Predicting Sales Prices of the Houses Using Regression Methods of Machine Learning

被引：0

作者：

Viktorovich, Parasich Andrey ^{[1
]}

Aleksandrovich, Parasich Viktor ^{[1
]}

Leopoldovich, Kaftannikov Igor ^{[1
]}

Vasilevna, Parasich Irina ^{[1
]}

机构：

[1] South Ural State Univ, Chelyabinsk, Russia

来源：

PROCEEDINGS OF THE 2018 3RD RUSSIAN-PACIFIC CONFERENCE ON COMPUTER TECHNOLOGY AND APPLICATIONS (RPC) | 2018年

关键词：

machine learning; Kaggle; neural networks; boosting; regression; SELECTION;

D O I：

暂无

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

In this article we will describe our solution for "House Prices: Advanced Regression Techniques" machine learning competition, which was held on Kaggle platform. The competitor's goal was to predict house's sale price by their attributes like house area, year of building, etc. In our solution, we use classic machine learning algorithms, and our original methods, which will be described here. At the end of the competition, we took 18th place among 2124 participants from whole world (top 1%).

引用

页数：5

共 6 条

[1] Ames, Iowa: Alternative to the Boston Housing Data as an End of Semester Regression Project [J].