Human Resources Analytics – Predict Employee Leave

Human Resources Analytics - Why are employees leaving

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”

Graphing moderation of PROCESS v3.0 Model 1

Graphing moderation with PROCESS V3.0 graph 2

This blog is about graphing moderation with the help of SPSS with the PROCESS macro, and our corresponding MD2C Graphing template for PROCESS v3.0 Model 1 – Moderation.

The case that we used is based on the article of Chapman and Lickel (2016), and you can find a detailed elaboration of this case in Andrew Hayes’ second book about Introduction to Mediation, Moderation, and Conditional Process Analysis (Hayes, 2017). You can download the data from Hayes’ website. The datafile you need for this example is called DISASTER. Besides, you can also download the PROCESS V3.0 macro for SPSS and SAS (and much more) from the site:

Continue reading “Graphing moderation of PROCESS v3.0 Model 1”

Optimizing prediction models on Azure – pruning the trees

Optimizing prediction models on Azure

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”

Azure Machine Learning: Predicting Annual Income

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”

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”