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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.
Deep Learning Project- Learn to apply deep learning paradigm to forecast univariate time series data.
Machine Learning Project in R-Detect fraudulent click traffic for mobile app ads using R data science programming language.
In this machine learning project, you will develop a machine learning model to accurately forecast inventory demand based on historical sales data.