R50 07e8e55842131ced568184067bdf4c34
Разработка / Тимлид

Cоревнование на kaggle - https://www.kaggle.com/c/house-prices-advance

Добавлено 09 сен 2021 в 12:41
Обзор задания:

You have some experience with R or Python and machine learning basics. This is a perfect competition for data science students who have completed an online course in machine learning and are looking to expand their skill set before trying a featured competition.

Competition Description

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.

Practice Skills

Creative feature engineering

Advanced regression techniques like random forest and gradient boosting

Acknowledgments

The Ames Housing dataset was compiled by Dean De Cock for use in data science education. It's an incredible alternative for data scientists looking for a modernized and expanded version of the often cited Boston Housing dataset.



Решение: Использовал градиентный бустинг catboost. Основная работа заключалась в выборе и подготовке признаков. Исследовал распределения признаков и их корреляцию с целевым вектором, отобрал важные признаки. Исследовал признаки на выбросы – для работы с выбросами использовал Boxcox нормализацию. Обработал категориальные признаки на предмет пропусков. Пост результат: получил оценку важности каждого признака (библиотека shap) и выгрузил результат в csv.



Результат: На тестовой выборке kaggle показал ошибку на уровне 0,14 – это средний результат.



Stack: Python, Jupiter, pandas, catboost, sklearn, scipy

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