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Starting data science with kaggle.com
- 1. Starting Data Science With Kaggle.com 6/25/2017 Starting Data Science with Kaggle.com - Nathaniel Shimoni 1Nathaniel Shimoni 25/6/2017
- 2. • What is Kaggle? • Why is Kaggle so great? The everyone wins approach • Kaggle tiers & top kagglers • Frequently used terms and the main rules • The benefits of starting with Kaggle • Common Kaggle data science process 6/25/2017 Starting Data Science with Kaggle.com Nathaniel Shimoni 2 Talk outline
- 3. • An online platform that runs data science competitions • Declares itself to be the home of data science • Has over 1M registered users & over 60k active users • One of the most vibrant communities for data scientists • A great place to meet other “data people” • A great place to learn and test your data & modeling skills 6/25/2017 Starting Data Science with Kaggle.com Nathaniel Shimoni 3 What is kaggle?
- 4. 6/25/2017 Starting Data Science with Kaggle.com Nathaniel Shimoni 4 Why is Kaggle so great? (the everyone wins approach) • Receives prizes, knowledge, exposure & portfolio showcase • Rapid development & adoption of highly performing platforms • Receives money, from competition sponsors • influence on the community • knowledge on the platforms & algo. trends • Have data & business task but no data scientists • Receives state of the art models quickly and without hiring data scientists
- 5. 6/25/2017 Starting Data Science with Kaggle.com Nathaniel Shimoni 5 My Kaggle profile
- 6. • Novice – a new Kaggle user • Contributor – participated in one or more competitions, ran a kernel, and is active in the forums • Expert – 2 top 25% finishes • Master - 2 top 10% finishes, & 1 top 10 (places) finish • Grandmaster – 5 top 10 finishes & 1 solo top 10 finish 6/25/2017 Starting Data Science with Kaggle.com Nathaniel Shimoni 6 Kaggle tiers
- 7. 6/25/2017 Starting Data Science with Kaggle.com Nathaniel Shimoni 7 Top Kagglers
- 8. • Leaderboard (public & private) 6/25/2017 Starting Data Science with Kaggle.com Nathaniel Shimoni 8 Frequently used terms Training Public LB Private LB Available once approved the rules Used for ranking submissions through the competition Training data Testing data Used for final scoring (the only score that truly matters) Public LB can serve as additional validation frame, but can also be source of over fitting
- 9. • Leakage - the introduction of information about the target that is not a legitimate predictor (usually by a mistake within the data preparation process) • Team merger – 2 or more participants competing together 6/25/2017 Starting Data Science with Kaggle.com Nathaniel Shimoni 9 Frequently used terms
- 10. • LB shuffle – the re-ranking that occurs at the end of the competition (upon moving from public to private LB) 6/25/2017 Starting Data Science with Kaggle.com Nathaniel Shimoni 10 Frequently used terms
- 11. 6/25/2017 Starting Data Science with Kaggle.com Nathaniel Shimoni 11 Main rules for Kaggle competitions • One account per user • No private sharing outside teams (public sharing is usually allowed and endorsed) • Limited number of entries per day & per competition • Winning solutions must be written in open source code • Winners should hand well documented source code in order to be eligible of the price • Usually select 2 solutions for final evaluation
- 12. • Project based learning – learn by doing • Solve real world challenges • Great supporting community • Benchmark solutions & shared code samples • Clear business objective and modeling task • Develop work portfolio and rank yourself against other competitors (and get recognition) • Compete against state of the art solutions • Learn (a lot!!!) when competition ends 6/25/2017 Starting Data Science with Kaggle.com Nathaniel Shimoni 12 Why start with Kaggle?
- 13. • Ability to team-up with others: learn from better Kagglers learn how to collaborate effectively merge different solutions to achieve a score boost meet new exciting people • Answer the questions of others – you only truly learn something when you teach it to someone else • Ability to apply new ideas at work with little effort • Varied areas of activity (verticals) 6/25/2017 Starting Data Science with Kaggle.com Nathaniel Shimoni 13 Why start with Kaggle?
- 14. • The ability to follow many experts where each of them specializes in a particular area (sample from my list) 6/25/2017 Starting Data Science with Kaggle.com Nathaniel Shimoni 14 Why start with Kaggle? Ensemble learning Mathias Müller Feature extraction Darius Barušauskas Validation Gert Jacobusse Super fast draft modeling ZFTurbo - unknown Inspiration – no minimal age for data science Mikel Bober-Irizar
- 15. 6/25/2017 Starting Data Science with Kaggle.com Nathaniel Shimoni 15 Common Kaggle Data Science process Data cleaning Data augmentation Adding External Data Single models Feature engineering Exploratory data analysis Single models Diverse single models Set the correct validation method Ensemble learning Final prediction EDA Feature generation modeling Ensemble learning Data cleaning & augmenting Not always allowed yet good practice to consider when possible 40%20% 30% 10% % of total time spent in each activity
- 16. • Impute missing values (mean, median, most common value, use separate prediction task) • Remove zero variance features • Remove duplicated features • Outlier removal – caution can be harmful, at cleaning stage we’ll remove irrelevant values (e.g. negative price) • Na’s encoding / imputing 6/25/2017 Starting Data Science with Kaggle.com Nathaniel Shimoni 16 Data cleaning
- 17. • External data sources: open street map weather measurement data online calendars • API’s • Scraping (using ScraPy / beautiful soup) 6/25/2017 Starting Data Science with Kaggle.com Nathaniel Shimoni 17 Data augmentation & external data
- 18. • Rescaling/ standardization of existing features • Performing data transformations: Tf-Idf, log1p, min-max scaling, binning of numeric features • Turn categorical features to numeric (label encoding / one hot encoding) • Create count features • Parsing textual features to get more generalizable features • Hashing trick • Extracting date/time features i.e DayOfWeek, month, year, dayOfMonth, isHoliday etc. 6/25/2017 Starting Data Science with Kaggle.com Nathaniel Shimoni 18 Feature engineering
- 19. • Remove near-zero-variance features • Use feature importance and eliminate least important features • Recursive Feature Elimination 6/25/2017 Starting Data Science with Kaggle.com Nathaniel Shimoni 19 Feature selection
- 20. • Grid search CV (exhaustive, rarely better than alternatives) • Random search CV • Hyper-opt • Bayesian optimization * Hyper parameter adjustment will usually yield better results but not as much as other activities 6/25/2017 Starting Data Science with Kaggle.com Nathaniel Shimoni 20 Hyper parameter optimization
- 21. • Train test split • Shuffle split • Kfold is the most commonly used • Time based separation • Group Kfold • Leave one group out 6/25/2017 Starting Data Science with Kaggle.com Nathaniel Shimoni 21 Validation
- 22. • Simple/weighted average of previous best models • Bagging of same type of models (i.e different rng, different hyper-param) • Majority vote • Using out of fold predictions as meta features a.k.a stacking 6/25/2017 Starting Data Science with Kaggle.com Nathaniel Shimoni 22 Ensemble learning
- 23. 6/25/2017 Starting Data Science with Kaggle.com Nathaniel Shimoni 23 Out Of Fold predictions – a.k.a meta features fold1 fold2 fold3 fold4 oof 1 oof 2 oof 3 oof 4 Out of fold predictions Averaged test predictions Test predictions fold1 Test predictions fold2 Test predictions fold3 Test predictions fold4 Divided training data - train on 3 folds predict the forth fold and the testing data
- 24. 6/25/2017 Starting Data Science with Kaggle.com Nathaniel Shimoni 24 Out Of Fold predictions – a.k.a meta features fold1 fold2 fold3 fold4 oof 1 oof 2 oof 3 oof 4 Out of fold predictions Averaged test predictions Test predictions fold1 Test predictions fold2 Test predictions fold3 Test predictions fold4 Divided training data - train on 3 folds predict the forth fold and the testing data
- 25. 6/25/2017 Starting Data Science with Kaggle.com Nathaniel Shimoni 25 Out Of Fold predictions – a.k.a meta features oof 1 oof 2 oof 3 oof 4 Model 1 e.g. knn Averaged test predictions Out of fold predictions oof 1 oof 2 oof 3 oof 4 Model 2 e.g. NN oof 1 oof 2 oof 3 oof 4 Model 3 e.g. gbm Train labels Model 1 e.g. knn Model 2 e.g. NN Model 3 e.g. gbm After training several models using this method (3 different models in this sample) We can now train a new model using our newly formed meta features * Note that we can either train our meta model using only these new features or use the new features along with our original train data for training
- 26. • Large focus on modeling relatively to the rest of the steps in the process • Small weight to runtime and scalability • Little reasoning for selecting a specific eval metric • Competing for the last few percent points isn’t always valuable • “Click and submit” phenomena 6/25/2017 Starting Data Science with Kaggle.com Nathaniel Shimoni 26 Disadvantages of Kaggle
- 27. • MOOC’s: Machine learning – Stanford Coursera Data science track – Johns Hopkins Coursera Udacity deep learning course • Documentation: Scikit learn documentation Keras documentation R caret package documentation 6/25/2017 Starting Data Science with Kaggle.com Nathaniel Shimoni 27 Additional reading resources
- 28. • This presentation draws heavily from the following sources: • Mark Peng’s presentation “Tips for participating Kaggle challenges” • Darius Barušauskas’s presentation “Tips and tricks to win Kaggle data science competitions” • Kaggle discussion forums and blog 6/25/2017 Starting Data Science with Kaggle.com Nathaniel Shimoni 28 Links to sources
- 29. Questions? 6/25/2017 Starting Data Science with Kaggle.com Nathaniel Shimoni 29
- 30. 6/25/2017 Starting Data Science with Kaggle.com Nathaniel Shimoni 30