Microsoft has now developed a data science track! You can learn the basics steps of data science, online and for free. These courses form part of the Microsoft Professional Degree Program. There are 10 steps to take, and sometimes you can choose between in example a course which uses R or the one that uses Python. The courses are self-paced, but some do have a starting data. You can find more about these courses on our DataChangers website. Continue reading “Gain Data Science Skills in 10 steps with the Microsoft Data Science Track”
Binary classification: heart disease prediction – 7 ideas how to start and improve your model
This experiment is based on the original Heart Disease Prediction experiment created by Weehyong Tok from Microsoft, which is one of the Healthcare Industry solutions. This experiment uses the data set from the UCI Machine Learning repository to train and test a model for heart disease prediction. We will use this as a starting point to give you 7 ideas how to start and improve the Cortana Intelligence Gallery examples. Thanks Weehyong for creating and sharing your experiment! Continue reading “Binary classification: heart disease prediction – 7 ideas how to start and improve your model”
Will somebody earn over 50k a year?
This blog is about building a model to classify people using demographics to predict whether a person will have an annual income over 50K dollars or not.
The dataset used in this experiment is the US Adult Census Income Binary Classification dataset, which is a subset of the 1994 Census database, using working adults over the age of 16 with an adjusted income index of > 100.
This blog is inspired on the Sample 5: Binary Classification with Web Service: Adult Database from the Cortana Intelligence Gallery. Continue reading “Azure Machine Learning: Predicting Annual Income”
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”
This blog explains how to build a Human Activity Classifier with Azure Machine Learning. This classifier predicts somebody’s activity class based on the use of wearable sensors. The complete experiment can be downloaded from the Azure Machine Learning Gallery. Continue reading “How to build a Human Activity Classifier with Azure Machine Learning”
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”
This blog is about building a classifier on the Azure Machine Learning platform for qualitative activity recognition of weight lifting exercises. This classifier predicts if an exercise has been done correctly (class A). Continue reading “Azure Machine Learning – Qualitative Activity Recognition of Weight Lifting Exercises”
This blog explains how to build a Student Performance Classifier with Azure Machine Learning. This classifier predicts if a student will pass or fail Mathematics. The complete experiment can be downloaded from the Azure Machine Learning Gallery. Continue reading “How to build a Student Performance Classifier with Azure Machine Learning”
Improving 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”