ML helps in identifying best Day & Time for telemarketing calls

High Frequency Forecasting - Pragmatic Confessions

Views expressed here are from author’s industry experience. Author also trains on Machine (Deep) Learning applications; for further details, he will be available at info@tatvaai.com or mavuluri.pradeep@gmail.com for more details.

Find more about author at http://in.linkedin.com/in/pradeepmavuluri

Attrition Percentage By Reason @ Online Delivery (Boys) Business

For more details, reach at either info@tatvaai.com or mavuluri.pradeep@gmail.com

Find more about author at http://in.linkedin.com/in/pradeepmavuluri

Detection of Lazy Delivery Boys using IoT Data and ML

TATVA AI Discussion Room

TATVA AI engages a dedicate discussion room for all future white board discussions, and place of contact near Kundalahalli Gate, Bengaluru-66.

Interested can drop in with prior appointment. info@tatvaai.com or mavuluri.pradeep@gmail.com

Always late bothers online food delivery business

Views expressed here are from author’s industry experience. Author also trains on Machine (Deep) Learning applications; for further details, he will be available at info@tatvaai.com or mavuluri.pradeep@gmail.com for more details.

Find more about author at http://in.linkedin.com/in/pradeepmavuluri

DL (Deep Learning) helps in customer retention and addition

Views expressed here are from author’s industry experience. Author also trains on Machine (Deep) Learning applications; for further details, he will be available at info@tatvaai.com or mavuluri.pradeep@gmail.com for more details.

Find more about author at http://in.linkedin.com/in/pradeepmavuluri

Glimpse of Tatva AI Solution on Restaurant Demand Calibration

Below is a glimpse of recent delivery by Tatva AI

Views expressed here are from author’s industry experience. Author also trains on Machine (Deep) Learning applications; for further details, he will be available at info@tatvaai.com or mavuluri.pradeep@gmail.com for more details.

Find more about author at http://in.linkedin.com/in/pradeepmavuluri

Now "fread" from data.table can read "gz" and "bz2" files directly

Dear R Programmers,

Those who all use data.table for your data readings, good news is that now, fread supports direct reading of zip formats like"gz" and "bz2".

To all my followers and readers, as mentioned earlier several times, good way for saving both space and reading fast is achievable by first saving 
raw files into "gz" format and their after reading the same into R and convert them into to fst format for all further reads/loading.

Happy R Programming!

Data Summary in One Go

Data Description R Code

This 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 use and for quick interpretation purposes.

This provide regular data summary stats needed (as shown in below image) in *.csv format which can be copied an pasted to excel as per your needs.

To use it follow the syntax:

source("https://raw.githubusercontent.com/pradeepmav/data_description_function/master/data_description.R")
data_description("datasetname")

 
Happy R Programming!


Author trains & develops Machine Learning (AI) applications, and can be reached at info@tatvaai.com or besteconometrician@gmail.com for more details.
Find more about author at http://in.linkedin.com/in/pradeepmavuluri

Commit to Memory: What you cannot expect with TensorFlow for Automated Machine Learning?

Views expressed here are from author's industry experience. Author trains on Machine Learning applications and can be reached at info@tatvaai.com or besteconometrician@gmail.com for more details. Find more about author at http://in.linkedin.com/in/pradeepmavuluri

AI/ML Talent Availability Against Market Expectations

Views expressed here are from author's industry experience. Author can be reached at info@tatvaai.com or besteconometrician@gmail.com for more details.

 

Find more about author at http://in.linkedin.com/in/pradeepmavuluri

Why Record Linkage needs a scalable computing power?

Though, “Record Linkage” is a popular word among statisticians, and epidemiologists - “the problem of matching/joining records from one data source to another which describe the same entity”; has a long historical attention from the time since data collection gained (1960s) and continues to gain attention as new methods of collection, formats and stacks of data being added to the existing. The other popular terms for the same are deduplication, data matching, entity/name resolution, record matching, etc. Please, refer to the following paper https://homes.cs.washington.edu/~pedrod/papers/icdm06.pdf, for one of the good works in this field. Also, one can look at the below google trends graph for the attention to this filed from 2014 to the present.

The purpose of this blog is to bring forth, why record linkage needs a scalable computing power, for which I present my observations with an simple example as show below:

 

Views expressed here are from his industry experience. He can be reached at mavuluri.pradeep@gmail or besteconometrician@gmail.com for more details.

Find more about author at http://in.linkedin.com/in/pradeepmavuluri

Python package maintenance GUI’s like R ones

It is quite often I observed people approaching me to help out in understanding whether Python has good Graphical User Interface (GUI) for package maintenance like R ones:

Base R: update.packages(ask=’graphics’)

RStudio: Tools >>> Check for Package Updates

Answer for it would be; currently few like this one “pips” are still under development, and still I am using “pip-upgrader” for the maintenance.