This repository contains an analysis of the Arvato data provided during the "Data Science" Nanodegree program. Due to the terms and conditions of the data, the data itself cannot be uploaded to GitHub.
The Notebook starts with Exploration and Cleaning of the data and then tries to find customer segments via Clustering. In the final step we predict whetever we should target a customer or not by using Classification.
The IPython Notebook is the only file needed. Just ensure you have the numpy
and pandas
installed, you can install it via pip install numpy
. Other dependencies will be installed inside the Notebook.
RSLVQ | RSLVQ + SMOTE | RUSBoost | XGBoost | XGBoost + SMOTE | GradientBoosting | GradientBoosting + SMOTE | Balanced RF | |
---|---|---|---|---|---|---|---|---|
Accuracy | 97.62 | 98.06 | 68.50 | 98.65 | 98.65 | 98.50 | 98.65 | 98.58 |
Kappa | 0.43 | -0.82 | 1.3 | 0.0 | 0.0 | -0.26 | 0.0 | 1.18 |
Accuracy: 58.71, Kappa: 2.24
We want to thank Bertelsmann Arvato Analytics for providing the dataset and we are also thankful, that Udacity brought up this interesting project. Huge thanks goes to the open source community, for providing dataset libraries and knowledge around them.