Machine Learning project (student score prediction)

🎓 Student Score Prediction System

An end-to-end Machine Learning project that predicts student academic performance based on multiple input features.
This repository demonstrates a production-oriented ML workflow, covering data analysis, model training, hyperparameter tuning, and deployment readiness.

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📌 Project Objective

The goal of this project is to build a reliable regression model that can predict student scores using structured educational data.
The project is designed to follow real-world machine learning engineering practices, not just exploratory notebook-based work.

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🚀 Key Highlights

• Modular and scalable project structure
• End-to-end ML pipeline implementation
• Data preprocessing and feature engineering
• Model training with hyperparameter tuning
• Artifact generation for trained models
• Deployment-ready application setup
• Docker support for containerized deployment
• CI/CD workflow integration

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🧠 Machine Learning Workflow

1. Exploratory Data Analysis (EDA)

   • Understanding feature distributions and relationships

2. Data Transformation

   • Handling missing values
   • Encoding categorical variables
   • Feature scaling

3. Model Training

   • Training multiple regression models
   • Performance evaluation using standard metrics

4. Hyperparameter Tuning

   • Selecting optimal parameters for best-performing model

5. Prediction Pipeline

   • Generating predictions on unseen data

6. Deployment Preparation

   • Application entry point and Docker configuration

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🗂️ Project Structure

STUDENT-SCORE-PREDICTION
│
├── .ebextensions/            # Deployment configuration files
├── .github/workflows/        # CI/CD workflows
├── artifacts/                # Stored models and preprocessors
├── catboost_info/            # Training logs and metadata
├── mlenv/                     # Virtual environment (local)
├── notebook/
│   ├── data/                 # Dataset used for training
│   ├── 1. EDA STUDENT PERFORMANCE.ipynb
│   └── 2. MODEL TRAINING.ipynb
├── src/                      # Core source code
├── templates/                # HTML templates (if applicable)
├── application.py            # Application entry point
├── Dockerfile                # Docker configuration
├── requirements.txt          # Python dependencies
├── setup.py                  # Package setup file
└── README.md                 # Project documentation