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7 reflections on Microsoft’s Binary Classification: Customer Relationship Prediction Azure Machine Learning experiment

Azure_Machine_Learning_Binary_Classification_Customer

The Azure ML sample experiment Binary Classification: Customer relationship prediction shows us how we can use Azure’s binary classification algorithms. In the original Microsoft sample experiment, the models predict a customer’s churn, appetency, and upselling target variables. In this blog, I’m only focusing on the upselling target. Continue reading “7 reflections on Microsoft’s Binary Classification: Customer Relationship Prediction Azure Machine Learning experiment”

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Azure ML Predictive Maintenance Template – data preparation and feature engineering

Predictive Maintenance

Predictive maintenance can be quite a challenge :). Some of you might have tried to build the Azure ML Predictive Maintenance Template by Microsoft.

In this template, you are guided through the steps that are required to build and deploy several predictive maintenance scenarios. These steps are offered as experiments in the Cortana Analytics Gallery and can be easily downloaded. The original data comes from the NASA prognostic data repository.

This blog focusses on step 1: Predictive Maintenance: Step 1 of 3, data preparation and feature engineering Continue reading “Azure ML Predictive Maintenance Template – data preparation and feature engineering”

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Azure Machine Learning: how to easily improve your model

results AzureML Regression Demand Estimation

Azure Machine Learning example: Regression Demand EstimationImproving your Azure Machine Learning model

In this example we start with a sample experiment from the Microsoft Azure Machine Learning Gallery: Regression: Demand estimation. In this example there are four models built, and compared, based the newly created features. We will explore whether standard operations could improve these samples models, inspired by the e-book Data Science in the Cloud with Azure Machine Learning and R of Stephen Elston.  We haven’t used the suggested new features that depend on prior info which wasn’t always complete, but created some other variables, that could be created based on the available dataset. Observing the sample, there are basically two areas where we see quick possibilities for improvement: data cleaning and transformation and evaluation of the results. Continue reading “Azure Machine Learning: how to easily improve your model”