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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.
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.
NETFLIX MOVIES AND TV SHOWS CLUSTERING is a project that aims to cluster the available movies and TV shows on Netflix based on their attributes such as genre, release year, and country of production.
This data set contains ratings from Google reviews. Reviews on attractions from 24 categories across Europe are considered. Using this data clustering model is built.
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.