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hirarchical-clustering

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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

This project explores Netflix's content evolution, analyzes TV shows and movies, and builds a recommendation system. Discover insights from a dataset of 7,787 titles as of 2019 and learn how we clustered content based on textual features.

  • Updated Sep 3, 2023
  • Jupyter Notebook

This project explores customer segmentation using various clustering techniques on a dataset of mall customers. The goal is to identify distinct customer groups based on demographic and behavioral attributes, enabling businesses to tailor their marketing strategies more effectively.

  • Updated Jul 11, 2024
  • Jupyter Notebook

Analyzing US crime statistics using hierarchical clustering to uncover patterns in state-level arrest data and Advanced analytics to delineate market segments in retail, optimizing targeted marketing strategies through customer behavior and demographic profiling.

  • Updated Apr 15, 2024

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