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房价预测

比赛说明

  • 房价预测
  • 要求购房者描述他们的梦想之家，他们可能不会从地下室天花板的高度或与东西方铁路的接近度开始。但是这个游乐场比赛的数据集证明，对价格谈判的影响远远超过卧室或白色栅栏的数量。
  • 有79个解释变量描述（几乎）爱荷华州埃姆斯的住宅房屋的每个方面，这个竞赛挑战你预测每个房屋的最终价格。

参赛成员

比赛分析

  • 回归问题：价格的问题
  • 常用算法： 回归、树回归、GBDT、xgboost、lightGBM
步骤:
一. 数据分析
1. 下载并加载数据
2. 总体预览:了解每列数据的含义,数据的格式等
3. 数据初步分析,使用统计学与绘图:初步了解数据之间的相关性,为构造特征工程以及模型建立做准备

二. 特征工程
1.根据业务,常识,以及第二步的数据分析构造特征工程.
2.将特征转换为模型可以辨别的类型(如处理缺失值,处理文本进行等)

三. 模型选择
1.根据目标函数确定学习类型,是无监督学习还是监督学习,是分类问题还是回归问题等.
2.比较各个模型的分数,然后取效果较好的模型作为基础模型.

四. 模型融合
1. 可以参考泰坦尼克号的简单模型融合方式，通过对模型的对比打分方式选择合适的模型
2. 在房价预测里我们使用模型融合的方法来输出结果，最终的效果很好。

五. 修改特征和模型参数
1.可以通过添加或者修改特征,提高模型的上限.
2.通过修改模型的参数,是模型逼近上限

一. 数据分析

数据下载和加载

# 导入相关数据包
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline

from scipy import stats
from scipy.stats import norm

root_path = '/opt/data/kaggle/getting-started/house-prices'

train = pd.read_csv('%s/%s' % (root_path, 'train.csv'))
test = pd.read_csv('%s/%s' % (root_path, 'test.csv'))

特征说明

train.columns

Index(['Id', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street',
       'Alley', 'LotShape', 'LandContour', 'Utilities', 'LotConfig',
       'LandSlope', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType',
       'HouseStyle', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd',
       'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType',
       'MasVnrArea', 'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual',
       'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinSF1',
       'BsmtFinType2', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', 'Heating',
       'HeatingQC', 'CentralAir', 'Electrical', '1stFlrSF', '2ndFlrSF',
       'LowQualFinSF', 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath',
       'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'KitchenQual',
       'TotRmsAbvGrd', 'Functional', 'Fireplaces', 'FireplaceQu', 'GarageType',
       'GarageYrBlt', 'GarageFinish', 'GarageCars', 'GarageArea', 'GarageQual',
       'GarageCond', 'PavedDrive', 'WoodDeckSF', 'OpenPorchSF',
       'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'PoolQC',
       'Fence', 'MiscFeature', 'MiscVal', 'MoSold', 'YrSold', 'SaleType',
       'SaleCondition', 'SalePrice'],
      dtype='object')


train.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1460 entries, 0 to 1459
Data columns (total 81 columns):
Id               1460 non-null int64
MSSubClass       1460 non-null int64
MSZoning         1460 non-null object
LotFrontage      1201 non-null float64
LotArea          1460 non-null int64
Street           1460 non-null object
Alley            91 non-null object
LotShape         1460 non-null object
LandContour      1460 non-null object
Utilities        1460 non-null object
LotConfig        1460 non-null object
LandSlope        1460 non-null object
Neighborhood     1460 non-null object
Condition1       1460 non-null object
Condition2       1460 non-null object
BldgType         1460 non-null object
HouseStyle       1460 non-null object
OverallQual      1460 non-null int64
OverallCond      1460 non-null int64
YearBuilt        1460 non-null int64
YearRemodAdd     1460 non-null int64
RoofStyle        1460 non-null object
RoofMatl         1460 non-null object
Exterior1st      1460 non-null object
Exterior2nd      1460 non-null object
MasVnrType       1452 non-null object
MasVnrArea       1452 non-null float64
ExterQual        1460 non-null object
ExterCond        1460 non-null object
Foundation       1460 non-null object
BsmtQual         1423 non-null object
BsmtCond         1423 non-null object
BsmtExposure     1422 non-null object
BsmtFinType1     1423 non-null object
BsmtFinSF1       1460 non-null int64
BsmtFinType2     1422 non-null object
BsmtFinSF2       1460 non-null int64
BsmtUnfSF        1460 non-null int64
TotalBsmtSF      1460 non-null int64
Heating          1460 non-null object
HeatingQC        1460 non-null object
CentralAir       1460 non-null object
Electrical       1459 non-null object
1stFlrSF         1460 non-null int64
2ndFlrSF         1460 non-null int64
LowQualFinSF     1460 non-null int64
GrLivArea        1460 non-null int64
BsmtFullBath     1460 non-null int64
BsmtHalfBath     1460 non-null int64
FullBath         1460 non-null int64
HalfBath         1460 non-null int64
BedroomAbvGr     1460 non-null int64
KitchenAbvGr     1460 non-null int64
KitchenQual      1460 non-null object
TotRmsAbvGrd     1460 non-null int64
Functional       1460 non-null object
Fireplaces       1460 non-null int64
FireplaceQu      770 non-null object
GarageType       1379 non-null object
GarageYrBlt      1379 non-null float64
GarageFinish     1379 non-null object
GarageCars       1460 non-null int64
GarageArea       1460 non-null int64
GarageQual       1379 non-null object
GarageCond       1379 non-null object
PavedDrive       1460 non-null object
WoodDeckSF       1460 non-null int64
OpenPorchSF      1460 non-null int64
EnclosedPorch    1460 non-null int64
3SsnPorch        1460 non-null int64
ScreenPorch      1460 non-null int64
PoolArea         1460 non-null int64
PoolQC           7 non-null object
Fence            281 non-null object
MiscFeature      54 non-null object
MiscVal          1460 non-null int64
MoSold           1460 non-null int64
YrSold           1460 non-null int64
SaleType         1460 non-null object
SaleCondition    1460 non-null object
SalePrice        1460 non-null int64
dtypes: float64(3), int64(35), object(43)
memory usage: 924.0+ KB

特征详情

train.head(5)
Id MSSubClass MSZoning LotFrontage LotArea Street Alley LotShape LandContour Utilities ... PoolArea PoolQC Fence MiscFeature MiscVal MoSold YrSold SaleType SaleCondition SalePrice
0 1 60 RL 65.0 8450 Pave NaN Reg Lvl AllPub ... 0 NaN NaN NaN 0 2 2008 WD Normal 208500
1 2 20 RL 80.0 9600 Pave NaN Reg Lvl AllPub ... 0 NaN NaN NaN 0 5 2007 WD Normal 181500
2 3 60 RL 68.0 11250 Pave NaN IR1 Lvl AllPub ... 0 NaN NaN NaN 0 9 2008 WD Normal 223500
3 4 70 RL 60.0 9550 Pave NaN IR1 Lvl AllPub ... 0 NaN NaN NaN 0 2 2006 WD Abnorml 140000
4 5 60 RL 84.0 14260 Pave NaN IR1 Lvl AllPub ... 0 NaN NaN NaN 0 12 2008 WD Normal 250000

5 rows × 81 columns

特征分析（统计学与绘图）

每一行是一条房子出售的记录，原始特征有80列，具体的意思可以根据data_description来查询，我们要预测的是房子的售价，即“SalePrice”。训练集有1459条记录，测试集有1460条记录，数据量还是很小的。

# 相关性协方差表,corr()函数,返回结果接近0说明无相关性,大于0说明是正相关,小于0是负相关.
train_corr = train.drop('Id',axis=1).corr()
train_corr
MSSubClass LotFrontage LotArea OverallQual OverallCond YearBuilt YearRemodAdd MasVnrArea BsmtFinSF1 BsmtFinSF2 ... WoodDeckSF OpenPorchSF EnclosedPorch 3SsnPorch ScreenPorch PoolArea MiscVal MoSold YrSold SalePrice
MSSubClass 1.000000 -0.386347 -0.139781 0.032628 -0.059316 0.027850 0.040581 0.022936 -0.069836 -0.065649 ... -0.012579 -0.006100 -0.012037 -0.043825 -0.026030 0.008283 -0.007683 -0.013585 -0.021407 -0.084284
LotFrontage -0.386347 1.000000 0.426095 0.251646 -0.059213 0.123349 0.088866 0.193458 0.233633 0.049900 ... 0.088521 0.151972 0.010700 0.070029 0.041383 0.206167 0.003368 0.011200 0.007450 0.351799
LotArea -0.139781 0.426095 1.000000 0.105806 -0.005636 0.014228 0.013788 0.104160 0.214103 0.111170 ... 0.171698 0.084774 -0.018340 0.020423 0.043160 0.077672 0.038068 0.001205 -0.014261 0.263843
OverallQual 0.032628 0.251646 0.105806 1.000000 -0.091932 0.572323 0.550684 0.411876 0.239666 -0.059119 ... 0.238923 0.308819 -0.113937 0.030371 0.064886 0.065166 -0.031406 0.070815 -0.027347 0.790982
OverallCond -0.059316 -0.059213 -0.005636 -0.091932 1.000000 -0.375983 0.073741 -0.128101 -0.046231 0.040229 ... -0.003334 -0.032589 0.070356 0.025504 0.054811 -0.001985 0.068777 -0.003511 0.043950 -0.077856
YearBuilt 0.027850 0.123349 0.014228 0.572323 -0.375983 1.000000 0.592855 0.315707 0.249503 -0.049107 ... 0.224880 0.188686 -0.387268 0.031355 -0.050364 0.004950 -0.034383 0.012398 -0.013618 0.522897
YearRemodAdd 0.040581 0.088866 0.013788 0.550684 0.073741 0.592855 1.000000 0.179618 0.128451 -0.067759 ... 0.205726 0.226298 -0.193919 0.045286 -0.038740 0.005829 -0.010286 0.021490 0.035743 0.507101
MasVnrArea 0.022936 0.193458 0.104160 0.411876 -0.128101 0.315707 0.179618 1.000000 0.264736 -0.072319 ... 0.159718 0.125703 -0.110204 0.018796 0.061466 0.011723 -0.029815 -0.005965 -0.008201 0.477493
BsmtFinSF1 -0.069836 0.233633 0.214103 0.239666 -0.046231 0.249503 0.128451 0.264736 1.000000 -0.050117 ... 0.204306 0.111761 -0.102303 0.026451 0.062021 0.140491 0.003571 -0.015727 0.014359 0.386420
BsmtFinSF2 -0.065649 0.049900 0.111170 -0.059119 0.040229 -0.049107 -0.067759 -0.072319 -0.050117 1.000000 ... 0.067898 0.003093 0.036543 -0.029993 0.088871 0.041709 0.004940 -0.015211 0.031706 -0.011378
BsmtUnfSF -0.140759 0.132644 -0.002618 0.308159 -0.136841 0.149040 0.181133 0.114442 -0.495251 -0.209294 ... -0.005316 0.129005 -0.002538 0.020764 -0.012579 -0.035092 -0.023837 0.034888 -0.041258 0.214479
TotalBsmtSF -0.238518 0.392075 0.260833 0.537808 -0.171098 0.391452 0.291066 0.363936 0.522396 0.104810 ... 0.232019 0.247264 -0.095478 0.037384 0.084489 0.126053 -0.018479 0.013196 -0.014969 0.613581
1stFlrSF -0.251758 0.457181 0.299475 0.476224 -0.144203 0.281986 0.240379 0.344501 0.445863 0.097117 ... 0.235459 0.211671 -0.065292 0.056104 0.088758 0.131525 -0.021096 0.031372 -0.013604 0.605852
2ndFlrSF 0.307886 0.080177 0.050986 0.295493 0.028942 0.010308 0.140024 0.174561 -0.137079 -0.099260 ... 0.092165 0.208026 0.061989 -0.024358 0.040606 0.081487 0.016197 0.035164 -0.028700 0.319334
LowQualFinSF 0.046474 0.038469 0.004779 -0.030429 0.025494 -0.183784 -0.062419 -0.069071 -0.064503 0.014807 ... -0.025444 0.018251 0.061081 -0.004296 0.026799 0.062157 -0.003793 -0.022174 -0.028921 -0.025606
GrLivArea 0.074853 0.402797 0.263116 0.593007 -0.079686 0.199010 0.287389 0.390857 0.208171 -0.009640 ... 0.247433 0.330224 0.009113 0.020643 0.101510 0.170205 -0.002416 0.050240 -0.036526 0.708624
BsmtFullBath 0.003491 0.100949 0.158155 0.111098 -0.054942 0.187599 0.119470 0.085310 0.649212 0.158678 ... 0.175315 0.067341 -0.049911 -0.000106 0.023148 0.067616 -0.023047 -0.025361 0.067049 0.227122
BsmtHalfBath -0.002333 -0.007234 0.048046 -0.040150 0.117821 -0.038162 -0.012337 0.026673 0.067418 0.070948 ... 0.040161 -0.025324 -0.008555 0.035114 0.032121 0.020025 -0.007367 0.032873 -0.046524 -0.016844
FullBath 0.131608 0.198769 0.126031 0.550600 -0.194149 0.468271 0.439046 0.276833 0.058543 -0.076444 ... 0.187703 0.259977 -0.115093 0.035353 -0.008106 0.049604 -0.014290 0.055872 -0.019669 0.560664
HalfBath 0.177354 0.053532 0.014259 0.273458 -0.060769 0.242656 0.183331 0.201444 0.004262 -0.032148 ... 0.108080 0.199740 -0.095317 -0.004972 0.072426 0.022381 0.001290 -0.009050 -0.010269 0.284108
BedroomAbvGr -0.023438 0.263170 0.119690 0.101676 0.012980 -0.070651 -0.040581 0.102821 -0.107355 -0.015728 ... 0.046854 0.093810 0.041570 -0.024478 0.044300 0.070703 0.007767 0.046544 -0.036014 0.168213
KitchenAbvGr 0.281721 -0.006069 -0.017784 -0.183882 -0.087001 -0.174800 -0.149598 -0.037610 -0.081007 -0.040751 ... -0.090130 -0.070091 0.037312 -0.024600 -0.051613 -0.014525 0.062341 0.026589 0.031687 -0.135907
TotRmsAbvGrd 0.040380 0.352096 0.190015 0.427452 -0.057583 0.095589 0.191740 0.280682 0.044316 -0.035227 ... 0.165984 0.234192 0.004151 -0.006683 0.059383 0.083757 0.024763 0.036907 -0.034516 0.533723
Fireplaces -0.045569 0.266639 0.271364 0.396765 -0.023820 0.147716 0.112581 0.249070 0.260011 0.046921 ... 0.200019 0.169405 -0.024822 0.011257 0.184530 0.095074 0.001409 0.046357 -0.024096 0.466929
GarageYrBlt 0.085072 0.070250 -0.024947 0.547766 -0.324297 0.825667 0.642277 0.252691 0.153484 -0.088011 ... 0.224577 0.228425 -0.297003 0.023544 -0.075418 -0.014501 -0.032417 0.005337 -0.001014 0.486362
GarageCars -0.040110 0.285691 0.154871 0.600671 -0.185758 0.537850 0.420622 0.364204 0.224054 -0.038264 ... 0.226342 0.213569 -0.151434 0.035765 0.050494 0.020934 -0.043080 0.040522 -0.039117 0.640409
GarageArea -0.098672 0.344997 0.180403 0.562022 -0.151521 0.478954 0.371600 0.373066 0.296970 -0.018227 ... 0.224666 0.241435 -0.121777 0.035087 0.051412 0.061047 -0.027400 0.027974 -0.027378 0.623431
WoodDeckSF -0.012579 0.088521 0.171698 0.238923 -0.003334 0.224880 0.205726 0.159718 0.204306 0.067898 ... 1.000000 0.058661 -0.125989 -0.032771 -0.074181 0.073378 -0.009551 0.021011 0.022270 0.324413
OpenPorchSF -0.006100 0.151972 0.084774 0.308819 -0.032589 0.188686 0.226298 0.125703 0.111761 0.003093 ... 0.058661 1.000000 -0.093079 -0.005842 0.074304 0.060762 -0.018584 0.071255 -0.057619 0.315856
EnclosedPorch -0.012037 0.010700 -0.018340 -0.113937 0.070356 -0.387268 -0.193919 -0.110204 -0.102303 0.036543 ... -0.125989 -0.093079 1.000000 -0.037305 -0.082864 0.054203 0.018361 -0.028887 -0.009916 -0.128578
3SsnPorch -0.043825 0.070029 0.020423 0.030371 0.025504 0.031355 0.045286 0.018796 0.026451 -0.029993 ... -0.032771 -0.005842 -0.037305 1.000000 -0.031436 -0.007992 0.000354 0.029474 0.018645 0.044584
ScreenPorch -0.026030 0.041383 0.043160 0.064886 0.054811 -0.050364 -0.038740 0.061466 0.062021 0.088871 ... -0.074181 0.074304 -0.082864 -0.031436 1.000000 0.051307 0.031946 0.023217 0.010694 0.111447
PoolArea 0.008283 0.206167 0.077672 0.065166 -0.001985 0.004950 0.005829 0.011723 0.140491 0.041709 ... 0.073378 0.060762 0.054203 -0.007992 0.051307 1.000000 0.029669 -0.033737 -0.059689 0.092404
MiscVal -0.007683 0.003368 0.038068 -0.031406 0.068777 -0.034383 -0.010286 -0.029815 0.003571 0.004940 ... -0.009551 -0.018584 0.018361 0.000354 0.031946 0.029669 1.000000 -0.006495 0.004906 -0.021190
MoSold -0.013585 0.011200 0.001205 0.070815 -0.003511 0.012398 0.021490 -0.005965 -0.015727 -0.015211 ... 0.021011 0.071255 -0.028887 0.029474 0.023217 -0.033737 -0.006495 1.000000 -0.145721 0.046432
YrSold -0.021407 0.007450 -0.014261 -0.027347 0.043950 -0.013618 0.035743 -0.008201 0.014359 0.031706 ... 0.022270 -0.057619 -0.009916 0.018645 0.010694 -0.059689 0.004906 -0.145721 1.000000 -0.028923
SalePrice -0.084284 0.351799 0.263843 0.790982 -0.077856 0.522897 0.507101 0.477493 0.386420 -0.011378 ... 0.324413 0.315856 -0.128578 0.044584 0.111447 0.092404 -0.021190 0.046432 -0.028923 1.000000

37 rows × 37 columns

所有特征相关度分析

# 画出相关性热力图
a = plt.subplots(figsize=(20, 12))#调整画布大小
a = sns.heatmap(train_corr, vmax=.8, square=True)#画热力图   annot=True 显示系数

png

SalePrice 相关度特征排序

# 寻找K个最相关的特征信息
k = 10 # number of variables for heatmap
cols = train_corr.nlargest(k, 'SalePrice')['SalePrice'].index
cm = np.corrcoef(train[cols].values.T)
sns.set(font_scale=1.5)
hm = plt.subplots(figsize=(20, 12))#调整画布大小
hm = sns.heatmap(cm, cbar=True, annot=True, square=True, fmt='.2f', annot_kws={'size': 10}, yticklabels=cols.values, xticklabels=cols.values)
plt.show()

'''
1. GarageCars 和 GarageAre 相关性很高、就像双胞胎一样，所以我们只需要其中的一个变量，例如：GarageCars。
2. TotalBsmtSF  和 1stFloor 与上述情况相同，我们选择 TotalBsmtS
3. GarageAre 和 TotRmsAbvGrd 与上述情况相同，我们选择 GarageAre
'''

png

'\n1. GarageCars 和 GarageAre 相关性很高、就像双胞胎一样，所以我们只需要其中的一个变量，例如：GarageCars。\n2. TotalBsmtSF  和 1stFloor 与上述情况相同，我们选择 TotalBsmtS\n3. GarageAre 和 TotRmsAbvGrd 与上述情况相同，我们选择 GarageAre\n'

SalePrice 和相关变量之间的散点图

sns.set()
cols = ['SalePrice', 'OverallQual', 'GrLivArea','GarageCars', 'TotalBsmtSF', 'FullBath', 'YearBuilt']
sns.pairplot(train[cols], size = 2.5)
plt.show();

png

train[['SalePrice', 'OverallQual', 'GrLivArea','GarageCars', 'TotalBsmtSF', 'FullBath', 'YearBuilt']].info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1460 entries, 0 to 1459
Data columns (total 7 columns):
SalePrice      1460 non-null int64
OverallQual    1460 non-null int64
GrLivArea      1460 non-null int64
GarageCars     1460 non-null int64
TotalBsmtSF    1460 non-null int64
FullBath       1460 non-null int64
YearBuilt      1460 non-null int64
dtypes: int64(7)
memory usage: 79.9 KB

二. 特征工程

test['SalePrice'] = None
train_test = pd.concat((train, test)).reset_index(drop=True)

1. 缺失值分析

  1. 根据业务,常识,以及第二步的数据分析构造特征工程.
  2. 将特征转换为模型可以辨别的类型(如处理缺失值,处理文本进行等)
total= train_test.isnull().sum().sort_values(ascending=False)
percent = (train_test.isnull().sum()/train_test.isnull().count()).sort_values(ascending=False)
missing_data = pd.concat([total, percent], axis=1, keys=['Total','Lost Percent'])

print(missing_data[missing_data.isnull().values==False].sort_values('Total', axis=0, ascending=False).head(20))


'''
1. 对于缺失率过高的特征，例如 超过15% 我们应该删掉相关变量且假设该变量并不存在
2. GarageX 变量群的缺失数据量和概率都相同，可以选择一个就行，例如：GarageCars
3. 对于缺失数据在5%左右（缺失率低），可以直接删除/回归预测
'''

'\n1. 对于缺失率过高的特征，例如 超过15% 我们应该删掉相关变量且假设该变量并不存在\n2. GarageX 变量群的缺失数据量和概率都相同，可以选择一个就行，例如：GarageCars\n3. 对于缺失数据在5%左右（缺失率低），可以直接删除/回归预测\n'

train_test = train_test.drop((missing_data[missing_data['Total'] > 1]).index.drop('SalePrice') , axis=1)
# train_test = train_test.drop(train.loc[train['Electrical'].isnull()].index)

tmp = train_test[train_test['SalePrice'].isnull().values==False]
print(tmp.isnull().sum().max()) # justchecking that there's no missing data missing

1

2. 异常值处理

单因素分析

这里的关键在于如何建立阈值，定义一个观察值为异常值。我们对数据进行正态化，意味着把数据值转换成均值为 0，方差为 1 的数据

fig = plt.figure(figsize=(12, 6))
ax1 = fig.add_subplot(1, 2, 1)
ax2 = fig.add_subplot(1, 2, 2)
ax1.hist(train.SalePrice)
ax2.hist(np.log1p(train.SalePrice))

'''
从直方图中可以看出：

* 偏离正态分布
* 数据正偏
* 有峰值
'''
# 数据偏度和峰度度量：

print("Skewness: %f" % train['SalePrice'].skew())
print("Kurtosis: %f" % train['SalePrice'].kurt())

'''
低范围的值都比较相似并且在 0 附近分布。
高范围的值离 0 很远，并且七点几的值远在正常范围之外。
'''

'\n低范围的值都比较相似并且在 0 附近分布。\n高范围的值离 0 很远，并且七点几的值远在正常范围之外。\n'

png

双变量分析

1.GrLivArea 和 SalePrice 双变量分析

var = 'GrLivArea'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
data.plot.scatter(x=var, y='SalePrice', ylim=(0,800000));

'''
从图中可以看出：

1. 有两个离群的 GrLivArea 值很高的数据，我们可以推测出现这种情况的原因。
    或许他们代表了农业地区，也就解释了低价。 这两个点很明显不能代表典型样例，所以我们将它们定义为异常值并删除。
2. 图中顶部的两个点是七点几的观测值，他们虽然看起来像特殊情况，但是他们依然符合整体趋势，所以我们将其保留下来。
'''

'\n从图中可以看出：\n\n1. 有两个离群的 GrLivArea 值很高的数据，我们可以推测出现这种情况的原因。\n    或许他们代表了农业地区，也就解释了低价。 这两个点很明显不能代表典型样例，所以我们将它们定义为异常值并删除。\n2. 图中顶部的两个点是七点几的观测值，他们虽然看起来像特殊情况，但是他们依然符合整体趋势，所以我们将其保留下来。\n'

png

# 删除点
print(train.sort_values(by='GrLivArea', ascending = False)[:2])
tmp = train_test[train_test['SalePrice'].isnull().values==False]

train_test = train_test.drop(tmp[tmp['Id'] == 1299].index)
train_test = train_test.drop(tmp[tmp['Id'] == 524].index)

2.TotalBsmtSF 和 SalePrice 双变量分析

var = 'TotalBsmtSF'
data = pd.concat([train['SalePrice'],train[var]], axis=1)
data.plot.scatter(x=var, y='SalePrice',ylim=(0,800000))

png

核心部分

“房价” 到底是谁？

这个问题的答案，需要我们验证根据数据基础进行多元分析的假设。

我们已经进行了数据清洗，并且发现了 SalePrice 的很多信息，现在我们要更进一步理解 SalePrice 如何遵循统计假设，可以让我们应用多元技术。

应该测量 4 个假设量：

  • 正态性
  • 同方差性
  • 线性
  • 相关错误缺失

正态性：

应主要关注以下两点：直方图 – 峰度和偏度。

正态概率图 – 数据分布应紧密跟随代表正态分布的对角线。

  1. SalePrice 绘制直方图和正态概率图：
sns.distplot(train['SalePrice'], fit=norm)
fig = plt.figure()
res = stats.probplot(train['SalePrice'], plot=plt)

'''
可以看出，房价分布不是正态的，显示了峰值，正偏度，但是并不跟随对角线。
可以用对数变换来解决这个问题
'''

'\n可以看出，房价分布不是正态的，显示了峰值，正偏度，但是并不跟随对角线。\n可以用对数变换来解决这个问题\n'

png

png

# 进行对数变换：
# 进行对数变换：
train_test['SalePrice'] = [i if i is None else np.log1p(i) for i in train_test['SalePrice']]

# 绘制变换后的直方图和正态概率图：
tmp = train_test[train_test['SalePrice'].isnull().values==False]

sns.distplot(tmp[tmp['SalePrice'] !=0]['SalePrice'], fit=norm);
fig = plt.figure()
res = stats.probplot(tmp['SalePrice'], plot=plt)

png

png

2. GrLivArea

绘制直方图和正态概率曲线图：

sns.distplot(train['GrLivArea'], fit=norm);
fig = plt.figure()
res = stats.probplot(train['GrLivArea'], plot=plt)

png

png

# 进行对数变换：
train_test['GrLivArea'] = [i if i is None else np.log1p(i) for i in train_test['GrLivArea']]

# 绘制变换后的直方图和正态概率图：
tmp = train_test[train_test['SalePrice'].isnull().values==False]
sns.distplot(tmp['GrLivArea'], fit=norm)
fig = plt.figure()
res = stats.probplot(tmp['GrLivArea'], plot=plt)

png

png

3.TotalBsmtSF

绘制直方图和正态概率曲线图：

sns.distplot(train['TotalBsmtSF'],fit=norm);
fig = plt.figure()
res = stats.probplot(train['TotalBsmtSF'],plot=plt)

'''
从图中可以看出：
* 显示出了偏度
* 大量为 0(Y值) 的观察值（没有地下室的房屋）
* 含 0(Y值) 的数据无法进行对数变换
'''

'\n从图中可以看出：\n* 显示出了偏度\n* 大量为 0(Y值) 的观察值（没有地下室的房屋）\n* 含 0(Y值) 的数据无法进行对数变换\n'

png

png

# 去掉为0的分布情况
tmp = train_test[train_test['SalePrice'].isnull().values==False]

tmp = np.array(tmp.loc[tmp['TotalBsmtSF']>0, ['TotalBsmtSF']])[:, 0]
sns.distplot(tmp, fit=norm)
fig = plt.figure()
res = stats.probplot(tmp, plot=plt)

png

png

# 我们建立了一个变量，可以得到有没有地下室的影响值（二值变量），我们选择忽略零值，只对非零值进行对数变换。
# 这样我们既可以变换数据，也不会损失有没有地下室的影响。

print(train.loc[train['TotalBsmtSF']==0, ['TotalBsmtSF']].count())
train.loc[train['TotalBsmtSF']==0,'TotalBsmtSF'] = 1
print(train.loc[train['TotalBsmtSF']==1, ['TotalBsmtSF']].count())

TotalBsmtSF    37
dtype: int64
TotalBsmtSF    37
dtype: int64

# 进行对数变换：
tmp = train_test[train_test['SalePrice'].isnull().values==False]

print(tmp['TotalBsmtSF'].head(10))
train_test['TotalBsmtSF']= np.log1p(train_test['TotalBsmtSF'])

tmp = train_test[train_test['SalePrice'].isnull().values==False]
print(tmp['TotalBsmtSF'].head(10))

0     856.0
1    1262.0
2     920.0
3     756.0
4    1145.0
5     796.0
6    1686.0
7    1107.0
8     952.0
9     991.0
Name: TotalBsmtSF, dtype: float64
0    6.753438
1    7.141245
2    6.825460
3    6.629363
4    7.044033
5    6.680855
6    7.430707
7    7.010312
8    6.859615
9    6.899723
Name: TotalBsmtSF, dtype: float64

# 绘制变换后的直方图和正态概率图：
tmp = train_test[train_test['SalePrice'].isnull().values==False]

tmp = np.array(tmp.loc[tmp['TotalBsmtSF']>0, ['TotalBsmtSF']])[:, 0]
sns.distplot(tmp, fit=norm)
fig = plt.figure()
res = stats.probplot(tmp, plot=plt)

png

png

同方差性：

最好的测量两个变量的同方差性的方法就是图像。

  1. SalePrice 和 GrLivArea 同方差性

绘制散点图：

tmp = train_test[train_test['SalePrice'].isnull().values==False]

plt.scatter(tmp['GrLivArea'], tmp['SalePrice'])

<matplotlib.collections.PathCollection at 0x11a366f60>

png

  1. SalePrice with TotalBsmtSF 同方差性

绘制散点图：

tmp = train_test[train_test['SalePrice'].isnull().values==False]

plt.scatter(tmp[tmp['TotalBsmtSF']>0]['TotalBsmtSF'], tmp[tmp['TotalBsmtSF']>0]['SalePrice'])

# 可以看出 SalePrice 在整个 TotalBsmtSF 变量范围内显示出了同等级别的变化。

<matplotlib.collections.PathCollection at 0x11d7d96d8>

png

三. 模型选择

1.数据标准化

tmp = train_test[train_test['SalePrice'].isnull().values==False]
tmp_1 = train_test[train_test['SalePrice'].isnull().values==True]

x_train = tmp[['OverallQual', 'GrLivArea','GarageCars', 'TotalBsmtSF', 'FullBath', 'YearBuilt']]
y_train = tmp[["SalePrice"]].values.ravel()
x_test = tmp_1[['OverallQual', 'GrLivArea','GarageCars', 'TotalBsmtSF', 'FullBath', 'YearBuilt']]

# 简单测试，用中位数来替代
# print(x_test.GarageCars.mean(), x_test.GarageCars.median(), x_test.TotalBsmtSF.mean(), x_test.TotalBsmtSF.median())

x_test["GarageCars"].fillna(x_test.GarageCars.median(), inplace=True)
x_test["TotalBsmtSF"].fillna(x_test.TotalBsmtSF.median(), inplace=True)

2.开始建模

  1. 可选单个模型模型有 线性回归（Ridge、Lasso）、树回归、GBDT、XGBoost、LightGBM 等.
  2. 也可以将多个模型组合起来,进行模型融合,比如voting,stacking等方法
  3. 好的特征决定模型上限,好的模型和参数可以无线逼近上限.
  4. 我测试了多种模型,模型结果最高的随机森林,最高有0.8.

bagging:

单个分类器的效果真的是很有限。 我们会倾向于把N多的分类器合在一起，做一个“综合分类器”以达到最好的效果。 我们从刚刚的试验中得知，Ridge(alpha=15)给了我们最好的结果。

from sklearn.linear_model import Ridge
from sklearn.model_selection import cross_val_score
from sklearn.ensemble import BaggingRegressor, RandomForestRegressor

ridge = Ridge(alpha=0.1)

# bagging 把很多小的分类器放在一起，每个train随机的一部分数据，然后把它们的最终结果综合起来（多数投票）
# bagging 算是一种算法框架
params = [1, 10, 20, 40, 60]
test_scores = []
for param in params:
    clf = BaggingRegressor(base_estimator=ridge, n_estimators=param)
    # cv=5表示cross_val_score采用的是k-fold cross validation的方法，重复5次交叉验证
    # scoring='precision'、scoring='recall'、scoring='f1', scoring='neg_mean_squared_error' 方差值
    test_score = np.sqrt(-cross_val_score(clf, x_train, y_train, cv=10, scoring='neg_mean_squared_error'))
    test_scores.append(np.mean(test_score))

print(test_score.mean())
plt.plot(params, test_scores)
plt.title('n_estimators vs CV Error')
plt.show()

png

# 模型选择
## LASSO Regression :
lasso = make_pipeline(RobustScaler(), Lasso(alpha=0.0005, random_state=1))
* Elastic Net Regression
ENet = make_pipeline(
    RobustScaler(), ElasticNet(
        alpha=0.0005, l1_ratio=.9, random_state=3))
Kernel Ridge Regression
KRR = KernelRidge(alpha=0.6, kernel='polynomial', degree=2, coef0=2.5)
## Gradient Boosting Regression
GBoost = GradientBoostingRegressor(
    n_estimators=3000,
    learning_rate=0.05,
    max_depth=4,
    max_features='sqrt',
    min_samples_leaf=15,
    min_samples_split=10,
    loss='huber',
    random_state=5)
## XGboost
model_xgb = xgb.XGBRegressor(
    colsample_bytree=0.4603,
    gamma=0.0468,
    learning_rate=0.05,
    max_depth=3,
    min_child_weight=1.7817,
    n_estimators=2200,
    reg_alpha=0.4640,
    reg_lambda=0.8571,
    subsample=0.5213,
    silent=1,
    random_state=7,
    nthread=-1)
## lightGBM
model_lgb = lgb.LGBMRegressor(
    objective='regression',
    num_leaves=5,
    learning_rate=0.05,
    n_estimators=720,
    max_bin=55,
    bagging_fraction=0.8,
    bagging_freq=5,
    feature_fraction=0.2319,
    feature_fraction_seed=9,
    bagging_seed=9,
    min_data_in_leaf=6,
    min_sum_hessian_in_leaf=11)
## 对这些基本模型进行打分
score = rmsle_cv(lasso)
print("\nLasso score: {:.4f} ({:.4f})\n".format(score.mean(), score.std()))
score = rmsle_cv(ENet)
print("ElasticNet score: {:.4f} ({:.4f})\n".format(score.mean(), score.std()))
score = rmsle_cv(KRR)
print(
    "Kernel Ridge score: {:.4f} ({:.4f})\n".format(score.mean(), score.std()))
score = rmsle_cv(GBoost)
print("Gradient Boosting score: {:.4f} ({:.4f})\n".format(score.mean(),
                                                          score.std()))
score = rmsle_cv(model_xgb)
print("Xgboost score: {:.4f} ({:.4f})\n".format(score.mean(), score.std()))
score = rmsle_cv(model_lgb)
print("LGBM score: {:.4f} ({:.4f})\n".format(score.mean(), score.std()))

from sklearn.linear_model import Ridge
from sklearn.model_selection import learning_curve

ridge = Ridge(alpha=0.1)

train_sizes, train_loss, test_loss = learning_curve(ridge, x_train, y_train, cv=10, 
                                                    scoring='neg_mean_squared_error',
                                                    train_sizes = [0.1, 0.3, 0.5, 0.7, 0.9 , 0.95, 1])

# 训练误差均值
train_loss_mean = -np.mean(train_loss, axis = 1)
# 测试误差均值
test_loss_mean = -np.mean(test_loss, axis = 1)

# 绘制误差曲线
plt.plot(train_sizes/len(x_train), train_loss_mean, 'o-', color = 'r', label = 'Training')
plt.plot(train_sizes/len(x_train), test_loss_mean, 'o-', color = 'g', label = 'Cross-Validation')

plt.xlabel('Training data size')
plt.ylabel('Loss')
plt.legend(loc = 'best')
plt.show()

png

mode_br = BaggingRegressor(base_estimator=ridge, n_estimators=10)
mode_br.fit(x_train, y_train)
y_test = np.expm1(mode_br.predict(x_test))

四 建立模型

模型融合 voting

# 模型融合
class AveragingModels(BaseEstimator, RegressorMixin, TransformerMixin):
    def __init__(self, models):
        self.models = models

    # we define clones of the original models to fit the data in
    def fit(self, X, y):
        self.models_ = [clone(x) for x in self.models]

        # Train cloned base models
        for model in self.models_:
            model.fit(X, y)

        return self

    # Now we do the predictions for cloned models and average them
    def predict(self, X):
        predictions = np.column_stack(
            [model.predict(X) for model in self.models_])
        return np.mean(predictions, axis=1)


# 评价这四个模型的好坏
averaged_models = AveragingModels(models=(ENet, GBoost, KRR, lasso))
score = rmsle_cv(averaged_models)
print(" Averaged base models score: {:.4f} ({:.4f})\n".format(score.mean(),
                                                              score.std()))

# 最终对模型的训练和预测
# StackedRegressor
stacked_averaged_models.fit(train.values, y_train)
stacked_train_pred = stacked_averaged_models.predict(train.values)
stacked_pred = np.expm1(stacked_averaged_models.predict(test.values))
print(rmsle(y_train, stacked_train_pred))

# XGBoost
model_xgb.fit(train, y_train)
xgb_train_pred = model_xgb.predict(train)
xgb_pred = np.expm1(model_xgb.predict(test))
print(rmsle(y_train, xgb_train_pred))
# lightGBM
model_lgb.fit(train, y_train)
lgb_train_pred = model_lgb.predict(train)
lgb_pred = np.expm1(model_lgb.predict(test.values))
print(rmsle(y_train, lgb_train_pred))
'''RMSE on the entire Train data when averaging'''

print('RMSLE score on train data:')
print(rmsle(y_train, stacked_train_pred * 0.70 + xgb_train_pred * 0.15 +
            lgb_train_pred * 0.15))
# 模型融合的预测效果
ensemble = stacked_pred * 0.70 + xgb_pred * 0.15 + lgb_pred * 0.15
# 保存结果
result = pd.DataFrame()
result['Id'] = test_ID
result['SalePrice'] = ensemble
# index=False 是用来除去行编号
result.to_csv('/Users/liudong/Desktop/house_price/result.csv', index=False)
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