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DTSTAMP:20260430T143939Z
UID:f8CddQ
DTSTART;VALUE=DATE:20220217
DTEND;VALUE=DATE:20220218
CLASS:PUBLIC
CREATED:20211220T094503
DESCRIPTION: If you would like to attend the LondonR event on 17th February
  2022\, please select your tickets below. Please stay for free networking 
 drinks after the main meeting as well. \n\n WORKSHOP\n3.30-5.30pm - 'Tidy 
 Text Classification in R: Predicting Musical Genre from Lyrics' by Daniel 
 de Bortoli and Andrew Little \n\n In this workshop\, we demonstrate an end
 -to-end text analysis workflow in R. In particular\, we focus on using tex
 t data in a supervised machine learning context\, where we attempt to pred
 ict musical genre from song lyrics. By attending you will be able to under
 stand: \n\n \n The steps typically involved in analysing text in R\, from 
 cleaning and pre-processing to a variety of widely used feature engineerin
 g techniques \n How to apply the above in supervised machine learning (cla
 ssification) \n The basics of tidymodels\, RStudio's new machine learning 
 framework in R\, including specific packages for text analysis  \n \n\n MA
 IN LONDONR MEETING\nFrom 6pm - Registration opens for LondonR meeting. \n\
 n 6.20pm – Main meeting opens with Introduction from Mango Solutions \n\
 n 6.30pm-8pm Presentations: \n\n \n 'Model UK Residential Property using i
 ndicies\, factors\, and forecast models' by Giles Heywood \n ‘Make a Twi
 tter bot with {rtweet} and GitHub Actions' by Matt Dray \n 'Modelling and 
 prediction of forest ecosystems using caret' by Lucasz Pawlik \n \n\n We w
 ill monitor Covid rules and apply any restrictions/capacity amounts that w
 e need to. The theatre is quite large and will allow for social distancing
 . \n
LAST-MODIFIED:20220616T124857
LOCATION:London South Bank University\, Keyworth Centre\, Keyworth St\, Lon
 don SE1 6NG
ORGANIZER:mailto:londonr@mango-solutions.com
SUMMARY:LondonR - 17th February 2022
URL;VALUE=URI:https://ti.to/londonr/londonr-february-2022
URL;VALUE=URI:https://ti.to/londonr/londonr-february-2022
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