This project helps a Germany Financial Institution identify new customers based on existing customers' demographic data and whole population of the Germany. The idea is to train an unsupervised learning model to identify the similarity and difference between the general population and the company’s customers based on geographical features provided. And then build a supervised learning model to identify potential new customers with the training data.
For this project I used Principle Component Analysis for feature deduction, KMeans for clustering and Xgboost for classification. I will talk about the details about Preprocessing and EDA, model selection and training, hyperparameter tuning in the following part.
Keywords: Customer Segmentation, PCA, KMeans, Unsupervised Learning
-
Notifications
You must be signed in to change notification settings - Fork 0
neojohny/arvato-financial-data
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Folders and files
Name | Name | Last commit message | Last commit date | |
---|---|---|---|---|
Repository files navigation
About
Arvato data segamentation and recommendation
Resources
Stars
Watchers
Forks
Releases
No releases published
Packages 0
No packages published