A comprehensive R analysis to optimize inventory and identify high-value/at-risk customers for a medical distributor

Django web application for comprehensive market analysis using machine learning and the Dunnhumby dataset

Customer segmentation analysis using unsupervised learning on German demographics data (Bertelsmann Arvato Analytics). The project applies data preprocessing, PCA for dimensionality reduction, and KMeans clustering to identify customer groups that are over-represented compared to the general population.

Advanced ML-based sentiment analysis and customer segmentation for 515k+ European hotel reviews. Identifies hidden patterns in customer satisfaction using NLTK VADER and K-Means clustering.

A complete data mining pipeline for supply chain and sales analysis, combining exploratory data analysis, predictive modeling, and optimization to generate actionable business insights.

Leveraging the Kaggle Online Retail Dataset (2009-2011), this system optimizes decision-making with: RFM Modeling for high-value customer identification, Ensemble Learning for purchase behavior prediction, Game Theory-Based Pricing for dynamic strategy optimization.

A study for a UK bank, undertaking segmentation analysis to identify trends and patterns in their customers.

This project applies RFM analysis to segment customers based on purchasing behavior. It combines data cleaning, EDA, and RFM scoring to identify key customer groups and support targeted marketing, retention, and growth strategies.

Customer behaviour analysis for 2Market, a global retailer, delivering actionable insights across demographics, advertising channels, and product preferences.

This project analyzes e-commerce order fulfillment using Advanced SQL Techniques and Python-based visualization to uncover insights on sales trends, customer segmentation, shipping cost optimization, and payment preferences.

Segmify is a comprehensive customer analysis tool that leverages RFM analysis, demographic insights, and buying behavior to segment customers and drive strategic business decisions. This project focuses on transforming raw customer data into actionable insights for improved marketing and sales strategies.

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.

This project focuses on predicting customer purchase behavior using machine learning models, with an emphasis on feature importance.

Capstone Project

This project involves a comprehensive Customer Segmentation Analysis for an Australian bike company. The analysis uses RFM model to categorize customer and visualise key metrics, aiming to enhance sales strategies and customer engagement.

This project focuses on classifying customers based on their purchasing behaviors. By analyzing the transaction data, we categorize customers into distinct groups to better understand their preferences and improve targeted marketing strategies. The classification is performed using various machine learning algorithms implemented in Python, specific

This repository contains project materials for the Spring 2024 STAT 208 class, specifically for Team 8. All materials are the property of Team 8, University of California, Riverside, A. Gary Anderson School of Management. Thank you for viewing our repository.

A topic designed by Warwick Business School requires students to enhance loan portfolio management by utilising cluster analysis to group borrowers with similar characteristics, enabling personalised loan products, targeted marketing strategies, and a better customer support process to serve the unique needs of each segment through cluster analysis

In this project, I will be performing an unsupervised clustering of customer's records from a Retail Chain.