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Housing prices kaggle competition. The challenge is to build a model that will predict the price of houses based on their characteristics.

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Housing prices competition: Project Preview

Description of the competition

Ask a home buyer to describe their dream house, and they probably won't begin with the height of the basement ceiling or the proximity to an east-west railroad. But this playground competition's dataset proves that much more influences price negotiations than the number of bedrooms or a white-picket fence.

With 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa, this competition challenges you to predict the final price of each home.

Data

Dataset page

Results

  • Data cleared from empty values;
  • Engineered features;
  • Dealing with outliers;
  • Normalized continuous features;
  • Stacked with StackingCVRegressor and blended regressors for predicting houses prices.

Resourses Used

  • Python Version: 3.7.4;
  • Packages: pandas, numpy, sklearn, matplotlib, seaborn, datetime, scipy, xgboost, lightgbm, mlxtend.

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Housing prices kaggle competition. The challenge is to build a model that will predict the price of houses based on their characteristics.

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