Student Exam Score Prediction with Linear Regression

This repository contains a simple linear regression model developed using the scikit-learn package to predict students' exam scores based on various attributes. The model is trained on two datasets: student-mat.csv (Math course) and student-por.csv (Portuguese language course).

Dataset Features

Both datasets share the following attributes:

• school: Student's school ('GP' - Gabriel Pereira or 'MS' - Mousinho da Silveira)
• sex: Student's sex ('F' - female or 'M' - male)
• age: Student's age (numeric: from 15 to 22)
• address: Student's home address type ('U' - urban or 'R' - rural)
• famsize: Family size ('LE3' - less or equal to 3 or 'GT3' - greater than 3)
• Pstatus: Parent's cohabitation status ('T' - living together or 'A' - apart)
• Medu: Mother's education (numeric: 0 - none, 1 - primary education to 4 - higher education)
• Fedu: Father's education (numeric: 0 - none, 1 - primary education to 4 - higher education)
• Mjob: Mother's job (nominal: 'teacher', 'health', 'services', 'at_home', 'other')
• Fjob: Father's job (nominal: 'teacher', 'health', 'services', 'at_home', 'other')
• ... (and more features)

Course Grades (Targets)

The scores G1, G2, and G3 are the target variables for prediction, related to the course subject (Math or Portuguese):

• G1: First period grade (numeric: from 0 to 20)
• G2: Second period grade (numeric: from 0 to 20)
• G3: Final grade (numeric: from 0 to 20, output target)

Dataset Source

The datasets used in this project are available from the UCI Machine Learning Repository. Find the dataset here.

Usage

The Jupyter Notebook files provided in this repository demonstrate the steps involved in data preprocessing, model training using linear regression, and evaluation of the predictive performance. The student-mat.csv and student-por.csv datasets are included for reference.

Requirements

To run the code, ensure you have:

• Python installed
• JuPyter Notebooks
• Necessary libraries: sklearn, NumPy, pandas, matplotlib, and seaborn

About This Project

This project serves as personal training and learning in machine learning and data analysis, focusing on predicting students' exam scores.

How to Contribute

Contributions, feedback, and suggestions are welcome! Feel free to open issues or pull requests.