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cophenetic-distance

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The project involves performing clustering analysis (K-Means, Hierarchical clustering, visualization post PCA) to segregate stocks based on similar characteristics or with minimum correlation. Having a diversified portfolio tends to yield higher returns and face lower risk by tempering potential losses when the market is down.

  • Updated Jan 20, 2022
  • Jupyter Notebook

To identify different segments in the existing customer, based on their spending patterns as well as past interaction with the bank, using clustering algorithms, and provide recommendations to the bank on how to better market to and service these customers.

  • Updated Sep 5, 2022
  • Jupyter Notebook

This project explores and analyzes financial data of a number of securities, applies Hierarchical and K-means clustering to group securities and create cluster profiles to develop personalized portfolios and investment strategies for clients

  • Updated Mar 21, 2023
  • Jupyter Notebook

Project involves performing clustering analysis (K-Means, Hierarchical clustering, visualization post PCA) to segregate stocks based on similar characteristics or with minimum correlation. Having a diversified portfolio tends to yield higher returns and faces lower risk by tempering potential losses when the market is down.

  • Updated Oct 10, 2023
  • Jupyter Notebook

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