Play Kaggle with R, Facebook V: Predicting Check Ins
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Sharing a study case from Kaggle competition, Facebook V: Predicting Check Ins data science competition. Hope will bring R users more possibilities using R doing Kaggle competition! For community sharing usage.
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Play Kaggle with R, Facebook V: Predicting Check Ins
- 1. 用 R 玩 Kaggle – 臉書打卡點預測 Play kaggle with R, Facebook V: Predicting Check Ins @Mia (R-Ladies) library(dplyr) r-ladies_global %>% filter(from = 'Taipei', travel_to = 'Lisbon')
- 2. The Agenda First Second Third Hey, Kaggle # with R-Ladies # with Masters Play R # Warm Up # EDA, Shiny Apps # Azure Jupyter Notebook Brief Intro # About R-Ladies # About me 2 Last Q&A # Recap # Resource Sharing
- 3. 1. Brief Intro Intro about R-Ladies and me! https://rladiestaipei.github.io/R-Ladies-Taipei/ 3
- 4. R-Ladies Taipei 4 Berl in Taiwa n 4 ● Founded in 2014 ● every month in Taipei, ● Our history: goo.gl/HbHNeP
- 5. Hello! I am Mia Chang (張懷文). ▪ Data Scientist, Lecturer ▪ Member of R-Ladies Taipei ▪ Co-founder of Azure Taiwan Community ▪ Microsoft Most Valuable Professionals (MVP) 2017 5
- 6. 2. Hey, Kaggle With R-Ladies and other masters! 6
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- 10. 3. Play R # Warm Up - 問題背景，問題定義 # Azure Jupiter Notebook # use Jupiter access Data # 結論 10
- 12. Warm Up - 關於這個問題背景，問題定義 12 With 8.6 million test records there are about a trillion (10^12) place-observation combinations.
- 13. Warm Up - 關於這個問題背景，問題定義 Schema row_id x y accuracy time place_id 13 EDA
- 14. “ Warm Up - 關於這個問題背景，問題定義 Three weeks into the eight-week competition, I climbed to the top of the public leaderboard with about 50 features 1. the summary data such as the number of historical check ins. 2. historical density of a place candidate, one year prior to the observation. 3.All features are rescaled if needed in order to result in similar interpretations for the train and test features. 14
- 16. EDA - 發現大家透過GPS, Wi-Fi or cellular 16 c
- 17. EDA - 尖峰打卡日 17
- 18. 玩資料 - Shinny App 18
- 19. 玩資料 - Github 19
- 20. 玩資料 - Azure Jupyter Notebook 20
- 21. 玩資料 - Azure Jupyter Notebook 21
- 22. # 演算法及結論 ＃Rcpp ＃It was expected that it would be clearly correlated with the variation in x and y but the pattern is not as obvious. Halfway through the competition I cracked the code ... 22
- 23. 4. Q&A # Recap # Action Item 23
- 24. Recap First Second Third Hey, Kaggle # with R-Ladies # with Masters Play R # Warm Up # EDA, Shiny Apps # Azure Jupyter Notebook Brief Intro # About R-Ladies # About me 24 Last Q&A # Recap # Resource Sharing
- 25. Action Item First Second Third Hi, Kaggle Play R Get your partners Visit R-Ladies R-Basic too! 25 Then ...
- 26. Thanks for your listening! 26 Look forward to your visit to R-Ladies Taipei! Also Azure Taiwan!
- 27. Bye! I am Mia Chang (張懷文) ▪ mia5419@gmail.com ▪ facebook.com/mia5419 27
- 28. 28 Take Away & Reference 1.Use EDA to help you find more feature. 2.Go to Kaggle website to get more resource to help you: forum, kernels 3.No matter you are learning R or you are going to traveling to visit other R-Ladies, call us for more resources :) 1. R-Ladies Meetup Page 2. R-Ladies Facebook Group 3. Blog Post by Tom Van de Wiele - Detail about implementation 4. Github Repository 5. Shiny App by Tom Van de Wiele - EDA that you can learn more 6. Kaggle Event Page 7. Microsoft Azure Notebooks