🎓 Student Performance Prediction Project

📋 Project Overview

This project aims to predict student performance based on various features such as gender, race/ethnicity, parental level of education, lunch type, test preparation course, and scores in reading and writing. The project involves building a machine learning pipeline to process and analyze the data, and deploying the model using a Flask application.

✨ Features

• Automated Data Pipeline: End-to-end ML pipeline with data ingestion, transformation, and model training
• Multiple Model Evaluation: Trains and compares multiple regression models to select the best performer
• Docker Support: Containerized deployment for easy setup and reproducibility
• Scalable Architecture: Modular design for easy maintenance and extension
• Production Ready: Includes prediction pipeline for real-time inference

🚀 Installation

Prerequisites

• Python 3.8 or higher
• pip package manager
• Docker (optional, for containerized deployment)

Setup

1. Clone the repository

   git clone https://github.com/yourusername/Student-performance-prediction.git
   cd Student-performance-prediction

2. Install dependencies

   pip install -r requirements.txt

3. Run the pipeline

   python main.py

📁 Project Structure

.
├── data/                      # Raw and processed data
├── artifacts/                 # Trained models and preprocessing objects
├── src/
│   ├── components/
│   │   ├── data_ingestion.py      # Data loading and splitting
│   │   ├── data_transformation.py # Data preprocessing
│   │   ├── model_trainer.py       # Model training and evaluation
│   │   └── predict_pipeline.py    # Prediction pipeline
│   └── pipeline/
│       └── predict_pipeline.py    # Main prediction interface
├── app.py                     # Flask application
├── main.py                    # Main entry point
├── requirements.txt           # Python dependencies
├── Dockerfile                # Docker configuration
└── README.md                 # Project documentation

🔧 Components

1. Data Ingestion

The data_ingestion.py script reads the dataset, splits it into training and test sets, and saves them to the artifacts directory. It performs the following tasks:

• ✅ Reads the raw dataset
• ✅ Splits the data into training and test sets
• ✅ Saves the datasets for further processing

2. Data Transformation

The data_transformation.py script preprocesses the data by handling missing values, encoding categorical features, and scaling numerical features. It utilizes:

• ColumnTransformer for applying different preprocessing pipelines to numerical and categorical data
• Saves the preprocessing object to be used in the prediction pipeline

3. Model Trainer

The model_trainer.py script trains multiple regression models and selects the best one based on performance metrics. It includes:

• Models Trained: Random Forest, Decision Tree, Gradient Boosting, XGBRegressor, CatBoostRegressor, and AdaBoost Regressor
• Evaluation Metrics: R2 score, MSE, MAE
• Best Model Selection: Automatically selects and saves the best performing model

4. Prediction Pipeline

The predict_pipeline.py script handles the prediction process. It includes:

• ✅ Loading the trained model and preprocessing object
• ✅ Making predictions based on new input data
• ✅ Real-time inference capabilities

🐳 Docker Deployment

To simplify the deployment and management of the Student Performance Prediction project, a Docker image has been created. You can use this image to quickly run the project in a containerized environment.

Pull and Run

# Pull the Docker image
docker pull suraj0203/student_performance_prediction:latest

# Run the container
docker run -p 5000:5000 suraj0203/student_performance_prediction:latest

Docker Image Details

• Image Name: suraj0203/student_performance_prediction:latest
• Repository Link: Docker Hub Repository

Build from Source

# Build the Docker image
docker build -t student-performance-prediction .

# Run the container
docker run -p 5000:5000 student-performance-prediction

📊 Model Performance

The project evaluates multiple regression models and selects the best one based on R2 score. The following models are compared:

Model	Description
Random Forest	Ensemble learning method using multiple decision trees
Decision Tree	Simple tree-based regression model
Gradient Boosting	Boosting algorithm that builds models sequentially
XGBRegressor	Extreme Gradient Boosting implementation
CatBoostRegressor	Gradient boosting with categorical feature support
AdaBoost Regressor	Adaptive boosting algorithm

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

👨‍💻 Author

Suraj G Rao

• GitHub: @suraj0203

🙏 Acknowledgments

• Scikit-learn for the machine learning framework
• Flask for the web application framework
• Docker for containerization support