https://github.com/gereleth/kaggle-telstra
My code for Telstra Network Disruptions Kaggle competition
https://github.com/gereleth/kaggle-telstra
Last synced: 3 months ago
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My code for Telstra Network Disruptions Kaggle competition
- Host: GitHub
- URL: https://github.com/gereleth/kaggle-telstra
- Owner: gereleth
- Created: 2016-03-04T11:56:51.000Z (over 9 years ago)
- Default Branch: master
- Last Pushed: 2016-10-18T12:57:35.000Z (almost 9 years ago)
- Last Synced: 2025-03-21T01:11:12.622Z (7 months ago)
- Language: Jupyter Notebook
- Size: 3.27 MB
- Stars: 74
- Watchers: 3
- Forks: 38
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Telstra Network Disruptions
My code for [Telstra Network Disruptions](https://www.kaggle.com/c/telstra-recruiting-network) Kaggle competition.
This code has a companion blog post with my [competition writeup](http://gereleth.github.io/Telstra-Network-Disruptions-Writeup/).
Competition data can be downloaded [here](https://www.kaggle.com/c/telstra-recruiting-network/data) and should go into the `data` folder. `data` folder also contains my final ensemble's predictions for the test set and out-of-fold predictions for the train set.
See my notebooks for:
* [Automatic model tuning with Sacred and Hyperopt](https://github.com/gereleth/kaggle-telstra/blob/master/Automatic%20model%20tuning%20with%20Sacred%20and%20Hyperopt.ipynb)
* [Discovering the magic feature and some visualizations](https://github.com/gereleth/kaggle-telstra/blob/master/Discovering%20the%20magic%20feature.ipynb)
* [Neural Net and Xgboost models and my blending approach](https://github.com/gereleth/kaggle-telstra/blob/master/NN%20and%20XGB%20models%20%2B%20my%20blending%20approach.ipynb)
* [Global Refinement of Random Forest](https://github.com/gereleth/kaggle-telstra/blob/master/Global%20refinement%20of%20random%20forest.ipynb)
To be added later:
* Calibrating probabilities
Code for loading data and building features is in `src/telstra_data.py`.