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dendogram

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Clustering methods in Machine Learning includes both theory and python code of each algorithm. Algorithms include K Mean, K Mode, Hierarchical, DB Scan and Gaussian Mixture Model GMM. Interview questions on clustering are also added in the end.

  • Updated Aug 7, 2024
  • Jupyter Notebook

Perform Clustering (Hierarchical, K Means Clustering and DBSCAN) for the airlines and crime data to obtain optimum number of clusters. Draw the inferences from the clusters obtained.

  • Updated Dec 28, 2022
  • Jupyter Notebook

Assignment-07-Clustering-Hierarchical-Airlines. Perform clustering (hierarchical) for the airlines data to obtain optimum number of clusters. Draw the inferences from the clusters obtained. Data Description: The file EastWestAirlinescontains information on passengers who belong to an airline’s frequent flier program. For each passenger the data …

  • Updated Jun 19, 2021
  • Jupyter Notebook

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

This project focuses on network anomaly detection due to the exponential growth of network traffic and the rise of various anomalies such as cyber attacks, network failures, and hardware malfunctions. This project implement clustering algorithms from scratch, including K-means, Spectral Clustering, Hierarchical Clustering, and DBSCAN

  • Updated Jun 11, 2023
  • Jupyter Notebook

Perform clustering (hierarchical) for the airlines data to obtain optimum number of clusters. Draw the inferences from the clusters obtained. Data Description: The file EastWestAirlinescontains information on passengers who belong to an airline’s frequent flier program. For each passenger the data include information on their mileage history and…

  • Updated Feb 12, 2022
  • Jupyter Notebook

Explore a comprehensive analysis of Netflix's extensive collection of movies and TV shows, clustering them into distinct categories. This GitHub repository contains all the details, code, and insights into how we've organized and grouped the vast content library into meaningful clusters.

  • Updated Apr 14, 2024
  • Jupyter Notebook

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