This is the fourth and last assignment of Machine Learning for Data Analysis by Wesleyan University on Coursera. My assignment diverges quite a bit from the approach taken by the instructor since I wanted to have only three clusters to determine pumps functionality (functional, functional needs repair, and Continue reading “Clustering Pumps [mlw4]”
machine learning
Shrinking pumps? [mlw3]
This is the third assignment of the Machine Learning for Data Analysis by Wesleyan University on Coursera. I applied least absolute shrinkage and selection operator (LASSO) to the DrivenData data set pumpItUp. LASSO is a technique which does variable selection shrinking the ‘useless’ coefficients (i.e., variables) toward zero. Applying this method Continue reading “Shrinking pumps? [mlw3]”
The forest and the pump! [mlw2]
The random forest algorithm is the topic of the second assignment of Machine Learning for Data Analysis by Wesleyan University on Coursera. This assignment extends the previous one because besides from using random forest instead of decision trees I included more variables than the previous assignment. In this analysis I included also Continue reading “The forest and the pump! [mlw2]”
Pump it up with a decision tree [mlw1]
This post is about the first assignment of Machine Learning for Data Analysis by Wesleyan University on Coursera. In the past month I have tried to mine the dataset of the pumpItUp challenge on DrivenData. The challenge requires Continue reading “Pump it up with a decision tree [mlw1]”