https://github.com/garethjns/kaggle-titanic
Trying out things on Kaggle's Titanic dataset
https://github.com/garethjns/kaggle-titanic
aif360 fairness-ml kaggle-titanic lightgbm sklearn
Last synced: about 1 month ago
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Trying out things on Kaggle's Titanic dataset
- Host: GitHub
- URL: https://github.com/garethjns/kaggle-titanic
- Owner: garethjns
- Created: 2017-05-12T20:57:24.000Z (about 8 years ago)
- Default Branch: master
- Last Pushed: 2021-07-09T10:34:06.000Z (almost 4 years ago)
- Last Synced: 2025-04-20T19:37:15.499Z (about 2 months ago)
- Topics: aif360, fairness-ml, kaggle-titanic, lightgbm, sklearn
- Language: Jupyter Notebook
- Homepage:
- Size: 356 KB
- Stars: 6
- Watchers: 1
- Forks: 6
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Kaggle-Titanic
This repo contains an number of scripts and notebooks trying out things on the Titanic dataset on Kaggle.
## [TitanicSexism](https://github.com/garethjns/Kaggle-Titanic/blob/master/titanicsexism-fairness-in-ml.ipynb)
A tongue-in-cheek look at removing the gender bias in the dataset, includes:
- Using [IBMs AIF360](https://github.com/IBM/AIF360) to evaluate bias in data and models, and trying out reweighing as a method to remove the bias.
- An object-orientated approach to preprocessing using sklearn Pipelines, with custom transformers.
See also: https://www.kaggle.com/garethjns/titanicsexism-fairness-in-ml
## [Shortest Titanic kernel](https://github.com/garethjns/Kaggle-Titanic/blob/master/shortest-titanic-kernel.ipynb)
A fork of [this kernel](https://www.kaggle.com/pavlofesenko/shortest-titanic-kernel-0-78468). Attempts to create a reasonably scoring model using the least code possible. A great example of how not to programme.
See also: https://www.kaggle.com/garethjns/shortest-titanic-kernel-0-78468
## LightGBM
Examples working with [Microsoft's LightGBM](https://github.com/microsoft/LightGBM)
### [lightgbm.ipynb](https://github.com/garethjns/Kaggle-Titanic/blob/master/lightgbm.ipynb)
Introduction to pre-processing and preparing the data to use in LightGBM.
See also: https://www.kaggle.com/garethjns/microsoft-lightgbm-0-795
### [lightgbm_run.py](https://github.com/garethjns/Kaggle-Titanic/blob/master/lightgbm_run.py)
Script to prepare data, grid search best model parameters, fit a (slightly more) robust ensemble on multiple data splits. Can score about 0.822 (top 3%) with a lucky random seed.
See also: https://www.kaggle.com/garethjns/microsoft-lightgbm-with-parameter-tuning-0-822
## [3 seconds and 3 features](https://github.com/garethjns/Kaggle-Titanic/blob/master/3_seconds_3_features.py)
A very simple and fast script fitting a logistic regression model with almost no preprocessing. Can score in the top 10% with a lucky random seed, and is a good example of why such a small dataset is terrible for model performance evaluation!
See also: https://www.kaggle.com/garethjns/3-seconds-and-3-features-top-10