End to End Data Science Project: Student Performance Indicator

Overview

This project predicts students' math scores. It covers the entire process from data ingestion and transformation, to model training and deployment.

Tools and Technologies

• Conda: For environment management.
• Git: Version control.
• MySQL: Database.
• GitHub: Code hosting.
• DVC: Data version control.
• MLflow: Model lifecycle management.
• Flask: Model Deployment.
• Dagshub: Data and experiment management.
• AWS CodePipeline: Continuous integration and delivery service for fast and reliable application and infrastructure updates.
• AWS Elastic Beanstalk: Easy-to-use service for deploying and scaling web applications and services.

Project Structure

• app.py: Flask application script for deploying the model using flask and handling user inputs.
• application.py: Flask application script for deploying the model on AWS Elastic Beanstalk.
• prediction_pipeline.py: Scripts for making predictions based on user input.
• data_ingestion.py: Scripts for data ingestion and preprocessing.
• data_transformation.py: Scripts for data transformation.
• model_trainer.py: Scripts for model training.
• notebooks: Jupyter notebooks for analysis and model training experimentation.
• utils.py: Utility scripts.
• requirements.txt: Project dependencies.
• artifacts: Stores generated artifacts such as model performance reports and trained models.

Setup Instructions

1. Clone the repository:

   git clone https://github.com/ajitmane36/End-to-End-Student-Math-Score-Prediction-with-Deployment.git
   cd mlproject

2. Create a Conda environment:

   conda create --name student-performance python=3.9
   conda activate student-performance

3. Install dependencies:

   pip install -r requirements.txt

4. Run the Flask application:

   python app.py

5. Navigate to the application in your browser:

   # Home Page
   http://127.0.0.1:5000
   
   # Prediction Page
   http://127.0.0.1:5000/predictdata

Usage

1. Home Page:

   • Access the home page to enter student data.
   • Submit the form to get predicted math scores.

2. Prediction Pipeline:

   • Handles user input and processes it through the trained model to provide predictions.

Model Deployment

Using Flask