Identifies the parts of the Germany population that best describe the core customer base of the Arvato company. Uses a supervised model to predict which individuals are most likely to convert into becoming customers for the company.
There is no additional libraries to run the code within the three notebooks except the ones already imported in each notebook.
This is the Capstone Project of the Data Science Udacity Nanodegree and it needed to be completed in order to obtain a certificate.
Arvato Project Workbook.ipynb: Customer Segmentation Report: used unsupervised learning method: K-Means to create customer segments.Arvato Project - Supervised Learning: Built multiple machine learning models to predict whether or not an individual will respond to a campaign.Arvato Project - Predictions: Made predictions using the best model and submitted the results to Kaggle.utils.py: Helper functions used in the three notebooks.
Results are found at the end of each notebook. Additionally, the final score obtained in the competition was 0.789.
I would like to thank Arvato Financial Solutions for sharing the data with us and Udacity for organizing the competition.