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”

Meetup Instruction Guide Build your Bot

build your bot

Build your Bot

Workshop Setup and Instruction Guide to Build your Bot

In most of our Microsoft Data Science meetup, hosted by i.e. Infi, InSpark, Winvision, Macaw, among others, we organize workshops. This time you will learn how to build your bot with the Cognitive Services of Microsoft. In this workshop you wil build a Question & Answer Bot. This type of bots is able to answer questions based on predefined answers.

The point of this workshop is to introduce you to the basics of creating a simple bot, and it is not intended to be a deep-dive into bot development. If you want to learn more, please check out the Microsoft Bot Framework. Continue reading “Meetup Instruction Guide Build your Bot”

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: http://www.processmacro.org/ Continue reading “Graphing moderation of PROCESS v3.0 Model 1”

The effect of Stress on Satisfaction and Self-Evaluation

Stress, Satisfaction and Self-Evaluation

This short blog is about exploring the relationships between stress, satisfaction and self-evaluation. For an assignment of the course Introduction to Psychology, I had to gather 20 responses to answer some questions. Due to the huge amount of responses, I thought it could be nice to share the results to thank all of you for your participation. Continue reading “The effect of Stress on Satisfaction and Self-Evaluation”

Predictive modeling and human interaction

Reflection predictive modeling. Credits to Olu Eletu

This blog gives you some reflections on predictive modeling and human interaction.

The prediction…

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”

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”