Customer segmentation (or market segmentation) are techniques to split customers into clusters based on similarities to get a sense of their behavior. In this notebook, we are going to analyze patterns in the Online Retail Data Set from the UCI Machine Learning Repository. A k-means model is used based on the RFM measures.
- Customer Segmentation.ipynb: main file with the logic. the visuazlaition on GitHub does not show Plotly
- Customer Segmentation.html: interactive option to see all the information no need to install something. You just have to download the html and run it in a browser
- Online Retail.xlsx: dataset
Pandas, plotly, datetime, matplotlib, seaborn sklearn and math.
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Customer Segmentation and Market Basket Analysis by Marcelo Marques https://www.kaggle.com/mgmarques/customer-segmentation-and-market-basket-analysis
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Recency, Frequency, Monetary Model with Python — and how Sephora uses it to optimize their Google and Facebook Ads by Yexi Yuan https://towardsdatascience.com/recency-frequency-monetary-model-with-python-and-how-sephora-uses-it-to-optimize-their-google-d6a0707c5f17
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Exploring Customers Segmentation With RFM Analysis and K-Means Clustering With Python by Hs.T https://medium.com/swlh/exploring-customers-segmentation-with-rfm-analysis-and-k-means-clustering-93aa4c79f7a7