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.

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.

Projeto para explorar dados de vendas, identificar padrões e criar modelos preditivos que otimizem estratégias comerciais e melhorem o relacionamento com clientes.

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.

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.

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.

A Power BI-driven retail sales analysis project uncovering customer purchasing patterns, seasonal trends, product preferences, and revenue drivers using transactional data. Key insights and visuals support data-informed business decisions in inventory, pricing, and marketing strategies.

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.

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

A machine learning project that predicts online shopping purchase intent using a k-nearest neighbor classifier. The model analyzes visitor behavior features like page visits, browsing duration, bounce rates, and user characteristics to predict whether a visitor will make a purchase. Built with scikit-learn.

This project leveraged PostgreSQL to analyze Walmart sales data, uncovering key insights into branch performance, product trends, and customer behavior. Data was imported, analyzed through structured queries, and results were exported to support actionable business strategies.

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

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