Predict Employee Leave
In this tutorial, you will learn how to employ a simulated dataset from Kaggle to build a machine learning model to both predict and explain whether employees will leave their employer or not and the reason(s) why they may do so. The data comprise a wide range of topics which allow to explain employees’ leave behavior in relation with A) organizational factors (department); B) employment relational factors (i.e. tenure, the number of projects participated in; the average working hours per month; objective career development; salary); and C) job-related factors (performance evaluation; involvement in workplace accidents). Continue reading “Human Resources Analytics – Predict Employee Leave”
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”
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”
Microsoft Data Science Azure Machine Learning Workshop
Lab Setup and Instruction Guide
In this first Microsoft Data Science meetup, hosted by Infi and with guest speaker Jeroen ter Heerdt from Microsoft, we also organized a workshop to get the basics of machine learning on the Azure platform. Continue reading “Meetup #1: Microsoft Data Science Azure Machine Learning Workshop”
MD2C has founded a new Meetup group: Microsoft Data Science.
This Microsoft Data Science Meetup group is for data scientist that would like to share experiences about doing data science on the Microsoft Azure data platform. Continue reading “New Meetup group: Microsoft Data Science”
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”
NOTE: 11 December 2017 – This blog is about the PROCESS v2.16 version. We have also an example with PROCESS v3.0!
This blog is about graphing conditional indirect effects with the help of SPSS with the PROCESS v2.16 macro, and our MD2C Graphing moderated mediation Excel template. Continue reading “Graphing conditional indirect effects with the MD2C Excel Template”
This blog is part of a lecture about descriptive statistics and exploring graphs with SPSS. Some of the data is of the students themselves, and for other graphs, I used the datasets from Andy Field’s Discovering Statistics Using IBM SPSS Statistics, a book I highly recommend! Continue reading “Research Methodology – Descriptive statistics and exploring graphs with SPSS”
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”