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
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
