Create and analyze customer segments with k-means clustering

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Introduction

  • Customer segmentation and why it is important to know
  • Building a k-means clustering model and choosing the optimal number of clusters with the Elbow method and the Silhouette coefficient
  • Interpretation of k-means clustering in the marketing context

Customer segmentation


Become more productive in visualizing data in Python

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Introduction


Recency, Frequency, Monetary metrics to forecast customer behaviour with linear regression

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Introduction


Apply multilayer perceptron model to retain your customers

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Introduction


All Customers are Not Equal

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Introduction


Utilize patterns of reasoning, which are common across data science

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Introduction


Learn about reasoning and components of an argument

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Background


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Background


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Outline of the post:


“Forewarned is forearmed” — English proverb

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Short Intro

Aigerim Shopenova

Data Scientist @ Rakuten | 💜 Data Science and Psychology | https://www.linkedin.com/in/aigerimshopenova/

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