This project segments 10,000 cable TV subscriber households based on spending for video, internet, and phone services. Using K-means clustering, I identified high-, mid-, and low-value segments to guide personalized marketing, optimize retention, increase revenue, and reveal service preferences and upselling opportunities.

Calcium imaging pipeline for R

Auto market segments identification with K-means

Multi Resolution Analysis of Tiling Arrays. (R package)

Chromosome Conformation Capture on Chip (4C): Data Processing. (Workflow/Scripts)

This case requires trainees to develop a customer segmentation to define marketing strategy. The sample dataset summarizes the usage behavior of about 9000 active credit card holders during the last 6 months. The file is at a customer level with 18 behavioral variables.

This repo contains works on various academic projects using real-life data for modelling and prediction

Using the mtcars and cnbc dataset, I'm exploring the hierarchical clustering technique.

rMiW: a R package for microscopy-based image analysis workflow

ClusterCarac helps you understand what makes each group in your data unique, whether the groups come from clustering methods or from existing categories like age, region, or customer type.

Automated Sound Analysis Pipeline for longitudinal analysis of birdsong development

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

My attempts at computer vision using data from the DeepFish dataset - help wanted

Modul latihan R untuk Customer Segmentation dari DQLAB yang sudah saya kerjakan.

Clustering exercise of ACC basketball players to analyze "positionless basketball".

Customer Segmentation to provide the offers based on the RFM Analysis.

Comparison of kmeans and supercells for image semgentiation in R

Segmentation of the Statistics Canada’s Set of Proximity Measures – A Clustering Algorithm Approach. UBC MDS capstone project for Statistics Canada.

Development version of the CptNonPar R package

A repository for supplementary considerations required to run certain stroke segmentation algorithms