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 ### Project Description
 
-This is the 4th project for the Machine Learning Engineer Nanodegree. In this project I apply unsupervised learning techniques on product spending data collected for customers of a wholesale distributor to identify customer segments hidden in the data. I first explore the data by selecting a small subset to sample and determine if any product categories highly correlate with one another. Afterwards, you I preprocess the data by scaling each product category and then identifying (and removing) unwanted outliers. Then I apply PCA transformations to the data and implement a clustering algorithm (Gaussian Mixture Model)to segment the transformed customer data. Finally, I compare the segmentation found with an additional labeling and consider ways this information could assist the wholesale distributor with future service changes.
+This is the 4th project for the Machine Learning Engineer Nanodegree. In this project I apply unsupervised learning techniques on product spending data collected for customers of a wholesale distributor to identify customer segments hidden in the data. 
+
+Here, I first explore the data to determine if any product categories highly correlate with one another by observing a small subset of the data and also by ploting a scater matrix. Afterwards, I preprocess the data by scaling each product category and then identifying (and removing) unwanted outliers. Then I apply PCA transformations to the data and implement a clustering algorithm (Gaussian Mixture Model)to segment the transformed customer data. Finally, I compare the segmentation found with an additional labeling and consider ways this information could assist the wholesale distributor with future service changes.
 
 ### Install