Student Grades Prediction Project

Overview

This project aims to predict a student's final grade (G3) based on various factors such as midterm grades (G1 and G2), study time, failures, and absences using a dataset from Kaggle. Initially, a Linear Regression model was used for prediction, and later, a Random Forest model was implemented for potentially improved performance.

Files and Directories

• pyproject.toml: Configuration file for project dependencies and settings.
• student.csv: Dataset containing student information and grades.
• student_grades_prediction_linear_regression.ipynb: Jupyter notebook for Linear Regression model implementation and evaluation.
• student_grades_prediction_linear_regression.py: Python script for Linear Regression model implementation.
• student_grades_prediction_random_forest.ipynb: Jupyter notebook for Random Forest model implementation and evaluation.
• student_grades_prediction_random_forest.py: Python script for Random Forest model implementation.

Getting Started

1. Install Poetry for dependency management: Poetry Installation Guide.
2. Run poetry install to install the project dependencies.
3. Ensure you have the necessary dataset (student.csv) in the project directory.

Dependencies

• pandas: Data manipulation and analysis library in Python.
• numpy: Numerical computing library for handling arrays and matrices.
• scikit-learn: Machine learning library for building and evaluating models.

Notes

• The Linear Regression model is a statistical approach to modeling the relationship between the independent variables and the target variable. It assumes a linear relationship.
• The Random Forest model is an ensemble learning method that uses multiple decision trees to make predictions.
• Both models are evaluated using metrics including R-squared, Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE).