The goal of this customer segmentation is to put customers into groups with similar characteristics, such as gender, age, annual income, and spending score. The segmentation includes data exploration, feature engineering, clustering, and analysis.

To identify a different customer segment and underlie the similar characteristics of their shopping behavior, an unsupervised learning algorithm will be applied to distinguish clusters using a similarity measurement and the distance of the similarity function.

• 1.1 Data Import/ Inspeaction/ Data Cleaning
• 1.2 Exploratory Analysis
• 1.3 Feature Engineering/ Feature Transformation/ PCA

• 2.1 KMean
• 2.2 DBSCANS

• Dashboard: http://clusteringdash.ngrok.io/
• Colab: https://colab.research.google.com/github/Primary43/CustomerSegmentation-Plotly-Dash/blob/main/python/clustering_plotly_dashboard.ipynb