Skip to content

Latest commit

 

History

History
187 lines (157 loc) · 29.6 KB

Kaggle Knowledge Contests.md

File metadata and controls

187 lines (157 loc) · 29.6 KB

Beginner-Friendly Kaggle Competitions

Kaggle Suggestions: Kaggle Getting Started

Types: Classification, Regression, Computer Vision, Image Processing, NLP, Optimization, Simulation

Kaggle Playground:

Competition Competition Link Notebook Github Repo Criteria
(From Tags)		 Practice Skills Marker Tutorials
Bike Sharing Demand Link Code TS
San Francisco Crime Classification Link Code C
Forest Cover Type Prediction Link Code C
The Analytics Edge (15.071x) Link Code C
Shelter Animal Outcomes Link Code C
Leaf Classification Link Code CV
New York City Taxi Fare Prediction Link Code R
TMDB Box Office Prediction Link Code C
What's Cooking? Link Code C
Dogs vs. Cats Redux: Kernels Edition Link Code CV
Aerial Cactus Identification Link Code CV
Kannada MNIST Link Code CV
Histopathologic Cancer Detection Link Code CV
Kobe Bryant Shot Selection Link Code C
Sentiment Analysis on Movie Reviews Link Code C
Ghouls, Goblins, and Ghosts... Boo! Link Code C
Northeastern SMILE Lab - Recognizing Faces in the Wild Link Code CV
What's Cooking? (Kernels Only) Link Code C
Invasive Species Monitoring Link Code CV
Random Acts of Pizza Link Code C
Store Item Demand Forecasting Challenge Link Code TS
Movie Review Sentiment Analysis (Kernels Only) Link Code C
Transfer Learning on Stack Exchange Tags Link Code C
Forest Cover Type (Kernels Only) Link Code C
Integer Sequence Learning Link Code C
CIFAR-10 - Object Recognition in Images Link Code CV
Poker Rule Induction Link Code C
Learning Social Circles in Networks Link Code C
Hash Code 2021 - Traffic Signaling Link Code O
Denoising Dirty Documents Link Code CV
Finding Elo Link Code C
Hash Code Archive - Drone Delivery Link Code O
March Machine Learning Mania 2021 - NCAAM - Spread Link Code C
Hash Code Archive - Photo Slideshow Optimization Link Code O
Open Images Object Detection RVC 2020 edition Link Code CV
Billion Word Imputation Link Code C
Halite by Two Sigma - Playground Edition Link Code S
March Machine Learning Mania 2021 - NCAAW - Spread Link Code C
Flavours of Physics: Finding τ → μμμ (Kernels Only) Link Code C
Painter by Numbers Link Code CV
Open Images Instance Segmentation RVC 2020 edition Link Code CV

"SCD" = See competition description, "-" = No suggestions present

C = Classification, CV = Computer Vision, DL = Deep Learning, G = Geospatial Analysis, O = Optimization, R = Regression, S = Simulation, TS = Time series analysis,

Kaggle Research:

Competition Competition Link Notebook Github Repo Criteria Practice Skills Marker Tutorials
The Analytics Edge (15.071x) Link Code C
Plant Pathology 2020 - FGVC7 Link Code CV
Hotel-ID to Combat Human Trafficking 2021 - FGVC8 Link Code CV
iMet Collection 2020 - FGVC7 Link Code CV
Herbarium 2022 - FGVC9 Link Code CV
Herbarium 2020 - FGVC7 Link Code CV
Plant Pathology 2021 - FGVC8 Link Code CV
Freesound General-Purpose Audio Tagging Challenge Link Code DL
COVID19 Global Forecasting (Week 1) Link Code C
COVID19 Global Forecasting (Week 4) Link Code C
COVID19 Global Forecasting (Week 3) Link Code C
PlantTraits2024 - FGVC11 Link Code CV
Sorghum -100 Cultivar Identification - FGVC 9 Link Code CV
COVID19 Global Forecasting (Week 2) Link Code C
COVID19 Local US-CA Forecasting (Week 1) Link Code C
iWildCam 2020 - FGVC7 Link Code CV
Hotel-ID to Combat Human Trafficking 2022 - FGVC9 Link Code CV
ImageNet Object Localization Challenge Link Code CV
FathomNet 2023 Link Code CV
iMaterialist (Fashion) 2020 at FGVC7 Link Code CV
GeoLifeCLEF 2022 - LifeCLEF 2022 x FGVC9 Link Code G
GeoLifeCLEF 2024 @ LifeCLEF & CVPR-FGVC Link Code G
iNaturalist Challenge at FGVC 2017 Link Code CV
iWildcam 2021 - FGVC8 Link Code CV
Multi-label Bird Species Classification - NIPS 2013 Link Code DL
iMaterialist Challenge at FGVC 2017 Link Code CV
iWildCam 2022 - FGVC9 Link Code CV
15.071x - The Analytics Edge (Spring 2015) Link -- C

"SCD" = See competition description, "-" = No suggestions present

C = Classification, CV = Computer Vision, DL = Deep Learning, G = Geospatial Analysis, O = Optimization, R = Regression, S = Simulation, TS = Time series analysis,

Kaggle Simulations:

Competition Competition Link Notebook Github Repo Criteria Practice Skills Marker Tutorials
Connect X Link Code S
Halite by Two Sigma - Playground Edition Link Code S

"SCD" = See competition description, "-" = No suggestions present

C = Classification, CV = Computer Vision, DL = Deep Learning, G = Geospatial Analysis, O = Optimization, R = Regression, S = Simulation, TS = Time series analysis,

Kaggle Competitions Instructions: Docs Comp.

Kaggle Official Learning Guide: Learn Comp

Gemini, Claude gave useless resources and suggestions. Perplexity gave useful google results and quality collection of problems.

Books on Kaggle:

  1. The Kaggle Book: Data analysis and machine learning for competitive data science
  2. The Kaggle Workbook: Self-learning exercises and valuable insights for Kaggle data science competitions
  3. https://github.com/alexmalins/kagglebook
  4. https://github.com/abhishekkrthakur/approachingalmost/blob/master/AAAMLP.pdf

Book review: https://insideainews.com/2023/06/23/book-review-the-kaggle-book-workbook/

Book discussion: https://www.youtube.com/live/Seh8ApkltLM

You have to manually search for books and test quality and relevance to kaggle, you cannot and should not depend on gpts.

GitHub Repos: (Kaggle Specific):

  1. https://github.com/PacktPublishing/Developing-Kaggle-Notebooks
  2. https://github.com/PacktPublishing/The-Kaggle-Book
  3. https://github.com/search?q=Kaggle&type=repositories

Kaggle Solutions:

  1. https://ndres.me/kaggle-past-solutions/
  2. https://www.kaggle.com/code/sudalairajkumar/winning-solutions-of-kaggle-competitions
  3. https://farid.one/kaggle-solutions/
  4. https://www.dataquest.io/blog/kaggle-getting-started/
  5. https://www.dataquest.io/blog/kaggle-tutorial/

Guidelines for Kaggle Competitions:

  1. Understand the Problem: Read the competition description thoroughly to understand the problem and the evaluation metric.
  2. Exploratory Data Analysis (EDA): Perform EDA to understand the data and identify patterns.
  3. Feature Engineering: Create new features that might improve the model's performance.
  4. Model Selection: Choose appropriate models based on the problem type (classification, regression, etc.).
  5. Model Training: Train your models using the training data.
  6. Validation: Use cross-validation to validate your model's performance.
  7. Hyperparameter Tuning: Optimize your model's hyperparameters using techniques like Grid Search or Random Search.
  8. Ensemble Methods: Combine multiple models to improve performance.
  9. Submission: Make predictions on the test set and submit your results.
  10. Learn from Others: Study the notebooks and discussions of top performers to learn new techniques.

Suggestions:

  1. https://www.kdnuggets.com/2022/05/packt-top-4-tricks-competing-kaggle-start.html
  2. https://www.kdnuggets.com/2022/11/comprehensive-list-kaggle-solutions-ideas.html
  3. https://www.kdnuggets.com/2018/11/secret-sauce-top-kaggle-competition.html
  4. https://www.coursera.org/articles/kaggle
  5. https://www.kdnuggets.com/?s=kaggle
  6. https://yanirseroussi.com/2014/08/24/how-to-almost-win-kaggle-competitions/
  7. https://www.heatonresearch.com/2015/05/25/first-kaggle.html
  8. https://www.datacamp.com/blog/kaggle-competitions-the-complete-guide
  9. https://insideainews.com/2015/01/10/data-science-101-lessons-kaggle-competitions/
  10. https://towardsdatascience.com/my-2-year-journey-on-kaggle-how-i-became-a-competition-master-ef0f0955c35d
  11. https://www.kaggle.com/discussions/general/134241
  12. https://www.linkedin.com/pulse/how-win-kaggle-competitions-derrick-mwiti
  13. https://www.hackerearth.com/practice/machine-learning/advanced-techniques/winning-tips-machine-learning-competitions-kazanova-current-kaggle-3/tutorial/
  14. https://developer.nvidia.com/blog/competition-and-community-insights-from-nvidias-kaggle-grandmasters/
  15. https://analyticsindiamag.com/developers-corner/step-by-step-guide-to-win-kaggle-competitions/

Read notebooks of best solutions from previous contests for further guidelines. And also look for YouTube tuts for further walkthroughs and insights. Finally, look for medium blogs on kaggle.

Extras:

  1. https://kaggle.com/discussions/getting-started/78482
  2. https://www.practiceprobs.com/
  3. https://www.kaggle.com/discussions/general/203318
  4. https://www.kaggle.com/discussions/general/205017
  5. https://medium.com/@gelingutz/my-first-real-kaggle-competition-a-step-by-step-guide-for-beginners-from-a-beginner-c8c6bbf1d466
  6. https://hackernoon.com/an-outsiders-journey-through-kaggle-g5c436qu
  7. https://www.youtube.com/watch?v=0ZJQ2Vsgwf0
  8. https://www.youtube.com/watch?v=RF4LwRl0npQ