Student Score Prediction Project

About

This repository contains a project aimed at predicting students' average scores based on various demographic, socioeconomic, and performance-related features. The project includes exploratory data analysis (EDA), preprocessing, model training and evaluation, and a Flask-based web application for making predictions.

Features

1. Exploratory Data Analysis (EDA):

   • Analyzed trends using histograms and KDE plots.
   • Key insight: Female students tend to perform better than male students.

2. Data Preprocessing:

   • Checked for missing and duplicate values (none found).
   • Created new features:
     • total_score: Sum of scores in math, reading, and writing.
     • average_score: Average of the three subject scores.
   • Applied:
     • One-hot encoding for categorical features.
     • Standard scaling for numerical features using ColumnTransformer.

3. Model Training:

   • Used multiple regression models, including:
     • Linear Regression, Lasso, Ridge, KNN, Decision Tree, Random Forest, XGBoost, CatBoost, and AdaBoost.
   • Evaluated models using metrics such as RMSE, MAE, and R².
   • Saved the best-performing model for deployment.

4. Web Application:

   • Built using Flask, providing an interactive interface for users to input data and predict student scores.

5. Modular Development:

   • The project is structured in a modular manner to ensure scalability and maintainability:
     • src/pipeline: Contains utility scripts for exception handling, logging, and general functions.
     • src/components: Houses components for Data Ingestion, Data Transformation, and Model Training.