🎯 END-TO-END ML PROJECT

A comprehensive machine learning project implementing a complete pipeline for student performance prediction with data ingestion, transformation, model training, and deployment.

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📋 Project Overview

This project demonstrates a production-ready machine learning workflow, including:

• Data Ingestion 📥: Loading and processing raw data
• Data Transformation 🔄: Feature engineering and preprocessing
• Model Training 🤖: Building and training ML models
• Pipeline Architecture 🏗️: Modular and scalable design
• Web Application 🌐: Flask-based web interface for predictions
• Deployment Ready 🚀: Docker and Elastic Beanstalk configuration

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📁 Project Structure

ML Project/
├── 📄 app.py                          # Main Flask application
├── 📄 application.py                  # Alternative application entry point
├── 📄 setup.py                        # Package setup configuration
├── 📄 requirements.txt                # Project dependencies
├── 📄 README.md                       # Project documentation
│
├── 📂 src/                            # Source code package
│   ├── 📄 __init__.py
│   ├── 📄 exception.py               # Custom exception handling
│   ├── 📄 logger.py                  # Logging configuration
│   ├── 📄 utils.py                   # Utility functions
│   │
│   ├── 📂 components/                # Core ML components
│   │   ├── 📄 data_ingestion.py      # Data loading and ingestion
│   │   ├── 📄 data_transformation.py # Feature engineering & preprocessing
│   │   └── 📄 model_trainer.py       # Model training logic
│   │
│   └── 📂 pipeline/                  # ML pipelines
│       ├── 📄 train_pipeline.py      # Training pipeline
│       └── 📄 predict_pipeline.py    # Prediction pipeline
│
├── 📂 notebook/                       # Jupyter notebooks
│   ├── 📓 model_training.ipynb       # Model exploration & training
│   ├── 📓 stud_performance.ipynb     # Data analysis & visualization
│   └── 📂 data/
│       └── 📊 stud.csv               # Student dataset
│
├── 📂 templates/                      # Web UI templates
│   ├── 🎨 home.html                  # Home page
│   └── 🎨 index.html                 # Prediction form page
│
├── 📂 artifacts/                      # Saved models and data
│   ├── 🤖 model.pkl                  # Trained model
│   ├── 🔧 preprocessor.pkl           # Data preprocessor
│   ├── 📊 raw_data.csv               # Raw dataset
│   ├── 📊 train.csv                  # Training data
│   └── 📊 test.csv                   # Testing data
│
├── 📂 logs/                           # Application logs
│
├── 📂 .ebextension/                   # AWS Elastic Beanstalk config
│   └── 📄 python.config
│
└── 📂 Ml_Project.egg-info/            # Package metadata

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🔧 Key Components

Data Ingestion (src/components/data_ingestion.py)

• Loads raw data from CSV files
• Splits data into train and test sets
• Handles data validation and cleaning

Data Transformation (src/components/data_transformation.py)

• Feature engineering and selection
• Preprocessing (scaling, encoding, etc.)
• Creates pipelines for reproducibility

Model Training (src/components/model_trainer.py)

• Trains multiple ML algorithms
• Hyperparameter tuning
• Model evaluation and selection
• Saves best model artifacts

Pipelines (src/pipeline/)

• train_pipeline.py: Orchestrates the entire training workflow
• predict_pipeline.py: Handles inference on new data

Web Application (app.py, application.py)

• Flask-based REST API
• Interactive web interface
• Real-time predictions

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🚀 Getting Started

Prerequisites

• Python 3.x
• pip and conda environment

Installation

1. Clone the repository

   git clone <repository-url>
   cd ML\ Project

2. Create a virtual environment

   python -m venv .venv
   .venv\Scripts\activate  # On Windows
   source .venv/bin/activate  # On Linux/Mac

3. Install dependencies

   pip install -r requirements.txt

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📊 Data

The project uses student performance data (stud.csv):

• Raw Data: artifacts/raw_data.csv
• Training Set: artifacts/train.csv
• Testing Set: artifacts/test.csv

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🏃 Running the Project

Train the Model

python -m src.pipeline.train_pipeline

Run the Web Application

python app.py

Then navigate to http://localhost:5000 in your browser.

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📦 Deployment

Docker

docker build -t ml-project .
docker run -p 5000:5000 ml-project

AWS Elastic Beanstalk

Configuration files are provided in .ebextension/ for easy deployment.

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🛠️ Utility Functions

• exception.py: Custom exception handling for better error management
• logger.py: Structured logging for debugging and monitoring
• utils.py: Helper functions for common tasks

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📈 Project Workflow

Raw Data → Ingestion → Transformation → Training → Evaluation → Deployment
   📊        📥          🔄             🤖         📈         🌐

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✨ Features

• ✅ Modular and scalable architecture
• ✅ Production-ready code structure
• ✅ Comprehensive error handling
• ✅ Logging and monitoring
• ✅ Web interface for predictions
• ✅ Docker support
• ✅ Cloud deployment ready

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👤 Author

Created an end-to-end ML project lifecycle.

• ✅ Name: Bibek Gupta
• ✅ Email: bibekg1406@gmail.com

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Last Updated: January 2026