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.

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

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

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

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

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

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

The project provides the Apriori algorithm and Market Basket Analysis (MBA) to analyze transactional data, generating personalized recommendations based on Support, Confidence, and Lift metrics to enhance customer experience and boost sales.

Ecommerce Customer Behaviour Analysis using SQL and MySQL Workbench to analyze revenue trends, customer preferences, and purchasing patterns.

Power BI dashboard analyzing marketing spend and sales performance for a fashion retailer, tracking Revenue, Profit, Orders, ROAS/ROI, WoW trends, and campaign effectiveness, including new vs. repeat customers and ads vs. direct sales contribution.

RFM-based customer segmentation analysis for an e-commerce dataset. Includes data cleaning, exploratory analysis, Recency-Frequency-Monetary scoring, segment classification, visual dashboards, and strategic business insights. Designed to identify high-value customers and guide targeted marketing actions

Customer segmentation in e-commerce using clustering techniques, with and without PCA. The project compares model performance, interpretability, and efficiency to provide actionable insights for personalised marketing and strategic decision-making.

Q2 sales and customer behaviour analysis identifying £70k-£95k annual revenue opportunities through delivery optimisation and discount strategy refinement.

Retail analytics project analyzing grocery reorder behavior, basket composition, product demand, and shopping timing patterns.

This project focuses on customer segmentation using RFM analysis and K-Means clustering into high value, low value, and potentially loyal groups. Key revenue metrics such as LastMonthRevenue and LifeTimeRevenue are calculated, with visualizations to provide insights into customer behavior for targeted marketing and improved retention str