People use regression model a lot to predict continuous variables. Other than that, regression model finds the correlations between input variables. By analyzing the relationships, people could find some underlying pattern to help us make decisions. Today, I will demonstrate how to do regression analysis and interpret its results by using house data from King County, Washington from May 2014 through May 2015.
Unsupervised machine learning algorithms are wildly used in the world, and clustering is one of them. Generally, purposes of clustering is finding labels for cases. Today, I’m gonna demonstrate two clustering algorithms and how to evaluate their performance.
It’s a practice problem that building a single prediction model is not a enough to assure we have the best model to predict accurately. In this article, I will introduce another classifier, K-NN, by using Letter Recognition Data Set at UCI machine learning repository.
Decision tree is an straightforward and powerful machine learning algorithm. However, it could mislead the result if the data is imbalanced. Today, we’re gonna talk about how to cope with imbalanced data by building three decision trees.
Many times, we couldn’t process data because ranges of attributes are totally different, like height and weight. Sometimes, even we solve that, we still have to deal with the effect of minor observation errors. In this work, I’m gonna demonstrate two preprocessing techniques to cope.