This repository contains data analysis and machine learning models to predict students' Performance Index based on study hours, previous scores, sleep hours, practice sheets, and extracurricular participation.

The dataset is sourced from Kaggle: Student Performance Dataset

• 10,000 observations
• 6 columns: Hours.Studied, Previous.Scores, Extracurricular.Activities, Sleep.Hours, Sample.Question.Papers.Practiced, Performance.Index
• No missing values

• Numeric descriptive statistics
• Distribution of Performance.Index
• Analysis of relationship with extracurricular activities
• Correlation between features

1. Linear Regression
2. Random Forest
3. XGBoost

Evaluation Results (RMSE on Test Set):

• Linear Regression: ~2.05
• Random Forest: ~8.17
• XGBoost: ~2.13

• Previous scores (Previous.Scores) are the main predictor.
• Study hours and extracurricular participation have a positive but smaller impact.
• Simple linear models perform well because the dataset has strong linear relationships.

• R, RStudio
• Packages: dplyr, ggplot2, caret, randomForest, xgboost, corrplot, summarytools, Metrics

This project is developed using R and can be run in a standard R environment (.R scripts) or in Jupyter Notebook (.ipynb) using the IRKernel.

• Using Jupyter allows combining R code, visualizations, and markdown analysis in a single interactive notebook.
• Compatible with Kaggle Notebooks for easy execution and sharing.