There are a lot of factors that can influence gas prices, from weather conditions to political decisions and administrative fees, and to totally unpredictable factors such as natural disasters or wars.The plan for this Azure machine learning tutorial is to investigate some accessible data and find correlations that can be exploited to create a prediction model.Azure Machine Learning StudioAzure Machine Learning Studio is web-based integrated development environment (IDE) for developing data experiments..It is closely knit with the rest of Azure’s cloud services and that simplifies development and deployment of machine learning models and services.Creating the ExperimentThere are five basic steps to creating a machine learning example..We will examine each of these steps through developing our own prediction model for gas prices.Obtaining the DataGathering data is one of the most important step in this process..Relevance and clarity of the data are the basis for creating good prediction models..Azure Machine Learning Studio provides a number of sample data sets..Another great collection of datasets can be found at archive.ics.uci.edu/ml/datasets.html.After collecting the data, we need to upload it to the Studio through their simple data upload mechanism:Once uploaded, we can preview the data..The following picture shows part of our data that we just uploaded..Our goal here is to predict the price under the column labeled E95.Our next step is to create a new experiment by dragging and dropping modules from the panel on the left into the working area.Preprocessing DataPreprocessing available data involves adjusting the available data to your needs..The first module that we will use here is “Descriptive Statistics”..It computes statistical data from the available data..Besides “Descriptive Statistics” module, one of the commonly used modules is “Clean Missing Data”..The aim of this step is to give meaning to missing (null) values by replacing it with some other value or by removing them entirely.Defining FeaturesAnother module applied at this step in our tutorial is the “Filter Based Feature Selection” module.. More details
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