Learning objective: In this exercise you will train and evaluate supervised and unsupervised machine learning models.

Workflow description:

The same customer transaction data set from the demo is used in this exercise, with some additional features on the loyalty program membership information.

The Loading Customer Data metanode reads the customer information as well as the loyalty program membership information.
Training a classification model to predict the membership status (Member or Gold). A decision tree model is trained, and its performance is evaluated. (Activity I)
Training a linear regression model to predict the Total spending with the number of purchased items and the loyalty program points. The goodness-of-fit of the trained model is then evaluated. (Activity II)
Grouping customers with similar characteristics into clusters by k-Means clustering. Visualizing the resulting clusters. (Activity III)

	AirBnB Project
	02 Production data pipeline - Ad hoc execution, versioning, and scheduling - Exercise
	04 Machine Learning - Exercise
	AirBnB_Data_Project
	Data_Transformation
	orders_oct_21-27.csv
	prediction-workflow

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