Data science: use cases and tools
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Two examples: advertisement and moderation in online classifieds
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Data science: use cases and tools
- 1. Data Science: Use Cases and Tools Alexey Grigorev 28/05/2020
- 4. Plan ● Use cases ○ Advertisement ○ Moderation in Online Classifieds ● Base skills
- 7. Exchange
- 8. Exchange
- 13. Exchange $0.10
- 14. Exchange
- 15. DSP1 ● What do we know about the user?
- 16. DSP1 ● What do we know about the user? ● What are they interested in?
- 17. DSP1 ● What do we know about the user? ● What are they interested in? ● What’s the probability of click and conversion?
- 18. DSP1 ● What do we know about the user? ● What are they interested in? ● What’s the probability of click and conversion? ● How much are we willing to pay for it? $0.10
- 19. DSP1 ● What do we know about the user? ● What are they interested in? ● What’s the probability of click and conversion? ● How much are we willing to pay for it? ● Should we show any ad?
- 20. DSP1 ● What do we know about the user? ● What are they interested in? ● What’s the probability of click and conversion? ● How much are we willing to pay for it? ● Should we show any ad?
- 21. Probability of click Estimate the probability that the user clicks on the ad
- 22. Probability of click ● Device characteristics (e.g. OS) ● Geography (country, city) ● Demography (gender, age) ● History: visited pages, installed apps
- 23. Probability of conversion Estimate the probability that the user will buy the product after clicking
- 24. Probability of conversion ● Device characteristics (e.g. OS) ● Geography (country, city) ● Demography (gender, age) ● History: visited pages, installed apps ● Features of the advertiser (how convenient the page is, etc)
- 25. Tools For data scientists ● SQL (AWS Athena) ● Apache Spark ● Scikit-Learn ● Own tools (e.g. FTRL)
- 26. Plan ● Use cases ○ Advertisement ○ Moderation in Online Classifieds ● Base skills
- 27. Classified Advertisement Such description. So much text
- 28. Such description. So much text Such description. So much text Such description. So much text
- 30. Problems ● Illegal goods ● NSFW content ● Duplicates ● Spam ● Fraud
- 31. Moderation Such description So much text
- 32. ML Such description So much text Automated moderation
- 33. ML Such description So much text Automated moderation
- 34. ML Such description So much text Ad queue MP Moderation panel Moderators Automated moderation
- 35. ML Such description So much text Ad queue Automated moderation Duplicate detection Illegal items detection Other models
- 36. Illegal goods ● Analyse the title and description ● Analyse the image
- 37. Duplicates ● How similar the listing is to other listings ● IP addresses, device signature ● How many other ads the user postes ● City, category
- 38. Tools For data scientists ● SQL (AWS Athena) ● Scikit-Learn ● TensforFlow, Apache MXNet ● Flask
- 39. Plan ● Use cases ○ Advertisement ○ Moderation in Online Classifieds ● Base skills
- 40. Base skills ● SQL, data manipulation ● Git ● Python ● NumPy, Pandas, Scikit-Learn ● Training and validating models ● Microservices, Flask, Docker
- 41. How to learn? ● Come up with a problem ● Look for solution (tools, libraries, tutorials) ● Solve the problem ● … ● Profit
- 42. How to learn? ● Come up with a problem ⇐ Important! Focus on the problem ● Look for solution (tools, libraries, tutorials) ● Solve the problem ● … ● Profit
- 43. mlbookcamp.com ● Learn ML by doing projects ● http://bit.ly/mlbookcamp ● Get 40% off with code “grigorevpc” ● Twitter: @Al_Grigor (book give-away this Sunday!) Machine Learning Bookcamp