Bertelsmann Arvato customers IA

Table of Contents

1. Overview
2. Installation
3. Running
4. Repository Structure
5. Final Considerations

Overview

The goal of this project is to create a Customer Segmentation Report for Arvato Financial Solutions. At this project, you will find a notebook showing the exploratory data analysis, the data wrangling and the unsupervised learning algorithm and a folder with multiple notebooks each one with a technique to try to get the best supervised learning model.

This project is part of the Udacity Data Science Nanodegree program.

Installation

Create a virtual environment named customers_seg.

$ python3 -m venv customers_seg -- for Linux and macOS
$ python -m venv customers_seg -- for Windows

After that, activate the Python virtual environment

$ source customers_seg/bin/activate -- for Linux and macOS
$ customers_seg\Scripts\activate -- for Windows

Install the requirements (The list of requirements may be bigger than the libraries needed because I got the libraries from the Udacity workspace)

$ pip install -r requirements.txt

Running

Unfortunately the data is private and is only available at Udacity workspace. But just to give you a taste of the data that if you enroll in the nanodegree you will have access:

• Udacity_AZDIAS_052018.csv:
  • Demographics data for the general population of Germany;
  • 891 211 persons (rows) x 366 features (columns).
• Udacity_CUSTOMERS_052018.csv:
  • Demographics data for customers of a mail-order company;
  • 191 652 persons (rows) x 369 features (columns).
• Udacity_MAILOUT_052018_TRAIN.csv:
  • Demographics data for individuals who were targets of a marketing campaign;
  • 42 982 persons (rows) x 367 (columns).
• Udacity_MAILOUT_052018_TEST.csv:
  • Demographics data for individuals who were targets of a marketing campaign;
  • 42 833 persons (rows) x 366 (columns).

Repository Structure

• The requirements.txt has the needed packages to run the code.
• Arvato Project Workbook.ipynb notebook with the exploratory data analysis, the data wrangling and the unsupervised learning algorithm
• terms_and_conditions The terms and conditions to use the Bertelsmann/Arvato data
• supervised_learning_notebooks folder with all the notebooks used for the supervised learn part

Final Considerations and acknowledgments

To acomplish those result, I've read many tutorials, articles and documentation from https://machinelearningmastery.com, https://www.kaggle.com and of course from https://stackoverflow.com to get insights.

Thanks to Bertelsmann/Arvato for kindly make the data of a real problem available at Udacity and for all Udacity professors and mentors.