customer-behavior

R package for Customer Behavior Analysis

Python project for Market Basket Analysis. Generates synthetic retail transactions, mines frequent itemsets using Apriori & FP-Growth, derives association rules, and outputs CSVs + visualizations. Portfolio-ready example demonstrating data science methods for uncovering product co-purchase patterns.

A deep exploration of loyalty as a multi-dimensional behavioral system shaped by intent, habit, and sensitivity. This article introduces a geometric framework for modeling customer behavior, predicting churn trajectories, and designing ML systems that understand loyalty as a dynamic state, not a metric.

Multivariate Time Series Classification for Human Activity Recognition with LSTM

Key: clustering, using logistic regression to build elasticity modeling for purchase probability, brand choice, and purchase quantity & deep neural network to build a black-box model to predict future customer behaviors.

Recency-Frequency based recommendation scoring for product recommendation.

This project was developed during the “Introduction to Machine Learning” Bootcamp organized by Global AI Hub in collaboration with Akbank.

E-commerce customer segmentation using Expectation-Maximization and Gaussian Mixture Models (GMM), with behavioral feature engineering, AIC/BIC model selection, cluster profiling, and PCA visualization.

From data to decisions! Focused on market research, I analyzed customer behavior, product associations, and uncover hidden opportunities for business growth.

Análise de dados aplicada a transações comerciais para geração de insights estratégicos e apoio à tomada de decisão / Data analysis applied to commercial transactions to generate strategic insights and support decision-making

R analysis of 96K+ Olist e-commerce orders testing whether late deliveries hurt customer satisfaction and retention, using Wilcoxon and Chi-square tests plus cohort-based revenue retention analysis.

A clean and insightful exploratory data analysis of Black Friday sales to uncover customer behavior, top-selling products, and sales patterns.

Exploratory Data Analysis of Online Food Delivery data using PySpark, Pandas, and Matplotlib to uncover customer trends, preferences, and business insights.

E-commerce sales analytics using Python, Pandas, NumPy, Matplotlib and Seaborn to analyze Olist data, customer behavior, product performance, revenue trends and business KPIs.

End-to-end exploratory analysis of e-commerce customer behavior and sales data using a public Kaggle dataset (v2, multi-order).

Analyzed customer shopping behavior through data cleaning, exploratory data analysis (EDA), and visualization to uncover purchasing trends and business insights.

Data cleaning, preprocessing, and exploratory analysis of Instacart customer purchasing behavior using Python.

AI-powered e-commerce return prediction system that identifies return risk from customer, order, product, and transaction patterns to support smarter business decisions.

E-commerce sales analytics project using Python, SQL, and Power BI to explore customer behavior, product performance, and business insights.

This project analyzes restaurant sales transactions using Excel, covering price, quantity, payment method, purchase type, city, and manager performance. Outliers were identified and removed to ensure data accuracy. An interactive dashboard with slicers and charts provides valuable insights into sales distribution, and overall business performance.