This repository contains a behavioural analytics project using R to model online shoppers' purchasing intention. It includes exploratory data analysis, hypothesis testing, and machine learning classification models built on over 12,000 e-commerce sessions. Tools used include R, ggplot2, caret, and randomForest.
From the README
Online Shoppers' Purchasing Intention Behavioural Analytics R Project Overview Analysed 12,000+ e-commerce sessions from the UCI Online Shoppers Purchasing Intention dataset to identify behavioural drivers of purchase conversion. Conducted hypothesis testing, correlation analysis, and built three classification models — Logistic Regression, Decision Tree, and Random Forest — alongside an exploratory linear regression on page engagement. Tools & Technologies • Language: R / RStudio • Visualisation: ggplot2, corrplot • Modelling: caret, rpart, randomForest, pROC • Data Wrangling: dplyr Dataset UCI Machine Learning Repository — Online Shoppers Purchasing Intention Dataset (Sakar & Kastro,...
