This Program is for Clustering Customer Data On the Basis of their Spending, Income,Family and Children.
Given the google rating data, hierarchical clustering algorithm is used to cluster reviews. This data set is populated by capturing user ratings from Google reviews. Reviews on attractions from 24 categories across Europe are considered. Google user rating ranges from 1 to 5 and the average user rating per category is calculated.
Implemented K - means and Hierarchical clustering to cluster the retail customers into different segments, based on their spending habits. Employed RFM metrics and assigned labels to each customer based on RFM score.
Classifying silhouette as one of 3 different types of vehicle, using a set of features extracted from the silhouette dataset
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