Human Resources Analytics – Predict Employee Leave

Human Resources Analytics - Why are employees leaving

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”

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”

Meetup #1: Microsoft Data Science Azure Machine Learning Workshop

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”

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”

How to build a Human Activity Classifier with Azure Machine Learning

Human Activity Classifier Azure Machine Learning

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”

Azure Machine Learning – Qualitative Activity Recognition of Weight Lifting Exercises

Qualitative Activity Recognition of Weight Lifting Exercises

Qualitative Activity Recognition of Weight Lifting ExercisesThis 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”

How to build a Student Performance Classifier with Azure Machine Learning

Predict-student-performance-with-azure-machine-learning

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”

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”