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MLHafizur/Arvato-Identify-Customer-Segments

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Bertelsmann Arvato Project

Project Overview

In this project, we are provided with demographic data of customers of a mail-order company in Germany and demographic data of general population of Germany. Using this data, we are required to identify new customers for the company.

We approach this project in 2 phases:

Use Unsupervised Learning to perform customer segmentation and identify clusters/segments from general population who best match mail-order company's customer base. Use Supervised Learning to identify targets for marketing campaign of the mail-order company who could possibly become their customers. Goal of this project is to predict individuals who are most likely to become customers for a mail-order sales company in Germany.

Data Used

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). In addition, the AZDIAS_Feature_Summary.csv from the previous project was added.

Installations

For the development of the project, the following Python libraries were required:

Matplotlib Pandas Numpy Scikit-learn In addition, Python 3 was used.

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