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.

全球新冠肺炎的数据分析，包括基础知识有：kmeans算法设计，SSE算法设计，分级聚类算法设计，cophenetic distance 算法设计。

An investment advisory firm needs to segment stock offerings so they may offer their customers understandable investment options.

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

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.

Using Hierarchial clustering to categories the spending of customers into groups based on their spending habit and other features