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How to predict cellphones from a large dataset containing large parts of text?
I have a large (~3.5 GB) dataset with 1513 rows and 55 columns.
Data is completely text based. Rows are a list of phone numbers and each phone number is a single cell (making 55 columns total).
I have been playing around with an R script and trying to get a basic model to predict the input of the dataset as a cellphone. I will also add that this is a column that I would like to classify.
I followed the instructions on this github page and they were as follows:
In the train set, we load the pre-trained VADER model and train it on a
subset of your training set (that has at least 100,000 unique phone
numbers), then use that model to tokenise your test set.
I followed the instructions in this example.
In the training data frame I have 100,000 unique phone numbers. So I split the data into 6 files that contain a total of 200,000 phone numbers.
For example:
phone number.1.txt, phone number.2.txt, phone number.3.txt, phone number.4.txt, phone number.5.txt, phone number.6.txt
I then read in all the files in a for loop:
for (i in 1:6)
{
fname = paste(“phone numbers.”,i,sep=”.”)
fpath = paste(dirname(mypath), “\”, fname, sep=”.”)
f = readLines(fpath)
file
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