GitHub - sundusafreen/Student-Success-analytics: Modelled how study hours, sleep, parental pressure, and lifestyle habits affect student academic outcomes — using predictive analytics to quantify which factors matter most.

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# Student Performance Prediction – R Data Mining Project

## Overview
This project investigates the behavioural and academic factors that influence student performance using a dataset of 6,378 records and 19 variables.  
All analysis, modelling, and visualisation were conducted in R.

## Methods Used
- Data cleaning & wrangling (dplyr, tidyr)
- Exploratory Data Analysis (ggplot2)
- Logistic Regression (glm)
- Decision Tree (rpart)
- Model evaluation (confusion matrix, accuracy)

## Repository Structure
- data/ → raw & cleaned datasets  
- scripts/ → R scripts for each analysis stage  
- notebooks/ → RMarkdown notebook  
- results/ → model outputs  
- reports/ → final report or presentation  

## How to Run
1. Open the `.Rproj` file  
2. Install packages:  
   `install.packages(c("tidyverse", "rpart", "caret", "ggplot2"))`  
3. Run scripts in order from `scripts/01_data_cleaning.R` onward

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Modelled how study hours, sleep, parental pressure, and lifestyle habits affect student academic outcomes — using predictive analytics to quantify which factors matter most.

python predictive-modeling regression-models feature-importance decsion-tree

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