Kaggle比赛（二）House Prices: Advanced Regression Techniques

Kaggle房价预测实战：特征工程与模型融合

最新推荐文章于 2025-08-01 17:05:56 发布

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最新推荐文章于 2025-08-01 17:05:56 发布 · 3.4k 阅读

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#Kaggle #机器学习

本文作者通过参与Kaggle的House Prices: Advanced Regression Techniques比赛，学习并实践了数据分析、特征工程和集成学习。文章介绍了数据预处理、离群值处理、特征相关性分析、缺失值处理、Box-Cox变换、独热编码以及多种模型的建立与融合。最终在Leaderboard上取得了0.11676的评分。

房价预测是我入门Kaggle的第二个比赛，参考学习了他人的一篇优秀教程：https://www.kaggle.com/serigne/stacked-regressions-top-4-on-leaderboard

通过Serigne的这篇notebook，我学习到了关于数据分析、特征工程、集成学习等等很多有用的知识，在这里感谢一下这位大佬。

本篇文章立足于Serigne的教程，将他的大部分代码实现了一遍，修正了个别小错误，也加入了自己的一些视角和思考，做了一些自认为reasonable的“改进”。最终在Leaderboard上的得分为0.11676，排名前13%。虽然最后结果反而变差了一点（没有道理啊！QAQ），但我觉得整个实践的过程仍然值得记录一下。

废话不多说，下面进入正文。

数据集概览

导入相关Python包：

#import some necessary librairies

import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
%matplotlib inline
import matplotlib.pyplot as plt  # Matlab-style plotting
import seaborn as sns
color = sns.color_palette()
sns.set_style('darkgrid')
import warnings
def ignore_warn(*args, **kwargs):
    pass
warnings.warn = ignore_warn #ignore annoying warning (from sklearn and seaborn)

from scipy import stats
from scipy.stats import norm, skew #for some statistics

pd.set_option('display.float_format', lambda x: '{:.3f}'.format(x)) #Limiting floats output to 3 decimal points

from subprocess import check_output
print(check_output(["ls", "../input"]).decode("utf8")) #check the files available in the directory

sample_submission.csv
test.csv
train.csv

读取csv文件：

train = pd.read_csv('datasets/train.csv')
test = pd.read_csv('datasets/test.csv')

查看数据集前5行：

train.head(5)

	Id	MSSubClass	MSZoning	LotFrontage	LotArea	Street	Alley	LotShape	LandContour	Utilities	...	PoolQC	Fence	MiscFeature	MoSold	YrSold	SaleType	SaleCondition	SalePrice
0	1	60	RL	65.000	8450	Pave	NaN	Reg	Lvl	AllPub	...	NaN	NaN	NaN	2	2008	WD	Normal	208500
1	2	20	RL	80.000	9600	Pave	NaN	Reg	Lvl	AllPub	...	NaN	NaN	NaN	5	2007	WD	Normal	181500
2	3	60	RL	68.000	11250	Pave	NaN	IR1	Lvl	AllPub	...	NaN	NaN	NaN	9	2008	WD	Normal	223500
3	4	70	RL	60.000	9550	Pave	NaN	IR1	Lvl	AllPub	...	NaN	NaN	NaN	2	2006	WD	Abnorml	140000
4	5	60	RL	84.000	14260	Pave	NaN	IR1	Lvl	AllPub	...	NaN	NaN	NaN	12	2008	WD	Normal	250000

5 rows × 81 columns

test.head(5)

	Id	MSSubClass	MSZoning	LotFrontage	LotArea	Street	Alley	LotShape	LandContour	Utilities	...	ScreenPorch	PoolQC	Fence	MiscFeature	MiscVal	MoSold	YrSold	SaleType	SaleCondition
0	1461	20	RH	80.000	11622	Pave	NaN	Reg	Lvl	AllPub	...	120	NaN	MnPrv	NaN	0	6	2010	WD	Normal
1	1462	20	RL	81.000	14267	Pave	NaN	IR1	Lvl	AllPub	...	0	NaN	NaN	Gar2	12500	6	2010	WD	Normal
2	1463	60	RL	74.000	13830	Pave	NaN	IR1	Lvl	AllPub	...	0	NaN	MnPrv	NaN	0	3	2010	WD	Normal
3	1464	60	RL	78.000	9978	Pave	NaN	IR1	Lvl	AllPub	...	0	NaN	NaN	NaN	0	6	2010	WD	Normal
4	1465	120	RL	43.000	5005	Pave	NaN	IR1	HLS	AllPub	...	144	NaN	NaN	NaN	0	1	2010	WD