House Prices - Advanced Regression Techniques
Competition link.
https://www.kaggle.com/competitions/house-prices-advanced-regression-techniques
Description of the data in the Data tab. You can read about the competition metric in Overview.
Useful information about models, validation, approaches can be found in the Code and Discussion tabs.
Also attaching the file `titanic_feature_engineering.ipynb`, recommend reviewing it to gain more insights into the feature engineering process.
To submit the results, you will need to create a notebook on Kaggle, make it public, train the model/models, submit the prediction for the test data from the notebook, and send the notebook link for review.
Example of saving the submission on the test:
sample_submission_df = pd.read_csv('../input/house-prices-advanced-regression-techniques/sample_submission.csv')
sample_submission_df['SalePrice'] = model.predict(test_df)
sample_submission_df.to_csv('/kaggle/working/submission.csv', index=False)
After that, the created `submission.csv` file needs to be submitted in the Kaggle competition tab.
Applications 2
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2 days89 USD
199 2 days89 USD
Good day!
I will gladly help improve the result to a public score ~0.16 by combining dataset features, analyzing the importance of their impact on the result, and using the GradientBoostingRegressor model.
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5 days111 USD
1544 18 1 5 days111 USD
Hello.
I will gladly help you with your task.
I have extensive experience in creating mathematical regression models, including tuning.
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