https://github.com/ageitgey/titanic_machine_learning_example
A simple example of how to solve Kaggle's "Titanic: Machine Learning from Disaster" challenge using Python and scikit-learn
https://github.com/ageitgey/titanic_machine_learning_example
Last synced: about 2 months ago
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A simple example of how to solve Kaggle's "Titanic: Machine Learning from Disaster" challenge using Python and scikit-learn
- Host: GitHub
- URL: https://github.com/ageitgey/titanic_machine_learning_example
- Owner: ageitgey
- License: mit
- Created: 2014-05-21T02:31:04.000Z (about 11 years ago)
- Default Branch: master
- Last Pushed: 2014-05-21T02:39:48.000Z (about 11 years ago)
- Last Synced: 2025-03-26T11:11:15.093Z (2 months ago)
- Language: Python
- Size: 172 KB
- Stars: 12
- Watchers: 2
- Forks: 12
- Open Issues: 1
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Metadata Files:
- Readme: README.md
- License: LICENSE
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README
titanic_machine_learning_example
================================
A simple example of how to solve Kaggle's
"[Titanic: Machine Learning from Disaster](http://www.kaggle.com/c/titanic-gettingStarted)"
challenge using Python and scikit-learn.
This simple example will get you about 78% accuracy. It shows you how to instantiate
and use various classifiers in scikit-learn.
Note: This example combines six different classifiers, just as example of
how to run and average multiple classifiers. You can actually get a better
accuracy by being smarter about how to combine classifiers and which ones to use.
This just shows you the scikit-learn syntax.
This example also assumes you've already done a grid search and found the best
hyper parameters for your classifiers (especially the SVM). But if you aren't
sure how to do that, the scikit-learn docs have a
[good example](http://scikit-learn.org/stable/auto_examples/grid_search_digits.html#example-grid-search-digits-py)
that you can copy.