Ind_Project_Mall-Customer-Clustering--Kaggle

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Summary

I worked with the Mall Customer Segmentation Dataset, which provided a various instances of shoppers of different ages, incomes, etc. I first reviewed the data and used paired plots to determine that gender was not a greaty differentiator/indicator of customer segmentation. I then employed three different Scikit-learn unsupervised clustering algorithms: K-Means, DBSCAN, and Mean Shift to determine if customer segmentation as a function of the other features-- Age and Income-- was possible and/or clear. With K-Means, I plotted inertia for different numbers of clusters to hone in on potential elbow points, and I ran through several combinations of min_samples and epsilon for DBSCAN.

Results

Both K-Means and Mean Shift settled on a cluster number of 5 when looking at the Score as a function of Income. They overlapped substantially in their assignment of segment/cluster, leading to clear indication behind the "meaning" of each segment:

• Label 0 is "mid" income and "mid" spending
• Label 1 is "high" income and "low" spending
• Label 2 is "high" income and "high" spending
• Label 3 is "low" income and "low" spending
• Label 4 is "low" income and "high" spending

Skills (Developed & Applied)

Programming, Python, Pandas, Dataframes, Unsupervised-ML, Clustering, DBSCAN, K-Means, Mean Shift, Scikit-learn, Technical Communication