Student Maths Score Prediction

Introduction

1. Problem Statement: This project aims to understand how various factors such as Gender, Ethnicity, Parental level of education, Lunch, and Test preparation course influence students' performance in tests, specifically math scores.

2. Data Collection: The dataset used for this project is sourced from Kaggle and consists of 8 columns and 1000 rows.

Dataset Information

• gender: Sex of students (Male/Female)
• race/ethnicity: Ethnicity of students (Group A, B, C, D, E)
• parental level of education: Parents' final education (e.g., bachelor's degree, some college, master's degree)
• lunch: Whether the student had lunch before the test (Standard or Free/Reduced)
• test preparation course: Completion status of test preparation course before the test
• math score
• reading score
• writing score

Project Overview

This project utilizes various machine learning algorithms to predict students' math scores:

1. Linear Regression
2. K-Neighbors Regressor
3. Decision Tree
4. Random Forest Regressor
5. XGBRegressor
6. CatBoost Regressor
7. AdaBoost Regressor

Libraries Used

• numpy: For numerical computations
• pandas: For data manipulation and analysis
• scikit-learn: For machine learning algorithms implementation
• xgboost: For gradient boosting
• catboost: For gradient boosting on decision trees
• matplotlib: For data visualization
• seaborn: For statistical data visualization

By following the methodology outlined and using the mentioned libraries, one can reproduce and expand upon the findings of this project.

Conclusion

The model achieved an accuracy of 88% on the test dataset, with the Linear Regression model being used for prediction.