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The German credit dataset contains information on 1000 loan applicants. Each applicant is described by a set of 20 different attributes. Of these 20 attributes, seventeen attributes are discrete while three are continuous. The main idea is to use techniques from the field of information theory to select a set of important attributes that can be used to classify tuples. In this data science project, you will train a neural network using these attributes; the neural network is then used to classify tuples.
In this project, we are going to work on Deep Learning using H2O to predict Census income.
There are different time series forecasting methods to forecast stock price, demand etc. In this machine learning project, you will learn to determine which forecasting method to be used when and how to apply with time series forecasting example.
In this project, we will try to predict how often players playing a video game called PUBG will win when they play by themselves.