**Tatvic Blog » R**, and kindly contributed to R-bloggers)

style="text-align: justify">In my upcoming three blogs, I am going to discuss about how Product managers, Data analyst and Data scientists can develop model for the prediction of the transactional product revenue on the basis of user actions like total numbers of time product added to the cart, total numbers of time product added to the cart, total numbers of page view of product and more. Product managers and data scientists can use linear regression tool for model based predictive analysis on business data here. We will apply
href="http://www.tatvic.com/blog/linear-regression-using-r/" >regression learning on product transactional data for defining most effective variables that can impact on product transactional revenue. In this blog, I will discuss about how can we develop prediction model on GA dataset and what are the summary statistics of the model. First, we will see how data analyst can get transaction related dataset from source (Google Analytics) for predictive analysis of product revenue.
style="margin-bottom: .571em"> For business analytics, we require set of product purchase historical data on which we can perform analysis operations. We use Google Analytics for capturing datasets like product name, product SKU for purchase items, numbers of instances removed from the cart, total numbers of product page view, item revenue and more. After capturing data from Google Analytics, we store it on our Mongo Instance on Amazon server. With
title="RMongo" href="http://cran.r-project.org/web/packages/RMongo/index.html" >RMongo Package we can load datasets in
href="http://rstudio.org/" >R-studio (is free and open source integrated development environment (IDE) for R) or can also use
href="http://www.code.google.com/p/r-google-analytics/">r
href="http://www.code.google.com/p/r-google-analytics/">-google-analytics for doing the same.**Business Dataset:**

As we know, business data can be either numerical or categorical. Suppose, if we take product revenue and number of product page views then they will be considered as Numerical Data. But if we take product-name and country-name then they will be considered as Categorical Data.

We have available extracted Google Analytics dataset which looks like below that we used for href="http://www.tatvic.com/blog/cart-analysis/" >Shopping cart analysis in last post.