Imagine you are an HR-Manager, and you would like to know which employees are likely to stay, and which might leave your company. Besides you would like to understand which factors contribute to leaving your company. You have gathered data in the past (well, in this case Kaggle simulated a dataset for you, but just imagine), and now you can start with this Hands On Lab – Predict Employee Leave to build your prediction model to see if that can help you.
In this lab, you will learn how to create a machine learning module that predicts whether an employee will stay or leave your company. We are aware of the limitations of the dataset but the objective of this hands on lab is to inspire you to explore the possibilities of using machine learning for your own research, and not to build the next HR-solution.
We created a starting experiment for you on the Azure AI Gallery to give you a smooth start. Continue reading “Human Resources Analytics – Predict Employee Leave”
This blog gives you some reflections on predictive modeling and human interaction.
The nice thing of predictive modeling is that it gives you possible answers, which you could use to define you or your customers’ actions. You can classify things or trying to predict numbers, like your sales. Another nice thing is that you can retrain your models over time to get -hopefully, but not guaranteed- better results. Continue reading “Predictive modeling and human interaction”
This is a simple example about optimizing prediction models on Azure. In this case we will use a Boosted Decision Tree model. We will show you how you can use the Permutation Feature Performance module to prune your trees.
We start with the Student Performance Classifier from a previous blog. We already found out that the Boosted Decision Tree algorithm gave the best results, so we will start with that one to train our model with. Continue reading “Optimizing prediction models on Azure – pruning the trees”
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”