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Suresh kumar gorakala's Blog (5)

Principal Component Analysis using R

Curse of Dimensionality:

One of the most commonly faced problems while dealing with data analytics problem such as recommendation engines, text analytics is high-dimensional and sparse data. At many times, we face a situation where we have a large set of features and fewer data points, or we have data with very high feature vectors. In such scenarios, fitting a model to the dataset, results in lower predictive power of the model. This scenario is often termed as…

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Added by suresh kumar gorakala on February 28, 2016 at 9:30pm — No Comments

Learn Everything about Sentiment Analysis using R

Today I will explain you how to create a basic Movie review engine based on the tweets by people using R. The implementation of the Review Engine will be as follows:
  • Gets Tweets from Twitter
  • Clean the data
  • Create a Word Cloud
  • Create a data dictionary
  • Score each tweet.

Gets Tweets from Twitter:

First step is to fetch the data from Twitter. In R, we have facility to call the twitter API using package…
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Added by suresh kumar gorakala on January 11, 2016 at 6:00am — No Comments

Topic Modeling in R

As a part of Twitter Data Analysis, So far I have completed Movie review using RDocument Classification using RToday we will be dealing with discovering topics in Tweets, i.e. to mine the tweets data to discover underlying topics– approach known as Topic Modeling.

What is Topic…
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Added by suresh kumar gorakala on December 23, 2015 at 8:30pm — No Comments

Basic recommendation engine using R

Originally posted here

In our day to day life, we come across a large number of Recommendation engines like Facebook Recommendation Engine for Friends’ suggestions, and suggestions of similar Like Pages, Youtube recommendation engine suggesting videos similar to our previous searches/preferences. In today’s blog post I will explain how to build a basic…
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Added by suresh kumar gorakala on October 13, 2015 at 6:30am — No Comments

Introduction to Logistic Regression with R

In my previous blog I have explained about linear regression. In today’s post I will explain about logistic regression. 

        Consider a scenario where we need to predict a medical condition of a patient (HBP) ,HAVE HIGH BP or NO HIGH BP, based on some observed symptoms – Age, weight, Issmoking, Systolic value, Diastolic value, RACE, etc.. In this…

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Added by suresh kumar gorakala on October 9, 2015 at 9:13am — No Comments

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