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aglomerative-hierarchical-clustering

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This repo explores KMeans and Agglomerative Clustering effectiveness in simplifying large datasets for ML. Goals include dataset download, finding optimal clusters via Elbow and Silhouette methods, comparing clustering techniques, validating optimal clusters, tuning hyperparameters. Detailed explanations and analysis are provided.

  • Updated May 28, 2023
  • Jupyter Notebook

Server-driven UI refers to a design pattern in which the user interface is primarily controlled and rendered by a server, with the client serving as a display and interaction layer. This approach allows for a separation of concerns between the presentation and business logic, and can simplify client-side development.

  • Updated Feb 12, 2023
  • Kotlin

This project aims to use k-means and Agglomerative clustering to segment customers into different groups based on their characteristics and purchasing habits. The goal is to understand the similarities and differences between the customer segments, which can help inform marketing strategies and target specific groups of customers.

  • Updated Dec 22, 2022
  • Jupyter Notebook

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