arvato_capstone_project

Udacity - Machine Learning Engineer Nanodegree Program

Project Overview

In this project, we analyze demographic data for customers of a mail-order sales company in Germany, comparing it against demographics information for the general population. Exploratory Data Analysis is performed to understand and clean the data. Unsupervised learning techniques are used to perform customer segmentation, identifying the parts of the population that best describe the core customer base of the company. Then, we'll apply what we've learned on a third dataset with demographic information for targets of a marketing campaign for the company, and use a model to predict which individuals are most likely to convert into becoming customers for the company.

Software and Libraries

This project uses Python 3 and is designed to be completed through the Jupyter Notebooks IDE. It is highly recommended that you use the Anaconda distribution to install Python, since the distribution includes all necessary Python libraries as well as Jupyter Notebooks. The following libraries are expected to be used in this project:

NumPy
pandas
Sklearn / scikit-learn
Matplotlib (for data visualization)
Seaborn (for data visualization)
joblib (use to save sklearn models)
missingno (to analyze missing data)
pyarrow (to save to parquet format)
tqdm (to show progress bars)
imblearn (for resampling imbalanced data)
xgboost (for the supervised model)
hyperopt (for hyperparameter tuning using Bayesian optimization)
functions.py (various utility functions used through the project)
other some functions from scikit-learn

How the project is organized

There are 3 Jupyter Notebooks that are supposed to be ran in order

1.Data Cleaning and Transformation.ipynb 2.Customer Segmentation.ipynb 3.Supervised Learning Model.ipynb

The notebooks expect that the following files:

Udacity_AZDIAS_052018.csv
Udacity_CUSTOMERS_052018.csv
Udacity_MAILOUT_052018_TEST.csv
Udacity_MAILOUT_052018_TRAIN.csv

The data is the property of Bertelsmann Arvato Analytics and it is not included in the repository.

Also AZDIAS_Feature_Summary.csv is expected to be presented. This file is constructed for this project and contains only metadata (the file is provided).

For the Kaggle competition the submit.csv file is included in the notebooks folder

hazemhamada / arvato_capstone_project Goto Github PK