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.

Bu proje, Global AI Hub ve Akbank iş birliğiyle düzenlenen Makine Öğrenimine Giriş Bootcamp kapsamında geliştirilmiştir.

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

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.

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.

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.

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

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

This repository contains configuration files for analysing & visualising data obtained from Southern Prefecture Restaurant.

A comprehensive 360° analysis of supermarket sales and customer behavior using Python, with visual insights and data-driven 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

End-to-end customer behavior analysis using SQL, Python, and Power BI to clean data, analyze purchase patterns, create dashboards, and provide actionable business insights.

Customer segmentation project using RFM analysis and clustering algorithms (K-Means, DBSCAN, GMM) to identify distinct customer groups based on purchasing behavior. Includes visualization, evaluation metrics, and parameter tuning methods to support business insights and marketing strategies.

This notebook focuses on RFM (Recency, Frequency, Monetary) segmentation, a popular method used in customer analysis to group customers based on their purchasing behavior. The key goal of RFM segmentation is to identify different customer segments by analyzing their transaction history and assigning them to categories based on their recency of purc