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Data Cleaning on text to prepare for analysis and machine learning @ EuroSciPy 2015

3d644406158b4d440111903db1f62622?s=47 ianozsvald
August 28, 2015

Data Cleaning on text to prepare for analysis and machine learning @ EuroSciPy 2015

Dirty data makes analysis and machine learning harder (or impossible!) and more prone to failure. I'll talk on the techniques we use at ModelInsight to fix badly encoded, inconsistent and hard-to-parse text data that enable us to prepare real-world industrial data for research.

Topics will include text cleaning through normalisation and similarity measures, date parsing, data joining and visualisation. This talk is aimed at helping you make rapid progress on new projects.

Conference link:
https://www.euroscipy.org/2015/schedule/presentation/4/
Write-up:
http://ianozsvald.com/2015/08/28/euroscipy-2015-and-data-cleaning-on-text-for-ml-talk/

3d644406158b4d440111903db1f62622?s=128

ianozsvald

August 28, 2015
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  1. Data Cleaning on text to prepare for analysis and machine

    learning @ EuroSciPy 2015 Ian Ozsvald @IanOzsvald ModelInsight.io
  2. Ian.Ozsvald@ModelInsight.io @IanOzsvald EuroSciPy August 2015 Who am I? • Past

    speaker+teacher at PyDatas, EuroPythons, PyCons, PyConUKs • Co-org of PyDataLondon • O'Reilly Author • ModelInsight.io for NLP+ML IP creation in London • “I clean data” #sigh • Please learn from my mistakes
  3. Ian.Ozsvald@ModelInsight.io @IanOzsvald EuroSciPy August 2015 Unstructured data->Value • Increasing rate

    of growth and meant for human consumption • Hard to: • Extract • Parse • Make machine-readable • It is also very valuable...part of my consultancy - we're currently automating recruitment: • Uses: search, visualisation, new ML features • Most industrial problems messy, not “hard”, but time consuming! • How can we make it easier for ourselves? • “80% of our time is cleaning data” [The Internet]
  4. Ian.Ozsvald@ModelInsight.io @IanOzsvald EuroSciPy August 2015 Extracting text from PDFs &

    Word • http://textract.readthedocs.org/en/latest/ (Python) • Apache Tika (Java - via jnius?) • Difficulties • Formatting probably horrible • No semantic interpretation (e.g. CVs) • Keyword stuffing, images, out-of-order or multi-column text, tables #sigh • “Content ExtRactor and MINEr” (CERMINE) for academic papers • Commercial CV parsers (e.g. Sovren) • Do you know of other tools that add structure?
  5. Ian.Ozsvald@ModelInsight.io @IanOzsvald EuroSciPy August 2015 Extracting tables from PDFs •

    ScraperWiki's https://pdftables.com/ (builds on pdfminer) • http://tabula.technology/ (Ruby/Java OS, seems to require user intervention) • messytables (Python, lesser known, auto- guesses dtypes for CSV & HTML & PDFs) • Maybe you can help with better solutions?
  6. Ian.Ozsvald@ModelInsight.io @IanOzsvald EuroSciPy August 2015 Fixing badly encoding text •

    http://ftfy.readthedocs.org/en/latest/ • HTML unescaping: • chromium-compact-language-detector will guess human language from 80+ options (so you can choose your own decoding options) -> “Turkish I Problem (next)” • chardet: CP1252 Windows vs UTF-8
  7. Ian.Ozsvald@ModelInsight.io @IanOzsvald EuroSciPy August 2015 The “Turkish I Problem” Irish:

    dotted and dotless lowercase i mean the same thing
  8. Ian.Ozsvald@ModelInsight.io @IanOzsvald EuroSciPy August 2015 Interpreting dtypes • Use pandas

    to get text data (e.g. from JSON/CSV) • Categories (e.g. “male”/”female”) are easily spotted by eye • [“33cm”, “22inches”, ...] could be easily converted • Date parsing: • The default is for US-style (MMDD), not Euro-style (DDMM) • pd.from_csv(parse_dates=[cols], dayfirst=False) • Labix dateutil, delorean, arrow, parsedatetime (NLP) • Could you write a module to suggest possible conversions on dataframe for the user (and notify if ambiguities are present e.g. 1/1 to 12/12...MM/DD or DD/MM)?
  9. Ian.Ozsvald@ModelInsight.io @IanOzsvald EuroSciPy August 2015 Automate feature extraction? • Can

    we extract features from e.g. Id columns (products/rooms/categories)? • We could identify categorical labels and suggest Boolean column equivalents • We could remove some of the leg-work...you avoid missing possibilities, junior data scientists get “free help” • What tools do you know of and use?
  10. Ian.Ozsvald@ModelInsight.io @IanOzsvald EuroSciPy August 2015 Automated validation? • Use 'known'

    to validate 'unknown' DF?
  11. Ian.Ozsvald@ModelInsight.io @IanOzsvald EuroSciPy August 2015 Automated validation?

  12. Ian.Ozsvald@ModelInsight.io @IanOzsvald EuroSciPy August 2015 Merging two data sources •

    pd.merge(df1, df2) # exact keys, SQL- like • fuzzywuzzy/metaphone for approximate string matching • DataMade's dedupe.readthedocs.org to identify duplicates (or OpenRefine)
  13. Ian.Ozsvald@ModelInsight.io @IanOzsvald EuroSciPy August 2015 Manual Normalisation • Eyeball the

    problem, solve by hand • Lots of unit-tests! • lower() # “Accenture”->”accenture” • strip() # “ this and ”->”this and” • Beware ;nbsp& (approx 20 of these!) • replace(<pattern>,””) # “BigCo Ltd”->”BigCo” • unidecode # “áéîöũ”->”aeiou” • normalise unicode (e.g. >50 dash variants!) • NLTK stemming & WordNet ISA relt.
  14. Ian.Ozsvald@ModelInsight.io @IanOzsvald EuroSciPy August 2015 Representations of Null • Just

    have 1 (not 4!) • Consider Engarde & Hypothesis • Write a schema-checker to check all of this on the source data & in your DataFrames!
  15. Ian.Ozsvald@ModelInsight.io @IanOzsvald EuroSciPy August 2015 Automated Normalisation? • My annotate.io

    • Why not make the machine do this for us? No regular expressions! No fiddling!
  16. Ian.Ozsvald@ModelInsight.io @IanOzsvald EuroSciPy August 2015 Visualising new data sources •

    GlueViz (numeric) • SeaBorn • setosa.io/csv-fingerprint/ • Do you have good tools?
  17. Ian.Ozsvald@ModelInsight.io @IanOzsvald EuroSciPy August 2015 Augmenting Data • Alchemy -

    sentiment and entities • DBPedia - entities – http://dbpedia.org/data/Barclays.json • NLTK • SpaCy • Use ML... but don't forget regexs and other simple techniques
  18. Ian.Ozsvald@ModelInsight.io @IanOzsvald EuroSciPy August 2015 Starting ML on Text •

    SKLearn's CountVectorizer for binary features • BernoulliNaiveBayes (then LogReg) • Favour better data & features • Diagnose failures at each stage • Avoid complex models until you need them
  19. Ian.Ozsvald@ModelInsight.io @IanOzsvald EuroSciPy August 2015 Closing... • Give me feedback

    on annotate.io • Give me your dirty-data horror stories, I want to fix some of these problems • http://ianozsvald.com/ • PyDataLondon monthly meetup • Do you have data science deployment stories for my keynote at BudapestBIForum? What's “hardest” in (data) science for your team?