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Each project comes with 2-5 hours of micro-videos explaining the solution.
Get access to 50+ solved projects with iPython notebooks and datasets.
Add project experience to your Linkedin/Github profiles.
Customer churn refers to the situation when a customer ends their relationship with a company, and its a costly problem. Customers are the fuel that powers a business.
Loss of customers impacts sales. Further, it’s much more difficult and costly to gain new customers than it is to retain existing customers. As a result, organizations need to focus on reducing customer churn.The good news is that machine learning can help. For many businesses that offer subscription-based services, its critical to both predict customer churn and explain what features relate to customer churn.
Older techniques such as logistic regression can be less accurate than newer techniques such as deep learning, which is why we are going to show you how to model an ANN in R with the keras package.
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 project, we are going to talk about H2O and functionality in terms of building Machine Learning models.
In this project, we are going to work on Deep Learning using H2O to predict Census income.