The following packages (i.e., libraries) are necessary to successfully run this project on your local machine:
- python >=3.6
- numpy >= 1.19.2
- pandas >= 1.0.1
- seaborn >= 0.10.0
- matplotlib >= 3.1.3
- scikit-learn >= 0.20
In this project, you will apply unsupervised learning techniques to identify segments of the population that form the core customer base for a mail-order sales company in Germany. These segments can then be used to direct marketing campaigns towards audiences that will have the highest expected rate of returns. The data that you will use has been provided by our partners at Bertelsmann Arvato Analytics, and represents a real-life data science task.
The unsupervised learning branch of machine learning is key in the organization of large and complex datasets. While unsupervised learning lies in contrast to supervised learning in the fact that unsupervised learning lacks objective output classes or values, it can still be important in converting the data into a form that can be used in a supervised learning task. Dimensionality reduction techniques can help surface the main signals and associations in your data, providing supervised learning techniques a more focused set of features upon which to apply their work. Clustering techniques are useful for understanding how the data points themselves are organized. These clusters might themselves be a useful feature in a directed supervised learning task. This project will give you hands-on experience with a real-life task that makes use of these techniques, focusing on the unsupervised work that goes into understanding a dataset.
In addition, the dataset presented in this project requires a number of assessment and cleaning steps before you can apply your machine learning methods. In workplace contexts, you will frequently need to work with data that is untidy or needs preprocessing before standard algorithms and models can be applied. The ability to perform data wrangling and the ability to make decisions on data that you work with are both valuable skills that you will practice in this project.
- Identify_Customer_Segments.ipynb: the jupyter notebook of the project;
- Identify_Customer_Segments.pdf: the pdf of the project;
- Udacity_Reviews_Unsup.pdf: the rubric of the project.
Just download (or git-clone) this project and use it with anaconda :)
Copyright (c) 2021 Vagner Zeizer C. Paes
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
Udacity is strongly and the reviewers are highly acknowledged for this great experience of writting a Data Science Blog.
- Vagner Zeizer Carvalho Paes
This project is part of the Introduction to Machine Learning with Tensorflow Nanodegree.