This repository contains code and report for "Capstone Project - Arvato Customer Segmentation" done as part of Udacity Machine Learning Engineer Nanodegree program.
In this project, the demographic data of German population and the customer data have been analysed in order to perform Customer Segmentation and Customer Acquisition. Arvato Financial Solutions is a services company that provides financial services, Information Technology (IT) services and Supply Chain Management (SCM) solutions for business customers on a global scale.
This project is to help a Mail-Order company to acquire new customers to sell its organic products. The goal of this project is to understand the customer demographics as compared to general population in order to decide whether to approach a person for future products.
- proposal.pdf My project proposal. Introducing the problem and my proposed solutions at the time. Some aspects changed as I was progressing throughout the project.
- report.pdf My project report. Splited into 5 major parts: Definition, Analysis, Methodology and Results.
- Notebooks 1,2 and 3 Containing respectively the code for Data preprocessing, Dimensionality reduction / clustering and Model creation.
- source_pytorch Contain the model/train/predict.py files required for the creation and deployment of a pytorch model with SageMaker.
This project has been entirely built within a "ml.m4.xlarge" SageMaker notebook instance. The "conda_pytorch_p36" kernel was select to run each notebook. If you plan on executing this code, you should ideally do it in the exact same environment as to avoid dependencies issues. Otherwise, just solve them as you go.