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Project on real-time proprietary data for Bertelsmann Arvato Analytics to identify customer segments that form the core customer base of the company using unsupervised learning techniques. Data cleaning was an integral part of the project since the data used here was real-world. Techniques like Principal Component Analysis were also used for Dim…

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Identifying-Customer-Segments

Part of Udacity's Introduction to Machine Learning with PyTorch Nanodegree. Proprietary data from Bertelsmann partners AZ Direct and Arvato Finance Solution was used in this project. Unsupervised Learning techniques like KMeans are used to organize the general population into clusters, then those clusters are used to see which of them comprise the main user base for the company. Prior to applying the machine learning methods, good amount of data cleaning has also been performed on the dataset.

Requirements

  • NumPy
  • pandas
  • Sklearn / scikit-learn
  • Matplotlib (for data visualization)
  • Seaborn (for data visualization)

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Project on real-time proprietary data for Bertelsmann Arvato Analytics to identify customer segments that form the core customer base of the company using unsupervised learning techniques. Data cleaning was an integral part of the project since the data used here was real-world. Techniques like Principal Component Analysis were also used for Dim…

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