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The Kaggel Playground House Prices: Advanced Regression Techniques
Home Page: https://www.kaggle.com/c/house-prices-advanced-regression-techniques
Jupyter Notebook 97.64%
Python 2.36%
house-prices's Introduction
- Offline cross validation: 0.10967,
- Online Leaderboard: 0.11475
- Ranking:120(Top 6%)
| Models |
XGBoost |
Lasso |
ElasticNet |
KernelRidge |
GaussianProcess |
| Offline CV |
0.11116 |
0.10977 |
0.10877 |
0.10747 |
0.10777 |
| Online LB |
0.11844 |
0.11755 |
0.11734 |
0.11695 |
0.11734 |
- Data Exploration.ipynb – jupyter notebook for data analysis
- preprocess.py – data preprocess and feature engineering
- models.py – functions about models, include loss function's definition, evaluate single model and two ensemble methods
- gride_search.py – use grid search to adjust the models' parameters
- ensemble.py – define the algorithm models and ensemble them to output the submission result
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