August 2018
Intermediate to advanced
378 pages
9h 9m
English
Content preview from R Deep Learning Essentials - Second Edition
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Preparing the data
The code to create our ratings is in Chapter9/create_recommend.R. The first part of the code runs through the raw transactional data. The data is in separate CSV files, so it processes each file, selects the records that have a customer linked (that is, CUST_CODE!="") to them, and then groups the sales by CUST_CODE and PROD_CODE_40. It then appends the results to a temporary file and moves on to the next input file:
library(magrittr)library(dplyr)library(readr)library(broom)set.seed(42)file_list <- list.files("../dunnhumby/in/", "trans*")temp_file <- "../dunnhumby/temp.csv"out_file <- "../dunnhumby/recommend.csv"if (file.exists(temp_file)) file.remove(temp_file)if (file.exists(out_file)) file.remove(out_file)options(readr.show_progress=FALSE) ...Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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