
Introduction to classification
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Not all response variables are quantitative. Predicting qualitative variables is called classification. This article will walk you through the introductory concepts of classification.
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In this article we will try out linear regression on Boston data set. As we go forward in the course, we shall include more complexities. As a first step, let us check for outliers and high leverage points. dt %>% pivot_longer(cols = c(1:14)) %>% ggplot(aes(x=value))+ geom_boxplot()+ facet_wrap(~name) It looks …
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Linear regression is a very simple method of supervised learning. despite widespread use of advanced models it remains one of the most widely used method. In fact many advanced methods are extensions of linear methods. It is extremely important that a data scientist understands linear regression in all aspects. Linear …
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This article creates a stepping stone for deeper understanding of elements of data science. Supervised and unsupervised learning, measuring accuracy of models and bias variance trade off are discussed briefly in this article. Introduction Suppose you are a sales manager in a company and you want to increase your sales. …
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