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”
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”
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”