R package for Customer Behavior Analysis

Multivariate Time Series Classification for Human Activity Recognition with LSTM

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.

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.

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.

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.

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

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.

Exploratory and statistical analysis of ride data for a fictional bikeshare company, completed in R and R Markdown.

Segment Sphere is a customer segmentation tool using RFM analysis to group customers based on recency, frequency, and monetary value. It processes e-commerce data, provides actionable insights, and visualizes results with interactive charts. Ideal for understanding customer behaviour and supporting data-driven decisions.

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.

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.

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

Statistical analysis of the Online Retail II dataset using R. Includes data cleaning, EDA, hypothesis testing, and business insights with visualizations.