After development of predictive model for transactional product revenue -(Product revenue prediction with R – part 1), we can further improvise the model prediction by modifications in the model. In this post, we will see what are the steps required for model improvement. With the help of a set of model summary parameters, the data
After development and improvement of predictive model with R (as in the previous 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
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,
The RHadoop project, the open-source project supported by Revolution Analytics to integrate R and Hadoop, continues to evolve. Now available is version 2 of the rmr package, which makes it possible for R programmers to write map-reduce tasks in the R language, and have them run within the Hadoop cluster. This update is the "simplest and fastest rmr yet",...
Jeffrey Breen (the man behind the Twitter airline sentiment analysis example) recently posted a collection of slides with some great tips for accessing data from R. "Tapping the Data Deluge" includes information on: Using the XLConnect package to read data from Excel spreadsheets Using the foreign package to read SPSS, SAS, Stata and dBase data files Using SQL queries...
Welcome to the second part. In the last blog post on Linear Regression with R, we have discussed about what is regression? and how it is used ? Now we will apply that learning on a specific problem of prediction. In this post, I will create a basic model to predict bounce rate as function
Regression Through this post I am going to explain How Linear Regression works? Let us start with what is regression and how it works? Regression is widely used for prediction and forecasting in field of machine learning. Focus of regression is on the relationship between dependent and one or more independent variables. The “dependent variable”
This guest post is by Alex Guazzelli, VP of Analytics at Zementis Inc. -- ed. PMML, the Predictive Model Markup Language, is the de facto standard to represent predictive analytics and data mining models. With PMML, it is extremely easy to move a predictive solution from one system to another, since it avoids proprietary issues and incompatibilities. Companies around...
Today's guest post is from Ron Fredericks, videographer and co-founder of LectureMaker, LLC — ed. I was initially surprised to find R user groups (RUGs) so popular. I filmed my first R session during the 2009 Predictive Analytics World in San Francisco. I filmed several more R user sessions over the past three years along with business/science clients and...
In case you missed them, here are some articles from June of particular interest to R users. RStan is a new package for Bayesian modeling with R. It's faster and can fit more highly-correlated models than the MCMC sampler of BUGS and JAGS. Biostatistician Corey Chivers used R to animate the epidemic-like growth of retailer Walmart in the US....
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
