You Are What You Buy Personal Information Extraction From Anonymized Data.

This project explores the correlation between consumer purchasing behavior and personal attributes using anonymized data. By analyzing key variables such as education, marital status, income, and recent purchasing habits, we aim to uncover insights that define consumer profiles. We implement several machine learning models, including Gradient Boosting, Random Forest, Support Vector Machine (SVM), and Deep Neural Networks (DNN), to predict personal information based on purchasing data. The dataset encompasses various expenditures across food and luxury items, allowing for a comprehensive analysis of consumer preferences. Our findings will contribute to understanding how purchasing behavior reflects individual characteristics, ultimately offering valuable implications for targeted marketing strategies and personalized consumer experiences. Through this work, we highlight the potential of anonymized data in extracting meaningful consumer insights while ensuring privacy and data integrity.

Keywords: Gradient Boost, Random Forest, SVM and DNN, classification algorithms, Kaggle dataset.