Kaggle-Playground

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Kaggle-Playground

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Jupyter notebooks and code examples for my Kaggle Beginners' Guide.

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- - Adjust.ipynb
  - dropped_features.txt
  - Explore.ipynb
  - Model.ipynb
  - rfe_from_forum.txt
  - Santander.ipynb
  - santander.md
- - explore.ipynb
  - knn.ipynb
- etr.py
- gbr.py
- rfr.py
- xgb.py
.gitignore
ensemble.py
features.ipynb
LICENSE
README.md
typo_dict.py
util3.py

// repository documentation

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# Kaggle-Playground

After I finished the Home Depot competition, I wrote [a summary](https://dnc1994.github.io/2016/05/rank-10-percent-in-first-kaggle-competition-en/) as a beginners' guide to Kaggle. It got retweeted by Kaggle's official account.

In this repo you can find details about how I preprocessed the dataset, constructed features, trained different models and finally built the stacked ensemble. Main files are `features.ipynb` and `ensemble.py`.
 
Feel free to suggest how to improve it!

## Acknowledgements

- [A Wonderful Kaggle Tutorial - Andrew Conti](http://nbviewer.jupyter.org/github/agconti/kaggle-titanic/blob/master/Titanic.ipynb)

- [Python Data Visualizations on Iris Dataset - Ben Hamner](https://www.kaggle.com/benhamner/d/uciml/iris/python-data-visualizations)

- [Beating Kaggle the Easy Way - Dong Ying](https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwiPxZHewLbMAhVKv5QKHb3PCGwQFggcMAA&url=http%3A%2F%2Fwww.ke.tu-darmstadt.de%2Flehre%2Farbeiten%2Fstudien%2F2015%2FDong_Ying.pdf&usg=AFQjCNE9o2BcEkqdnu_-lQ3EFD3eRAFWiw&sig2=oiU8TCEH57EYF9v9l6Scrw&bvm=bv.121070826,d.dGo)

- [Search Results Relevance 1st Place Winner's Interview - Chenglong Chen](https://github.com/ChenglongChen/Kaggle_CrowdFlower/blob/master/BlogPost/BlogPost.md)

- [(Chinese) Solution for Prudential Life Insurance Assessment - Nutastray](http://rstudio-pubs-static.s3.amazonaws.com/158725_5d2f977f4004490e9b095c0ef9357c6b.html)

## Author
[Linghao Zhang](https://github.com/dnc1994)

## License
[MIT license](https://github.com/dnc1994/Kaggle-Playground/blob/master/LICENSE)

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