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The weekly sales transaction dataset consists of weekly purchased quantities of 800 products over 52 weeks. Normalised values are provided too. The objective of this data science project in R is to find out product bundles that can be put together on sale. Typically Market Basket Analysis was used to identify such bundles, here we are going to compare the relative importance of time series clustering in identifying product bundles.
In this machine learning project, you will build predictive models to identify wine preferences of people using physiochemical properties of wines and help restaurants recommend the right quality of wine to a customer.
In this machine learning churn project, we implement a churn prediction model in python using ensemble techniques.
In this project, we are going to talk about H2O and functionality in terms of building Machine Learning models.