https://github.com/rachine/emailprediction
NLP challenge - Emaill Prediction - ALTEGRAD course
https://github.com/rachine/emailprediction
data-science kaggle machine-learning nlp
Last synced: 3 months ago
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NLP challenge - Emaill Prediction - ALTEGRAD course
- Host: GitHub
- URL: https://github.com/rachine/emailprediction
- Owner: Rachine
- License: mit
- Created: 2017-02-06T21:08:08.000Z (over 9 years ago)
- Default Branch: master
- Last Pushed: 2017-03-19T21:37:46.000Z (over 9 years ago)
- Last Synced: 2025-11-02T21:08:07.668Z (8 months ago)
- Topics: data-science, kaggle, machine-learning, nlp
- Language: Python
- Size: 1.17 MB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Authors: Authors.md
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README
# EmailPrediction
NLP challenge - Emaill Prediction - ALTEGRAD course
### Authors
- Pauline Nicolas
- Leo Treguer
- [Rachid Riad](https://rachine.github.io/)
### Code
This code is for the Kaggle Challenge for NLP class, we got 0.39075 as final score. To reproduce the result, all the utilities function are in [function.py](https://github.com/Rachine/EmailPrediction/blob/master/function.py) and to get the csv file launch [tfidf_centroid_model.py](https://github.com/Rachine/EmailPrediction/blob/master/tfidf_centroid_model.py).
### References
1] Vitor R Carvalho and William Cohen. Recommending recipients in the enron email corpus.Machine Learning,2007.
[2] David Graus, David Van Dijk, Manos Tsagkias, Wouter Weerkamp, and Maarten De Rijke. Recipient recom-mendation in enterprises using communication graphs and email content. InProceedings of the 37th internationalACM SIGIR conference on Research & development in information retrieval, pages 1079–1082. ACM, 2014.
### Acknowledgments
A part of the code is taken from this [Scikit Learn Examples](http://scikit-learn.org/).