A silhouette-guided instance-weighted k-means algorithm that integrates silhouette scores into the clustering process to improve clustering quality.

Using the Elbow Method and Silhouette Analysis to find the optimal K in K-Means Clustering.

Unsupervised ML analysis of lifestyle data to uncover risk patterns for Dry Eye Disease

Analysis to optimize services & resident satisfaction in senior living facilities by segmenting population based on characteristics & behaviors.

A clustering evaluation framework that combines micro- and macro-averaged silhouette scores into a composite metric using statistical weighting.

Unsupervised machine learning

Utilized Python-based unsupervised machine learning algorithms, including K-Means and DBSCAN, to effectively segment the mall customer market.

Customer clustering using silhouette K-means and silhouette analysis on Python. Also using logistic regression on Python to predict top 30 customers.

Description: "Data preprocessing, K-Means clustering with Silhouette analysis, and PCA dimensionality reduction on microclimate, obesity, and gene expression datasets. Achieved 267% clustering improvement with PCA."

Data Mining - EDA, Feature Selection, Standardize, Remove Global Outliers, Normalize, Feature Extraction (with PCA), Clustering, Classification (baseline models and hyperparameter tuning with GridSearchCV).

Unsupervised Learning - Using K Means algorithm to Cluster the customers.

The project uses KMeans clustering on the Global Superstore dataset to categorize customers based on their buying habits, aiming to help retailers make better business decisions by tailoring their marketing strategies and improving their inventory management.

This project explores customer segmentation and market analysis in the context of online retail using an online retail dataset. By applying advanced analytics, we aim to uncover insights that can drive strategic decisions and enhance business performance.

A modular, research-grade Python library for unsupervised learning with embeddings (PCA, t-SNE, UMAP) and clustering (KMeans, DBSCAN, GMM). Includes reproducible experiments, metrics, visualizations, and tests—perfect for ML research and coursework.

Creating predictive models to classify Trump's vote share and clustering counties based on demographics and economic variables. Report findings in PDF with detailed methodologies, model assessments, and R code for the project.

An analysis and approach to customer segmentation

Learning Styles Segmentation using K-Prototypes