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

style="text-align: justify">After development and improvement of predictive model with R (as in the previous
href="http://www.tatvic.com/blog/product-revenue-prediction-with-r-part-2/" >blog), I have focused here about making a prediction with the R model ( linear regression model ) and comparison with the Google prediction API model. In statistical modeling, R will calculate intercept and variable coefficients to describe the relationship between a response variable and the explanatory variables. And that model will use this relation for making the prediction purpose.
The statistical relation can be described by the following formula (which is derived from
href="http://www.tatvic.com/blog/product-revenue-prediction-with-r-part-2/#formula" >this model ),
style="text-align: justify">Productrevenue = 39.92 – 0.0078 * (xcartaddtotalrs_out) – 34.10 * (xcartremove_out) + 12.480 * (xprodviews_out) – 13.50 * (xuniqprodview_out) + 0.00037 * (xprodviewinrs_out) Therefor in R, there is predict() function of making a prediction with this relation. But for making prediction we require input data. We will store request data to input_data. Now, suppose we have an input dataset (
href="http://www.tatvic.com/blog/product-revenue-prediction-with-r/#codesnap" >description ) like This means we want to predict the transactional product revenue on the base of xcartaddtotalrs, xcartremove, xproductviews, xuniqprodview and xprodviewinrs. Now, we will make a prediction by predict function and model_out prediction model (which we have already developed in
href="http://www.tatvic.com/blog/?p=3013">Product revenue prediction with R – part 2).
style="text-align: justify">We
style="text-align: justify"> can do
style="text-align: justify">the same prediction activity
style="text-align: justify"> on google prediction API with less effort. When we processed the same
href="http://www.tatvic.com/blog/product-revenue-prediction-with-r/#codesnap" >dataset with google prediction API for predictive modeling, it’s model summary would look like
style="text-align: center">
href="http://www.tatvic.com/blog/wp-content/uploads/2012/09/get1.jpg">
class="aligncenter wp-image-3293" src="http://www.tatvic.com/blog/wp-content/uploads/2012/09/get1.jpg" alt="" width="700" height="270" />
Let’s identify above attributes from prediction result. The id is unique model identity information for model identification. In model information, there are numberInstances which describes total numbers of rows are 4061 in the dataset, modelType attribute describes the type of model (either regression or categorical) which is regression and meanSquaredError which is 1606123.17. Root of mean squared error is the cost of this regression model in the Google Prediction API model which is 1267.33.
style="text-align: justify">Don’t think this stuff is more complex, it’s pretty interesting once you are used to developing it. To start learning this predictive modeling, just start with rough implementation and improve step by step as per your requirement. If you need to do it yourself you can
href="http://www.tatvic.com/blog/downloads/product_revenue_r.rar?" onclick="_gaq.push(['_trackEvent','Downloads','Product Revenue-r','Blog',,1]);">download this R code + sample dataset. In next of my blog-
title="Product revenue prediction with Prediction API" href="http://www.tatvic.com/blog/product-revenue-prediction-with-prediction-api/" >Product revenue prediction with Prediction API, I will discuss about generating prediction with Google Prediction API with more description.
style="text-align: justify">Want us to help you implement or analyze the data for your visitors.
href="http://www.tatvic.com/contact/?ref=blogpost">Contact us

> input_data <- data.frame(xcartaddtotalrs_out=0, xcartremove_out=0, xprodviews_out=47, xuniqprodview_out=38, xprodviewinrs_out=5828)

> predict(model_out,input_data,type="response")

output
115.8346013