Publications by Pradeep Mavuluri
Average Expenses for TV across states of USA
This post makes an attempt to depict the averages spent across the states towards their TV channel expenses for a big size country (USA). Though it has been developed using sample data belonging to a particular service provider; this post depicts its interest in regional differences in average spent on said service across the country. Herein, I w...
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Average Expenses for TV across states of USA
This post makes an attempt to depict the averages spent across the states towards their TV channel expenses for a big size country (USA). Though it has been developed using sample data belonging to a particular service provider; this post depicts its interest in regional differences in average spent on said service across the country. Herein, I w...
2051 sym 4 img
Big Data Insights – IT Support Log Analysis
This post brings forth to the audience, few glimpses (strictly) of insights that were obtained from a case of how predictive analytic’s helped a fortune 1000 client to unlock the value in their huge log files of the IT Support system. Going to quick background, a large organization was interested in value added insights (actionable ones) from t...
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Big Data Insights – IT Support Log Analysis
This post brings forth to the audience, few glimpses (strictly) of insights that were obtained from a case of how predictive analytic’s helped a fortune 1000 client to unlock the value in their huge log files of the IT Support system. Going to quick background, a large organization was interested in value added insights (actionable ones) from t...
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Big Data Insights: Tale of IT Investments and Returns
Once again, this post brings forth to the audience, a predictive analytical insight from huge volumes of information technology security data belonging to two fortune 500 companies (more or less having similar characteristics). Going to a quick background of the study, here, analytical interest was to know how both organizations under...
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Big Data Insights: Tale of IT Investments and Returns
Once again, this post brings forth to the audience, a predictive analytical insight from huge volumes of information technology security data belonging to two fortune 500 companies (more or less having similar characteristics). Going to a quick background of the study, here, analytical interest was to know how both organizations under...
2473 sym R (3576 sym/2 pcs) 2 img
Hard-nosed Indian Data Scientist Gospel Series – Part 1 : Incertitude around Tools and Technologies
Before recession a commercial tool was popular in the country, hence, uncertainty around tools and technology was not much; however, after recession, incertitude (i.e. uncertainty) around tools and technology have pre-occupied and occupying data science learning, delivery and deployment. When python was continuing as general programming languag...
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Clean or shorten Column names while importing the data itself
When it comes to clumsy column headers namely., wide ones with spaces and special characters, I see many get panic and change the headers in the source file, which is an awkward option given variety of alternatives that exist in R for handling them. One easy handling of such scenarios is using library(janitor), as name suggested can be employed f...
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Read and write using fst & feather for large data files.
For past few years , I was using featheras my favorite data writing and reading option in R (one reason was its cross platform compatible across Julia, Python and R), however, recently, observed it’s read and write time lines were not at all effective with large files of size > 5 GB. And found fst format to be good for both read and write of la...
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Data Summary in One Go
Data Description R CodeThis function and package is long pending for publishing from my side, this time expecting soon to put as package for quick usage, before that thought releasing it for feedback.Below function provides R code for getting data description details like missing, distinct, min, max, mean, median, mode in one go for ready to us...
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