This repository hold all experiments conducted during my PhD (2019-2023). HPML means "Hybrid Partitions for Multi-Label Classification". SET-UP-1
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Updated
Sep 24, 2024
This repository hold all experiments conducted during my PhD (2019-2023). HPML means "Hybrid Partitions for Multi-Label Classification". SET-UP-1
AI - Project 3 - This project implements Aglomerative Clustering to cluster all generated points in 2D space using: Centroid & Medoid
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.
Wine Classification using Machine learning algorithms
GitHub repo for customer data analysis to drive personalized marketing strategies and enhance engagement, loyalty, and revenue
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.
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.
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.
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.
This is a learning based code refactoring model
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