Showcase for using H2O and R for scoring for marketing campaign in retail

Customer Segmentation using RFM Analysis

Customer segmentation is the process of dividing customers into groups based on common characteristics so companies can market to each group effectively and appropriately. This Project uses R language and Kmeans clustering algorithm to segment customers into clusters.

Customer Profile & Shopping Behavior Analysis is an R-based project analyzing customer data from retail stores, focusing on segmentation, seasonal trends, and market behaviors.

RFM-based customer segmentation using R and Tableau. Includes data cleaning, scoring, and an interactive dashboard to uncover actionable insights.

Customer segmentation is dividing the customers into segments based on RFM scores. In this project I've used RFM model in R to generate RFM score.

This exploratory data analysis project aimed to unravel key insights into donor behavior, preferences, and regional trends using SQL and R

I describe the methods used to segment customers of a Brazilian online retailer via K-means clustering of their recency, frequency, and monetary value of purchases.

A machine learning project using R programming language to implement exploratory data analysis and modeling techniques to discover customer shopping trends that can help to improve marketing strategies for business organizations.

Use k-means clustering to segment credit card customer data from a Kaggle dataset

A repository of all my projects in R.

K-means as an unsupervised machine learning technique. Customer Segmentation Case.

Customer clustering with k-means and DBSCAN

The aim of this project is to analyze the spending behavior of customer groups using various techniques.

A RFM model is implemented to relate to customers in each segment this code has been implemented in R

Materi praktikum Talent Scouting Academy (TSA) Kominfo 2023-Customer Segmentation

This project performs customer segmentation using k-means clustering in R. It categorizes customers into different groups based on their age, income, and spending patterns, helping businesses target specific customer groups more effectively.

This R project conducts a comprehensive analysis of customer distances and sales for retail stores. Leveraging SQL server connectivity, it calculates distances, categorizes sales within specified radii, and outputs insightful data for retail business decision-making.

Analysis of SuperStore sales data with visual insights into customer segmentation and product trends