Student Performance Prediction Using Linear Regression

Project Overview

This project implements a machine learning model to predict student exam scores using Linear Regression. The system analyzes various factors including study hours, attendance, parental involvement, and other educational and environmental factors to provide accurate performance predictions. The model achieves high accuracy through comprehensive feature engineering and data preprocessing.

Technical Highlights

• Implementation using Python with scikit-learn, pandas, and numpy
• Linear Regression as the primary prediction model
• Extensive feature engineering
• Robust data preprocessing pipeline
• Comprehensive visualization using seaborn, matplotlib, and plotly
• Model evaluation using R² score and mean squared error
• Learning curve analysis for model performance assessment

Purpose and Applications

• Helps educators identify students who may need additional support
• Assists educational institutions in resource allocation
• Enables early intervention strategies for at-risk students
• Supports academic counselors in providing targeted guidance
• Facilitates data-driven decision making in educational planning
• Helps parents understand factors affecting their child's performance

Installation

# Clone the repository
git clone https://github.com/BhaveshBhakta/Student-Performance-Prediction-Using-ML.git
cd Student-Performance-Prediction-Using-ML

# Create and activate virtual environment
python -m venv venv
venv\Scripts\activate

# Install required packages
pip install -r requirements.txt
# Required packages:
# - pandas
# - numpy
# - scikit-learn
# - seaborn
# - matplotlib
# - plotly
# - warnings

Collaboration

Feel free to fork this repository, contribute, or open issues for suggestions and improvements!