GitHub topics: silhouette-analysis
semoglou/composite_silhouette
A clustering evaluation framework that combines micro- and macro-averaged silhouette scores into a composite metric using statistical weighting.
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AnshikaBansal2004/senior-living-segmentation-analysis
Analysis to optimize services & resident satisfaction in senior living facilities by segmenting population based on characteristics & behaviors.
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devanisdwi/skripsi
Learning Styles Segmentation using K-Prototypes
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abhroroy365/Market_Analysis
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.
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Lefteris-Souflas/Election-Classification-and-Clustering-Analysis
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.
Language: R - Size: 587 KB - Last synced at: about 2 months ago - Pushed at: about 1 year ago - Stars: 0 - Forks: 0
orestasdulinskas/customer_segmentation
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.
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labrijisaad/Optimal-K-in-K-Means-Clustering
Using the Elbow Method and Silhouette Analysis to find the optimal K in K-Means Clustering.
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VinayVirraj/Customer-segmentation
An analysis and approach to customer segmentation
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pierogio/ML_unsupervised
Unsupervised machine learning
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ZhenyuWangg/Mall-Customers-Segmentation--Classification-using-Machine-Learning-
Utilized Python-based unsupervised machine learning algorithms, including K-Means and DBSCAN, to effectively segment the mall customer market.
Size: 780 KB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 0 - Forks: 0
IamJafar/E-Commerce-Customer-Segmentation
Unsupervised Learning - Using K Means algorithm to Cluster the customers.
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josepaulosa/Data_Mining
Data Mining - EDA, Feature Selection, Standardize, Remove Global Outliers, Normalize, Feature Extraction (with PCA), Clustering, Classification (baseline models and hyperparameter tuning with GridSearchCV).
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