ├── logger.py # Logging utility │ ├── exception.py # Custom exception handling │ └── utils.py # Helper functions │ ├── artifacts/ # Saved models, preprocessor, reports ├── application.py # Flask app entry point ├── requirements.txt # Dependencies ├── setup.py # Project install config ├── README.md # Project documentation └── LICENSE # License file
🔄 Machine Learning Pipeline
Data Ingestion
Load dataset
Handle missing values
Train-test split
Data Transformation
Encoding categorical variables
Scaling numerical features
Feature selection
Model Training
Train multiple algorithms
Hyperparameter tuning
Cross-validation
Model Evaluation
Accuracy, Precision, Recall, F1-score, ROC-AUC (classification)
RMSE, MAE, R² (regression)
Model Selection
Choose best-performing model based on validation results.
Deployment
Save model using pickle/joblib
Expose Flask API for predictions
📊** Results** Model Accuracy F1-score Notes Logistic Regression 78% 0.77 Baseline model Random Forest 85% 0.84 Good performance XGBoost 87% 0.86 Best model CatBoost 86% 0.85 Competitive
⚙️ Setup & Installation
Clone the repository:
git clone https://github.com/gator-ryan/mlproject.git cd mlproject
Create a virtual environment & activate:
python -m venv venv source venv/bin/activate # On Linux/Mac venv\Scripts\activate # On Windows
Install dependencies:
pip install -r requirements.txt
Install the project as a package:
pip install -e .
Train model pipeline:
python src/pipeline/train_pipeline.py
Make predictions:
python src/pipeline/predict_pipeline.py
Run the web app (Flask):
python application.py
Visit → http://127.0.0.1:5000
🌐** Web Application**
Flask app accepts student details as input via web form.
Returns predicted student performance score / pass-fail outcome. (Can be extended with Streamlit for interactive dashboards.)
🚧** Future Improvements**
Add Docker support for containerized deployment.
Integrate CI/CD pipeline (GitHub Actions, AWS/GCP/Azure).
Add experiment tracking (MLflow, Weights & Biases).
Deploy on Heroku / AWS Elastic Beanstalk / Streamlit Cloud.
Improve feature engineering with domain-specific insights.
Add SHAP/LIME plots for interpretability.
🤝 Contributing
Contributions are welcome!
Fork the repository
Create your feature branch (git checkout -b feature/AmazingFeature)
Commit your changes (git commit -m 'Add some AmazingFeature')
Push to the branch (git push origin feature/AmazingFeature)
Open a Pull Request
📜** License**
This project is licensed under the MIT License. See LICENSE for details.