331 search results for "hadoop"

Predict User’s Return Visit within a day part-2

October 22, 2012
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Predict User’s Return Visit within a day part-2

Welcome to the second part of the series on predicting user’s revisit to the website. In my earlier blog Logistic Regression with R, I discussed what is logistic regression. In the first part of the series, we applied logistic regression to available data set. The problem statement there was whether a user will return in

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Predict User’s Return Visit within a day part-1

October 22, 2012
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Predict User’s Return Visit within a day part-1

In my earlier blog, I have discussed about what is logistic regression? And how logistic model is generated in R? Now we will apply that learning on a specific problem of prediction. In this post, I will create a basic model to predict whether a user will return on website in next 24 hours. This

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Logistic Regression with R

October 21, 2012
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Logistic Regression with R

Logistic Regression In my first blog post, I have explained about the what is regression? And how linear regression model is generated in R? In this post, I will explain what is logistic regression? And how the logistic regression model is generated in R? Let’s first understand logistic regression. Logistic regression is one of the

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Vendor news: TIBCO’s proprietary R runtime; Teradata’s appliance integrates R

October 17, 2012
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Vendor news: TIBCO’s proprietary R runtime; Teradata’s appliance integrates R

In a webinar today previewing Spotfire 5 (scheduled for release this November), TIBCO announced that it will include TERR: The Tibco Enterprise Runtime for R. TERR is a closed-source reimplementation of the R language engine, and not based on the GPL-licensed R project from the R Foundation. Here's the relevant slide from the webinar: By making the TERR engine...

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Nine lightning talks on R

October 12, 2012
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At Tuesday's Bay Area R User Group meetup, nine speakers gave five-minute talks on various aspects of R. Revolution Analytics' Luba Gloukhov was one of the presenters, and also provides the summary of the talks below. Links to the slides are included where available for you to check out. Ariel Faigon: Chrestomathy with R Ariel walked us through his...

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Revolution Newsletter: September/October 2012

October 11, 2012
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The most recent edition of the Revolution Newsletter is out. The news section is below, and you can read the full September/October edition (with highlights from this blog and community events) online. You can subscribe to the Revolution Newsletter to get it monthly via email. New R Courses Announced: Two new courses presented by Bob Muenchen (author of R...

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Product revenue prediction with R – part 2

October 8, 2012
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Product revenue prediction with R – part 2

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

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Product revenue prediction with R – part 3

October 8, 2012
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Product revenue prediction with R – part 3

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

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Product revenue prediction with R – part 1

October 8, 2012
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Product revenue prediction with R – part 1

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,

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Tips on accessing data from various sources with R

October 3, 2012
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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...

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