How to get started in Kaggle competition

•

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How to get started in Kaggle competition for data scientists.

Données & analyses

How to get started in
Merja Kajava
September 1, 2015
Helsinki Data Analytics and Science Meetup

is a competition platform
for (aspiring) data
scientists

Why participate in Kaggle
The data
Competition steps
Tips

Why participate in
Kaggle competition?

1 Learn from the best.
Forums
Scripts
Solutions from prize winners

2 Work with cool datasets.
Flights in GE Flights Quest
Driver telematic analysis
Amazon employee access
rights

+1 You can also win money

What kinds of
competitions Kaggle has?

Public
In-class
Private

What languages can you
use?
Any open-source language
(sometimes also sponsor’s
proprietary languages)
Gnu Octave
(no Matlab)

What is the data like?

Data comes from companies
and non-profit organizations

Data sizes vary
Zip ~1 MB
Zip ~6 GB

Data comes in all shapes
Customer data
Log files
Timeseries
HTML pages
Images
Documents

How does Kaggle
competition work?

Competition flow
Duration typically 4 to 8 weeks
Max. 5 entries per day

test
Model
train
submission.csvPredict
Build prediction model
Calculate CV to
cross-validate

Evaluate submission
Typical evaluations
Area under the ROC curve
Normalized Gini coefficient
RMSLE
…

Public leaderboard
Private leaderboard
~10-30% of test data
submission.csv
Submit entry

Choose two entries for final
Practical choice
Best entry in public leaderboard
+
Best CV from local entries

Tips

Look at data. Visualize it.
Source
https://www.kaggle.com/justfor/liberty-mutual-group-property-inspection-prediction/explore-data/notebook
https://www.kaggle.com/odiseo1982/liberty-mutual-group-property-inspection-prediction/compare-variables-between-train-and-test/files

Focus on feature engineering
Feature selection
Feature construction
Dates
Locations
Categories
Segmentation
Statistics

Build different models
3 target variables
4 cities
=
Build 12 models
Source
https://www.kaggle.com/c/see-click-predict-fix/visualization/1390

Try different algorithms
Random forest
Vowpal Wabbit
GBM
Xgboost

Build ensembles
Average of submissions
Weighted average of submissions
Ranked average of submissions
Stacked generalization
Blending

Keep track of your
submissions
Submission id

Next steps

Start competing
Create Kaggle account
Choose competition
Go for it!

Useful links
Kaggle Blog
http://blog.kaggle.com
Kaggle Competitions: Where to begin
http://www.analyticsvidhya.com/blog/2015/06/start-journey-kaggle/
Kaggle Feature Engineering
http://machinelearningmastery.com/discover-feature-engineering-how-to-
engineer-features-and-how-to-get-good-at-it/
Kaggle Ensembling Guide
http://mlwave.com/kaggle-ensembling-guide/

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Elements of a Great API Reference

Elements of a Great API Reference

Findings From City Bike Open Data - VeloFinland 2019

Findings From City Bike Open Data - VeloFinland 2019

Findings From City Bike Open Data - VeloFinland 2019

Prototyping with OpenAPI

Prototyping with OpenAPI

Prototyping with OpenAPI

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API-trendit ja niiden vaikutus käytännössä

API-trendit ja niiden vaikutus käytännössä

Spatial Open Data with APIs

Spatial Open Data with APIs

Spatial Open Data with APIs

APIDays Finland 2019 - How to Choose the Ultimate AI Platform

APIDays Finland 2019 - How to Choose the Ultimate AI Platform

APIDays Finland 2019 - How to Choose the Ultimate AI Platform

Plus de Merja Kajava (6)

Elements of a Great API Reference

Elements of a Great API Reference

Elements of a Great API Reference

Findings From City Bike Open Data - VeloFinland 2019

Findings From City Bike Open Data - VeloFinland 2019

Findings From City Bike Open Data - VeloFinland 2019

Prototyping with OpenAPI

Prototyping with OpenAPI

Prototyping with OpenAPI

API-trendit ja niiden vaikutus käytännössä

API-trendit ja niiden vaikutus käytännössä

API-trendit ja niiden vaikutus käytännössä

Spatial Open Data with APIs

Spatial Open Data with APIs

Spatial Open Data with APIs

APIDays Finland 2019 - How to Choose the Ultimate AI Platform

APIDays Finland 2019 - How to Choose the Ultimate AI Platform

APIDays Finland 2019 - How to Choose the Ultimate AI Platform

Dernier

Yilin Xia (yilinx2@illinois.edu), Shawn Bowers (bowers@gonzaga.edu), Lan Li (lanl2@illinois.edu), and Bertram Ludäscher (ludaesch@illinois.edu) Presented at IDCC-2024 in Edinburg. ABSTRACT. We propose a new approach for modeling and reconciling conflicting data cleaning actions. Such conflicts arise naturally in collaborative data curation settings where multiple experts work independently and then aim to put their efforts together to improve and accelerate data cleaning. The key idea of our approach is to model conflicting updates as a formal argumentation framework (AF). Such argumentation frameworks can be automatically analyzed and solved by translating them to a logic program PAF whose declarative semantics yield a transparent solution with many desirable properties, e.g., uncontroversial updates are accepted, unjustified ones are rejected, and the remaining ambiguities are exposed and presented to users for further analysis. After motivating the problem, we introduce our approach and illustrate it with a detailed running example introducing both well-founded and stable semantics to help understand the AF solutions. We have begun to develop open source tools and Jupyter notebooks that demonstrate the practicality of our approach. In future work we plan to develop a toolkit for conflict resolution that can be used in conjunction with OpenRefine, a popular interactive data cleaning tool.

Reconciling Conflicting Data Curation Actions: Transparency Through Argument...

Reconciling Conflicting Data Curation Actions: Transparency Through Argument...

Reconciling Conflicting Data Curation Actions: Transparency Through Argument...

Bertram Ludäscher

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SAC 25 Final National, Regional & Local Angel Group Investing Insights 2024 0...

SAC 25 Final National, Regional & Local Angel Group Investing Insights 2024 0...

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Digital Transformation Playbook by Graham Ware

Digital Transformation Playbook by Graham Ware

Digital Transformation Playbook by Graham Ware

Discover Why Less is More in B2B Research

Discover Why Less is More in B2B Research

Discover Why Less is More in B2B Research

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Statistics notes ,it includes mean to index numbers

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如何办理英国诺森比亚大学毕业证（NU毕业证书）成绩单原件一模一样

如何办理英国诺森比亚大学毕业证（NU毕业证书）成绩单原件一模一样

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DATA SUMMIT 24 Building Real-Time Pipelines With FLaNK

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Reconciling Conflicting Data Curation Actions: Transparency Through Argument...

Reconciling Conflicting Data Curation Actions: Transparency Through Argument...

Reconciling Conflicting Data Curation Actions: Transparency Through Argument...

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SAC 25 Final National, Regional & Local Angel Group Investing Insights 2024 0...

SAC 25 Final National, Regional & Local Angel Group Investing Insights 2024 0...

Digital Transformation Playbook by Graham Ware

Digital Transformation Playbook by Graham Ware

Digital Transformation Playbook by Graham Ware

Discover Why Less is More in B2B Research

Discover Why Less is More in B2B Research

Discover Why Less is More in B2B Research

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Statistics notes ,it includes mean to index numbers

Statistics notes ,it includes mean to index numbers

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How to get started in Kaggle competition

1. How to get started in Merja Kajava September 1, 2015 Helsinki Data Analytics and Science Meetup

2. is a competition platform for (aspiring) data scientists

3.

4. Why participate in Kaggle The data Competition steps Tips

5. Why participate in Kaggle competition?

6. 1 Learn from the best. Forums Scripts Solutions from prize winners

7. 2 Work with cool datasets. Flights in GE Flights Quest Driver telematic analysis Amazon employee access rights

8. +1 You can also win money

9. What kinds of competitions Kaggle has?

10. Public In-class Private

11. What languages can you use? Any open-source language (sometimes also sponsor’s proprietary languages) Gnu Octave (no Matlab)

12. What is the data like?

13. Data comes from companies and non-profit organizations

14. Data sizes vary Zip ~1 MB Zip ~6 GB

15. Data comes in all shapes Customer data Log files Timeseries HTML pages Images Documents

16. How does Kaggle competition work?

17. Competition flow Duration typically 4 to 8 weeks Max. 5 entries per day

18. test Model train submission.csvPredict Build prediction model Calculate CV to cross-validate

19. Evaluate submission Typical evaluations Area under the ROC curve Normalized Gini coefficient RMSLE …

20. Public leaderboard Private leaderboard ~10-30% of test data submission.csv Submit entry

21. Choose two entries for final Practical choice Best entry in public leaderboard + Best CV from local entries

23. Look at data. Visualize it. Source https://www.kaggle.com/justfor/liberty-mutual-group-property-inspection-prediction/explore-data/notebook https://www.kaggle.com/odiseo1982/liberty-mutual-group-property-inspection-prediction/compare-variables-between-train-and-test/files

24. Focus on feature engineering Feature selection Feature construction Dates Locations Categories Segmentation Statistics

25. Build different models 3 target variables 4 cities = Build 12 models Source https://www.kaggle.com/c/see-click-predict-fix/visualization/1390

26. Try different algorithms Random forest Vowpal Wabbit GBM Xgboost

27. Build ensembles Average of submissions Weighted average of submissions Ranked average of submissions Stacked generalization Blending

28. Keep track of your submissions Submission id

30. Start competing Create Kaggle account Choose competition Go for it!

31. Useful links Kaggle Blog http://blog.kaggle.com Kaggle Competitions: Where to begin http://www.analyticsvidhya.com/blog/2015/06/start-journey-kaggle/ Kaggle Feature Engineering http://machinelearningmastery.com/discover-feature-engineering-how-to- engineer-features-and-how-to-get-good-at-it/ Kaggle Ensembling Guide http://mlwave.com/kaggle-ensembling-guide/