Udacity Arvato Identify Customer Segments

Capstone Project

Table of Contents

1. Problem Statement
2. Project Motivation
3. Instalation
4. Files
5. Results and Conclusions
6. Licenses and Acknowledgements

Problem Statement

• Consider a company's marketing campaign (Arvato Financial Services), in which we need to select those individuals who can become the company's future customers. For this task, we have the following databases: demographic information from Germany (country where the company is located) and information from individuals who are already customers of this company.
  
• First, the demographic information of the German population was analyzed in order to understand and explore the main characteristics of this population.
  
• Then, we create a predictive model that can determine with reasonable accuracy whether a person can become a possible consumer of the company, when subjected to a certain marketing campaign.
  
• Finally, we classify each possible consumer, from an unexplored test database, and submit the result on the kaggle platform.
  

Project Motivation

• The project is a problem for a company, with real data and with several possible approaches. It is a rich set of data and an interesting problem to be solved. Submitting work on Kaggle is a way to compare the quality of our algorithm with of other students algorithms. That's why I chose to do this specific project that motivated me to learn even more.

Instalation

• The following packages are necessary: numpy , datetime, pandas , matplotlib, seaborn , math, sklearn , pylab ,itertools, imblearn, pickle, xgboost

Files

• project.pdf - Report with detailed explanation of the entire project.
  
• capstone_proposal.pdf - Report with a proposal for thus project.
  
• util.py - python module with basically data processing and feature engineering
  
• cluster.py -- python module with clustering methods for segmentation report
  
• pca.py -- python module with pca methods for dimensionality reduction
  
• 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);
• unknown_values.csv: Mapping dictionary with attributes and the value of the unkown value

Results and Conclusions

• The result of this work can be found in the file final_project.pdf, as well as any details of implementation, conclusions and future work

Licenses and Acknowledgements

• The project is part of Udacity's machine learning nanodegree program. The data provided is not public, and belongs to Arvato and Udacity