This is the Tianchi ￥250,000 house prices prediction challenge (https://tianchi.aliyun.com/competition/introduction.htm?spm=5176.100150.711.5.1ed42784KGi5ZD&raceId=231654). In the regression problem, I use different model to train the dataset, including Lasso, Elastic Net, Kernel Ridge, SVR, Gradient Boosting Regression, XGBoost, LightGBM. Finally I got a result 0.1155 of Root-Mean-Squared-Error (RMSE) on logarithm of the house price and ranked top 3% (110/3152) on Kaggle.
OS X & Linux:
git clone https://github.com/zhangchi9/Ames-Iowa-house-prices-prediction.gitWindows:
git clone https://github.com/zhangchi9/Ames-Iowa-house-prices-prediction.gitRun house_price_visualization.ipynb for data visualization.
Run Model.ipynb for model training. The dataset is already included in the repository, no need to download.
Python 3.6
Jupyter Notebook
Chi Zhang – @LinkedIn – [email protected]
Distributed under the MIT license. See LICENSE for more information.