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You’re reading Not a theory, but a theory. blog post of the machine learning course of the Kaggle Insights Team. In this post, you’ll see a practical example on how to run machine learning code in notebooks and how to use Kaggle notebooks. The lecture series takes you on a journey through machine learning from the concepts to the applications and finishes with a practical exercise to apply the skills in a real world setting. The practical exercise where we deploy the models in the browser is also available on this page. We will start the practical part of this series with a real world example. The dataset for the example comes from a collaboration with HelpNow, the online helpdesk system of the University of Vienna. The data comes from the HelpNow Ticket system which contains all interactions with the helpdesk from September 2013 to April 2014. The Ticket objects contain the following data about the interaction with the helpdesk: the title of the ticket, the date, the user who filed the ticket, the time of filing, the title of the helper and the reason for the call. In total we have over 3 million tickets and over 5 million comments. The task is to identify the type of support tickets that require a call to a help desk. There are 3 possible outcomes: A ticket is not a ticket that needs to be handled by the helpdesk A ticket is a ticket that needs to be handled by the helpdesk A ticket is not a ticket that needs to be handled by the helpdesk The dataframe for the ticket is the following: 1. Build a model for the classifier To build a model for the ticket classifier, we will use the scikit-learn library. The dataset consists of two classes: tickets and non-tickets. To build the classifier, we will use one-vs-all logistic regression. For the benchmark training, we use the dataset from the machine learning course of the Kaggle Insights Team. for k in range(2): # training. We use the entire dataset. train_samples = int(0.7 * np.random.rand(2 * 1_000_000)) train_inputs = np.random.randint(2, size=2 * train_samples).reshape(-1, 2) train_labels =

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