End to End Machine Learning Project

Student Performance Prediction

This project focuses on predicting student performance in math exams based on various demographic and academic factors. It employs a machine learning approach, leveraging historical student data to build and train a predictive model.

Dataset Variables

Variable	Description
gender	Student's gender (male/female)
race_ethnicity	Student's racial or ethnic group (group A-E)
parental_level_of_education	Parent's education level
lunch	Lunch type (free/reduced, standard)
test_preparation_course	Completion of test preparation course (none, completed)
reading_score	Standardized reading test score (out of 100)
writing_score	Standardized writing test score (out of 100)
math_score	Target variable: Standardized math test score (out of 100)

Project Highlights

• End-to-End ML Pipeline: Includes data ingestion, transformation, model training, evaluation, and deployment using Flask.
• Diverse Model Evaluation: Explores and compares various regression models:
  • Linear Regression
  • Lasso
  • Ridge
  • Decision Tree
  • Random Forest
  • XGBoost
  • CatBoost
  • AdaBoost
  • Gradient Boosting.
• Hyperparameter Tuning: Employs GridSearchCV to optimize model parameters for enhanced performance.
• Flask Web App: Deploys a user-friendly web application allowing users to input student information and receive predicted math scores.

Project Structure

├── .ebextensions
│  └── python.config
├── artifacts
│   ├── data.csv
│   ├── model.pkl
│   ├── preprocessor.pkl
│   ├── test.csv
│   └── train.pkl
├── notebook
│   ├── data
│   │   └── stud.csv
│   ├── 1. EDA STUDENT PERFORMANCE.ipynb
│   └── 2. MODEL TRAINING.ipynb
├── src
│   ├── components
│   │   ├── _init_.py
│   │   ├── data_ingestion.py
│   │   ├── data_transformation.py
│   │   └── model_trainer.py
│   ├── pipeline
│   │   ├── _init_.py
│   │   └── prediction_pipeline.py
│   ├── _init_.py
│   ├── exception.py
│   ├── logger.py
│   └── utils.py
├── templates
│   ├── home.html
│   └── index.html
├── .gitignore
├── application.py
├── requirements.txt
└── setup.py

How to Run

1. Run the Flask App:

   python application.py

2. Access the Application:

   Open a web browser and navigate to http://127.0.0.1:5000/