Segment customers of Arvato Financial Solutions into distinct categories using PCA and K-Means Clustering
This project uses Principal Component Analysis and K-Means clustering to see if any similarities exist between customers of Arvato Financial Solutions,and used those similarities to segment customers into distinct categories. This segmentation is used to help the business make more informed marketing and product decisions.
The data files for this project is private to Arvato Financial Solutions so thus could not be included in this public repository. However it consists of:
- Udacity_AZDIAS_Subset.csv: Demographic data for the general population of Germany; 891211 persons (rows) x 85 features (columns).
- Udacity_CUSTOMERS_Subset.csv: Demographic data for customers of a mail-order company; 191652 persons (rows) x 85 features (columns).
- Data_Dictionary.md: Information file about the features in the provided datasets.
- AZDIAS_Feature_Summary.csv: Summary of feature attributes for demographic data.