House Prices Advanced Regression Techniques

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Kaggle Competition 的练习

房价预测

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# 数据分析库
import pandas as pd
import numpy as np
import random

# 数据可视化
import seaborn as sns
import matplotlib.pyplot as plt

# 机器学习库
from sklearn.preprocessing import OneHotEncoder
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC, LinearSVC
from sklearn.ensemble import RandomForestClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.linear_model import Perceptron
from sklearn.linear_model import SGDClassifier
from sklearn.tree import DecisionTreeClassifier

pd.options.display.max_rows = 10  # 最大显示行数
pd.options.display.float_format = '{:.5f}'.format  # 精确度 保留一位小数
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train_df = pd.read_csv('/train.csv')
test_df = pd.read_csv('/test.csv')
train_df.shape, test_df.shape
((1460, 81), (1459, 80))

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train_df.head()
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 ... 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
0 1 60 RL 65.00000 8450 Pave NaN Reg Lvl AllPub Inside Gtl CollgCr Norm Norm 1Fam 2Story 7 5 2003 2003 Gable CompShg VinylSd VinylSd BrkFace 196.00000 Gd TA PConc Gd TA No GLQ 706 Unf 0 150 856 GasA ... Y SBrkr 856 854 0 1710 1 0 2 1 3 1 Gd 8 Typ 0 NaN Attchd 2003.00000 RFn 2 548 TA TA Y 0 61 0 0 0 0 NaN NaN NaN 0 2 2008 WD Normal 208500
1 2 20 RL 80.00000 9600 Pave NaN Reg Lvl AllPub FR2 Gtl Veenker Feedr Norm 1Fam 1Story 6 8 1976 1976 Gable CompShg MetalSd MetalSd None 0.00000 TA TA CBlock Gd TA Gd ALQ 978 Unf 0 284 1262 GasA ... Y SBrkr 1262 0 0 1262 0 1 2 0 3 1 TA 6 Typ 1 TA Attchd 1976.00000 RFn 2 460 TA TA Y 298 0 0 0 0 0 NaN NaN NaN 0 5 2007 WD Normal 181500
2 3 60 RL 68.00000 11250 Pave NaN IR1 Lvl AllPub Inside Gtl CollgCr Norm Norm 1Fam 2Story 7 5 2001 2002 Gable CompShg VinylSd VinylSd BrkFace 162.00000 Gd TA PConc Gd TA Mn GLQ 486 Unf 0 434 920 GasA ... Y SBrkr 920 866 0 1786 1 0 2 1 3 1 Gd 6 Typ 1 TA Attchd 2001.00000 RFn 2 608 TA TA Y 0 42 0 0 0 0 NaN NaN NaN 0 9 2008 WD Normal 223500
3 4 70 RL 60.00000 9550 Pave NaN IR1 Lvl AllPub Corner Gtl Crawfor Norm Norm 1Fam 2Story 7 5 1915 1970 Gable CompShg Wd Sdng Wd Shng None 0.00000 TA TA BrkTil TA Gd No ALQ 216 Unf 0 540 756 GasA ... Y SBrkr 961 756 0 1717 1 0 1 0 3 1 Gd 7 Typ 1 Gd Detchd 1998.00000 Unf 3 642 TA TA Y 0 35 272 0 0 0 NaN NaN NaN 0 2 2006 WD Abnorml 140000
4 5 60 RL 84.00000 14260 Pave NaN IR1 Lvl AllPub FR2 Gtl NoRidge Norm Norm 1Fam 2Story 8 5 2000 2000 Gable CompShg VinylSd VinylSd BrkFace 350.00000 Gd TA PConc Gd TA Av GLQ 655 Unf 0 490 1145 GasA ... Y SBrkr 1145 1053 0 2198 1 0 2 1 4 1 Gd 9 Typ 1 TA Attchd 2000.00000 RFn 3 836 TA TA Y 192 84 0 0 0 0 NaN NaN NaN 0 12 2008 WD Normal 250000

5 rows × 81 columns

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# test_df.head()
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train_df.describe()
Id MSSubClass LotFrontage LotArea OverallQual OverallCond YearBuilt YearRemodAdd MasVnrArea BsmtFinSF1 BsmtFinSF2 BsmtUnfSF TotalBsmtSF 1stFlrSF 2ndFlrSF LowQualFinSF GrLivArea BsmtFullBath BsmtHalfBath FullBath HalfBath BedroomAbvGr KitchenAbvGr TotRmsAbvGrd Fireplaces GarageYrBlt GarageCars GarageArea WoodDeckSF OpenPorchSF EnclosedPorch 3SsnPorch ScreenPorch PoolArea MiscVal MoSold YrSold SalePrice
count 1460.00000 1460.00000 1201.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1452.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1379.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000
mean 730.50000 56.89726 70.04996 10516.82808 6.09932 5.57534 1971.26781 1984.86575 103.68526 443.63973 46.54932 567.24041 1057.42945 1162.62671 346.99247 5.84452 1515.46370 0.42534 0.05753 1.56507 0.38288 2.86644 1.04658 6.51781 0.61301 1978.50616 1.76712 472.98014 94.24452 46.66027 21.95411 3.40959 15.06096 2.75890 43.48904 6.32192 2007.81575 180921.19589
std 421.61001 42.30057 24.28475 9981.26493 1.38300 1.11280 30.20290 20.64541 181.06621 456.09809 161.31927 441.86696 438.70532 386.58774 436.52844 48.62308 525.48038 0.51891 0.23875 0.55092 0.50289 0.81578 0.22034 1.62539 0.64467 24.68972 0.74732 213.80484 125.33879 66.25603 61.11915 29.31733 55.75742 40.17731 496.12302 2.70363 1.32810 79442.50288
min 1.00000 20.00000 21.00000 1300.00000 1.00000 1.00000 1872.00000 1950.00000 0.00000 0.00000 0.00000 0.00000 0.00000 334.00000 0.00000 0.00000 334.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 2.00000 0.00000 1900.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 1.00000 2006.00000 34900.00000
25% 365.75000 20.00000 59.00000 7553.50000 5.00000 5.00000 1954.00000 1967.00000 0.00000 0.00000 0.00000 223.00000 795.75000 882.00000 0.00000 0.00000 1129.50000 0.00000 0.00000 1.00000 0.00000 2.00000 1.00000 5.00000 0.00000 1961.00000 1.00000 334.50000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 5.00000 2007.00000 129975.00000
50% 730.50000 50.00000 69.00000 9478.50000 6.00000 5.00000 1973.00000 1994.00000 0.00000 383.50000 0.00000 477.50000 991.50000 1087.00000 0.00000 0.00000 1464.00000 0.00000 0.00000 2.00000 0.00000 3.00000 1.00000 6.00000 1.00000 1980.00000 2.00000 480.00000 0.00000 25.00000 0.00000 0.00000 0.00000 0.00000 0.00000 6.00000 2008.00000 163000.00000
75% 1095.25000 70.00000 80.00000 11601.50000 7.00000 6.00000 2000.00000 2004.00000 166.00000 712.25000 0.00000 808.00000 1298.25000 1391.25000 728.00000 0.00000 1776.75000 1.00000 0.00000 2.00000 1.00000 3.00000 1.00000 7.00000 1.00000 2002.00000 2.00000 576.00000 168.00000 68.00000 0.00000 0.00000 0.00000 0.00000 0.00000 8.00000 2009.00000 214000.00000
max 1460.00000 190.00000 313.00000 215245.00000 10.00000 9.00000 2010.00000 2010.00000 1600.00000 5644.00000 1474.00000 2336.00000 6110.00000 4692.00000 2065.00000 572.00000 5642.00000 3.00000 2.00000 3.00000 2.00000 8.00000 3.00000 14.00000 3.00000 2010.00000 4.00000 1418.00000 857.00000 547.00000 552.00000 508.00000 480.00000 738.00000 15500.00000 12.00000 2010.00000 755000.00000 
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# test_df.describe()
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train_df.info()
print('_' * 50)
test_df.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
__________________________________________________
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1459 entries, 0 to 1458
Data columns (total 80 columns):
Id               1459 non-null int64
MSSubClass       1459 non-null int64
MSZoning         1455 non-null object
LotFrontage      1232 non-null float64
LotArea          1459 non-null int64
Street           1459 non-null object
Alley            107 non-null object
LotShape         1459 non-null object
LandContour      1459 non-null object
Utilities        1457 non-null object
LotConfig        1459 non-null object
LandSlope        1459 non-null object
Neighborhood     1459 non-null object
Condition1       1459 non-null object
Condition2       1459 non-null object
BldgType         1459 non-null object
HouseStyle       1459 non-null object
OverallQual      1459 non-null int64
OverallCond      1459 non-null int64
YearBuilt        1459 non-null int64
YearRemodAdd     1459 non-null int64
RoofStyle        1459 non-null object
RoofMatl         1459 non-null object
Exterior1st      1458 non-null object
Exterior2nd      1458 non-null object
MasVnrType       1443 non-null object
MasVnrArea       1444 non-null float64
ExterQual        1459 non-null object
ExterCond        1459 non-null object
Foundation       1459 non-null object
BsmtQual         1415 non-null object
BsmtCond         1414 non-null object
BsmtExposure     1415 non-null object
BsmtFinType1     1417 non-null object
BsmtFinSF1       1458 non-null float64
BsmtFinType2     1417 non-null object
BsmtFinSF2       1458 non-null float64
BsmtUnfSF        1458 non-null float64
TotalBsmtSF      1458 non-null float64
Heating          1459 non-null object
HeatingQC        1459 non-null object
CentralAir       1459 non-null object
Electrical       1459 non-null object
1stFlrSF         1459 non-null int64
2ndFlrSF         1459 non-null int64
LowQualFinSF     1459 non-null int64
GrLivArea        1459 non-null int64
BsmtFullBath     1457 non-null float64
BsmtHalfBath     1457 non-null float64
FullBath         1459 non-null int64
HalfBath         1459 non-null int64
BedroomAbvGr     1459 non-null int64
KitchenAbvGr     1459 non-null int64
KitchenQual      1458 non-null object
TotRmsAbvGrd     1459 non-null int64
Functional       1457 non-null object
Fireplaces       1459 non-null int64
FireplaceQu      729 non-null object
GarageType       1383 non-null object
GarageYrBlt      1381 non-null float64
GarageFinish     1381 non-null object
GarageCars       1458 non-null float64
GarageArea       1458 non-null float64
GarageQual       1381 non-null object
GarageCond       1381 non-null object
PavedDrive       1459 non-null object
WoodDeckSF       1459 non-null int64
OpenPorchSF      1459 non-null int64
EnclosedPorch    1459 non-null int64
3SsnPorch        1459 non-null int64
ScreenPorch      1459 non-null int64
PoolArea         1459 non-null int64
PoolQC           3 non-null object
Fence            290 non-null object
MiscFeature      51 non-null object
MiscVal          1459 non-null int64
MoSold           1459 non-null int64
YrSold           1459 non-null int64
SaleType         1458 non-null object
SaleCondition    1459 non-null object
dtypes: float64(11), int64(26), object(43)
memory usage: 912.0+ KB

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train_df.describe(include="O")
MSZoning Street Alley LotShape LandContour Utilities LotConfig LandSlope Neighborhood Condition1 Condition2 BldgType HouseStyle RoofStyle RoofMatl Exterior1st Exterior2nd MasVnrType ExterQual ExterCond Foundation BsmtQual BsmtCond BsmtExposure BsmtFinType1 BsmtFinType2 Heating HeatingQC CentralAir Electrical KitchenQual Functional FireplaceQu GarageType GarageFinish GarageQual GarageCond PavedDrive PoolQC Fence MiscFeature SaleType SaleCondition
count 1460 1460 91 1460 1460 1460 1460 1460 1460 1460 1460 1460 1460 1460 1460 1460 1460 1452 1460 1460 1460 1423 1423 1422 1423 1422 1460 1460 1460 1459 1460 1460 770 1379 1379 1379 1379 1460 7 281 54 1460 1460
unique 5 2 2 4 4 2 5 3 25 9 8 5 8 6 8 15 16 4 4 5 6 4 4 4 6 6 6 5 2 5 4 7 5 6 3 5 5 3 3 4 4 9 6
top RL Pave Grvl Reg Lvl AllPub Inside Gtl NAmes Norm Norm 1Fam 1Story Gable CompShg VinylSd VinylSd None TA TA PConc TA TA No Unf Unf GasA Ex Y SBrkr TA Typ Gd Attchd Unf TA TA Y Gd MnPrv Shed WD Normal
freq 1151 1454 50 925 1311 1459 1052 1382 225 1260 1445 1220 726 1141 1434 515 504 864 906 1282 647 649 1311 953 430 1256 1428 741 1365 1334 735 1360 380 870 605 1311 1326 1340 3 157 49 1267 1198 
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分析概要

Feature Status Dispose
Alley 缺失比较多 删除
PoolQC 只有七家有游泳池并且和 PoolArea 相关 先不填充 删除
Fence 栏杆质量只有20%的有 缺失的填充为没有
MiscFeature 其他项目也只有好少的房子有 先不填充 删除
FireplaceQu 有一半家没有壁炉 填 0
Garagetype 空代表没有 填 0
Garagefinish 空代表没有 填 0
Garagequal 空代表没有 填 0
Garagecond 空代表没有 填 0
LotFrontage 和物业相连的街道有1/3缺失 没想到太好的填充方法 删除

整理 description 文件

数据描述文件记录了所有特征所代表的含义，其中许多特征是字符串，现在我们要整理为个字典，便于我们查询。

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description_dict = {}
with open('/data_description.txt','r') as description:
    description_data = description.read()
    description.close()
    
description_data = description_data.split('\n')
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for i in description_data:
    if ':' in i:
        key = i.split(':')[0]
        description_dict[key] = []
    elif i.split() and '       ' in i:
        value = i.split()[0]
        description_dict[key].append(value)

print(description_dict)
{'MSSubClass': ['20', '30', '40', '45', '50', '60', '70', '75', '80', '85', '90', '120', '150', '160', '180', '190'], 'MSZoning': ['A', 'C', 'FV', 'I', 'RH', 'RL', 'RP', 'RM'], 'LotFrontage': [], 'LotArea': [], 'Street': ['Grvl', 'Pave'], 'Alley': ['Grvl', 'Pave', 'NA'], 'LotShape': ['Reg', 'IR1', 'IR2', 'IR3'], 'LandContour': ['Lvl', 'Bnk', 'HLS', 'Low'], 'Utilities': ['AllPub', 'NoSewr', 'NoSeWa', 'ELO'], 'LotConfig': ['Inside', 'Corner', 'CulDSac', 'FR2', 'FR3'], 'LandSlope': ['Gtl', 'Mod', 'Sev'], 'Neighborhood': ['Blmngtn', 'Blueste', 'BrDale', 'BrkSide', 'ClearCr', 'CollgCr', 'Crawfor', 'Edwards', 'Gilbert', 'IDOTRR', 'MeadowV', 'Mitchel', 'Names', 'NoRidge', 'NPkVill', 'NridgHt', 'NWAmes', 'OldTown', 'SWISU', 'Sawyer', 'SawyerW', 'Somerst', 'StoneBr', 'Timber', 'Veenker'], 'Condition1': ['Artery', 'Feedr', 'Norm', 'RRNn', 'RRAn', 'PosN', 'PosA', 'RRNe', 'RRAe'], 'Condition2': ['Artery', 'Feedr', 'Norm', 'RRNn', 'RRAn', 'PosN', 'PosA', 'RRNe', 'RRAe'], 'BldgType': ['1Fam', '2FmCon', 'Duplx', 'TwnhsE', 'TwnhsI'], 'HouseStyle': ['1Story'], '       1.5Fin\tOne and one-half story': [], '       1.5Unf\tOne and one-half story': ['2Story'], '       2.5Fin\tTwo and one-half story': [], '       2.5Unf\tTwo and one-half story': ['SFoyer', 'SLvl'], 'OverallQual': ['10', '9', '8', '7', '6', '5', '4', '3', '2', '1'], 'OverallCond': ['10', '9', '8', '7', '6', '5', '4', '3', '2', '1'], 'YearBuilt': [], 'YearRemodAdd': [], 'RoofStyle': ['Flat', 'Gable', 'Gambrel', 'Hip', 'Mansard', 'Shed'], 'RoofMatl': ['ClyTile', 'CompShg', 'Membran', 'Metal', 'Roll', 'Tar&Grv', 'WdShake', 'WdShngl'], 'Exterior1st': ['AsbShng', 'AsphShn', 'BrkComm', 'BrkFace', 'CBlock', 'CemntBd', 'HdBoard', 'ImStucc', 'MetalSd', 'Other', 'Plywood', 'PreCast', 'Stone', 'Stucco', 'VinylSd', 'Wd', 'WdShing'], 'Exterior2nd': ['AsbShng', 'AsphShn', 'BrkComm', 'BrkFace', 'CBlock', 'CemntBd', 'HdBoard', 'ImStucc', 'MetalSd', 'Other', 'Plywood', 'PreCast', 'Stone', 'Stucco', 'VinylSd', 'Wd', 'WdShing'], 'MasVnrType': ['BrkCmn', 'BrkFace', 'CBlock', 'None', 'Stone'], 'MasVnrArea': [], 'ExterQual': ['Ex', 'Gd', 'TA', 'Fa', 'Po'], 'ExterCond': ['Ex', 'Gd', 'TA', 'Fa', 'Po'], 'Foundation': ['BrkTil', 'CBlock', 'PConc', 'Slab', 'Stone', 'Wood'], 'BsmtQual': ['Ex', 'Gd', 'TA', 'Fa', 'Po', 'NA'], 'BsmtCond': ['Ex', 'Gd', 'TA', 'Fa', 'Po', 'NA'], 'BsmtExposure': ['Gd', 'Av', 'Mn', 'No', 'NA'], 'BsmtFinType1': ['GLQ', 'ALQ', 'BLQ', 'Rec', 'LwQ', 'Unf', 'NA'], 'BsmtFinSF1': [], 'BsmtFinType2': ['GLQ', 'ALQ', 'BLQ', 'Rec', 'LwQ', 'Unf', 'NA'], 'BsmtFinSF2': [], 'BsmtUnfSF': [], 'TotalBsmtSF': [], 'Heating': ['Floor', 'GasA', 'GasW', 'Grav', 'OthW', 'Wall'], 'HeatingQC': ['Ex', 'Gd', 'TA', 'Fa', 'Po'], 'CentralAir': ['N', 'Y'], 'Electrical': ['SBrkr', 'FuseA', 'FuseF', 'FuseP', 'Mix'], '1stFlrSF': [], '2ndFlrSF': [], 'LowQualFinSF': [], 'GrLivArea': [], 'BsmtFullBath': [], 'BsmtHalfBath': [], 'FullBath': [], 'HalfBath': [], 'Bedroom': [], 'Kitchen': [], 'KitchenQual': ['Ex', 'Gd', 'TA', 'Fa', 'Po'], 'TotRmsAbvGrd': [], 'Functional': ['Typ', 'Min1', 'Min2', 'Mod', 'Maj1', 'Maj2', 'Sev', 'Sal'], 'Fireplaces': [], 'FireplaceQu': ['Ex', 'Gd', 'TA', 'Fa', 'Po', 'NA'], 'GarageType': ['2Types', 'Attchd', 'Basment', 'BuiltIn', 'CarPort', 'Detchd', 'NA'], 'GarageYrBlt': [], 'GarageFinish': ['Fin', 'RFn', 'Unf', 'NA'], 'GarageCars': [], 'GarageArea': [], 'GarageQual': ['Ex', 'Gd', 'TA', 'Fa', 'Po', 'NA'], 'GarageCond': ['Ex', 'Gd', 'TA', 'Fa', 'Po', 'NA'], 'PavedDrive': ['Y', 'P', 'N'], 'WoodDeckSF': [], 'OpenPorchSF': [], 'EnclosedPorch': [], '3SsnPorch': [], 'ScreenPorch': [], 'PoolArea': [], 'PoolQC': ['Ex', 'Gd', 'TA', 'Fa', 'NA'], 'Fence': ['GdPrv', 'MnPrv', 'GdWo', 'MnWw', 'NA'], 'MiscFeature': ['Elev', 'Gar2', 'Othr', 'Shed', 'TenC', 'NA'], 'MiscVal': [], 'MoSold': [], 'YrSold': [], 'SaleType': ['WD', 'CWD', 'VWD', 'New', 'COD', 'Con', 'ConLw', 'ConLI', 'ConLD', 'Oth'], 'SaleCondition': ['Normal', 'Abnorml', 'AdjLand', 'Alloca', 'Family', 'Partial']}

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description_dict['FireplaceQu']
['Ex', 'Gd', 'TA', 'Fa', 'Po', 'NA']

预处理

首先先删除一些确实较多和不太好填充的feature。

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# 删除
train_df = train_df.drop(['Alley', 'PoolQC', 'MiscFeature', 'LotFrontage'], axis=1)
test_df = test_df.drop(['Alley', 'PoolQC', 'MiscFeature', 'LotFrontage'], axis=1)

处理 GarageYrBlt: Year garage was built

车库的年代，没有填充 0，改为 1900 年开始。

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def preprocess_garage_year(dataset):
    dataset = dataset.fillna(1900)
    dataset -= 1900
    return dataset
    
train_df['GarageYrBlt'] = preprocess_garage_year(train_df['GarageYrBlt'])
test_df['GarageYrBlt'] = preprocess_garage_year(test_df['GarageYrBlt'])

处理 Electrical

Electrical: Electrical system

SBrkr    Standard Circuit Breakers & Romex
FuseA    Fuse Box over 60 AMP and all Romex wiring (Average) 
FuseF    60 AMP Fuse Box and mostly Romex wiring (Fair)
FuseP    60 AMP Fuse Box and mostly knob & tube wiring (poor)
Mix  Mixed

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freq_port = train_df.Electrical.dropna().mode()[0] # 返回出现次数最多的值（众数）
freq_port
'SBrkr'

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def preprocess_garage_year(dataset):
    dataset = dataset.fillna(freq_port)
    return dataset
    
train_df['Electrical'] = preprocess_garage_year(train_df['Electrical'])
test_df['Electrical'] = preprocess_garage_year(test_df['Electrical'])

处理 MasVnrArea: Masonry veneer area in square feet

砖石饰面面积:砖石饰面面积(平方英尺)

缺失的不是太多（148），mean 103，众数（75%以上）为 0，还没想到太好的填充，先填个0试试吧。

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train_df.MasVnrArea.describe()
count   1452.00000
mean     103.68526
std      181.06621
min        0.00000
25%        0.00000
50%        0.00000
75%      166.00000
max     1600.00000
Name: MasVnrArea, dtype: float64

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def preprocess_masvararea(dataset):
    dataset = dataset.fillna(0)
    return dataset
    
train_df['MasVnrArea'] = preprocess_masvararea(train_df['MasVnrArea'])
test_df['MasVnrArea'] = preprocess_masvararea(test_df['MasVnrArea'])

处理 MasVnrType

MasVnrType: Masonry veneer type

BrkCmn   Brick Common
BrkFace  Brick Face
CBlock   Cinder Block
None None
Stone    Stone

这个值很奇怪，不太明白这是什么，是没有好呢还是 Stone 好呢？

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train_df.MasVnrType.dropna().mode()[0] # 返回出现次数最多的值（众数）
'None'

大多数都没有，那就把缺失值填为没有吧。

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def preprocess_masvnrtype(dataset):
    dataset = dataset.fillna('None')
    return dataset
    
train_df['MasVnrType'] = preprocess_masvnrtype(train_df['MasVnrType'])
test_df['MasVnrType'] = preprocess_masvnrtype(test_df['MasVnrType'])

处理其他缺失 feature

需要填充缺失值和重编码。

根据 data description 把 字符串类型的 feature 重编码。

构造 feature 对应的 map

观察发现以下这些缺失我们可以填充，顺便重编码。

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missing_value = ['Fence',
    'FireplaceQu',
    'GarageType',
    'GarageFinish',
    'GarageQual',
    'GarageCond',
    'BsmtQual',
    'BsmtCond',
    'BsmtExposure',
    'BsmtFinType1',
    'BsmtFinType2',]
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def generate_map(map_list, end_index=1):
    d = {}
    j = len(map_list) - end_index
    for i in map_list:
        d[i] = j
        j -= 1
    return d
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missing_map_dict = {}
for feature in missing_value:
    missing_map_dict[feature] = generate_map(description_dict[feature])

missing_map_dict
{'BsmtCond': {'Ex': 5, 'Fa': 2, 'Gd': 4, 'NA': 0, 'Po': 1, 'TA': 3},
 'BsmtExposure': {'Av': 3, 'Gd': 4, 'Mn': 2, 'NA': 0, 'No': 1},
 'BsmtFinType1': {'ALQ': 5,
  'BLQ': 4,
  'GLQ': 6,
  'LwQ': 2,
  'NA': 0,
  'Rec': 3,
  'Unf': 1},
 'BsmtFinType2': {'ALQ': 5,
  'BLQ': 4,
  'GLQ': 6,
  'LwQ': 2,
  'NA': 0,
  'Rec': 3,
  'Unf': 1},
 'BsmtQual': {'Ex': 5, 'Fa': 2, 'Gd': 4, 'NA': 0, 'Po': 1, 'TA': 3},
 'Fence': {'GdPrv': 4, 'GdWo': 2, 'MnPrv': 3, 'MnWw': 1, 'NA': 0},
 'FireplaceQu': {'Ex': 5, 'Fa': 2, 'Gd': 4, 'NA': 0, 'Po': 1, 'TA': 3},
 'GarageCond': {'Ex': 5, 'Fa': 2, 'Gd': 4, 'NA': 0, 'Po': 1, 'TA': 3},
 'GarageFinish': {'Fin': 3, 'NA': 0, 'RFn': 2, 'Unf': 1},
 'GarageQual': {'Ex': 5, 'Fa': 2, 'Gd': 4, 'NA': 0, 'Po': 1, 'TA': 3},
 'GarageType': {'2Types': 6,
  'Attchd': 5,
  'Basment': 4,
  'BuiltIn': 3,
  'CarPort': 2,
  'Detchd': 1,
  'NA': 0}}

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# 预处理 feature 把 str 转换为序列 
def preprocess_feature_strtoint(feature_df, feature_mapping, default=0):
    feature_df = feature_df.map(feature_mapping)
    feature_df = feature_df.fillna(default)
    return feature_df
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def preprocess_feature(dataset):
    for feature in missing_value:
        dataset[feature] = preprocess_feature_strtoint(dataset[feature], missing_map_dict[feature])
    return dataset
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train_df = preprocess_feature(train_df)
test_df = preprocess_feature(test_df)

检查训练集缺失值，已经没有了。

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train_df[train_df.isnull().values==True]
Id MSSubClass MSZoning LotArea Street 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 Fence MiscVal MoSold YrSold SaleType SaleCondition SalePrice

填充测试集

测试集还有许多缺失，先决定用众数填充。

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test_df[test_df.isnull().values==True]
Id MSSubClass MSZoning LotArea Street 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 Fence MiscVal MoSold YrSold SaleType SaleCondition
95 1556 50 RL 10632 Pave IR1 Lvl AllPub Inside Gtl ClearCr Norm Norm 1Fam 1.5Fin 5 3 1917 1950 Gable CompShg Wd Sdng Wd Sdng None 0.00000 TA TA BrkTil 4.00000 2.00000 1.00000 1.00000 0.00000 1.00000 0.00000 689.00000 689.00000 GasA Gd N SBrkr 725 499 0 1224 0.00000 0.00000 1 1 3 1 NaN 6 Mod 0 0.00000 1.00000 17.00000 1.00000 1.00000 180.00000 2.00000 2.00000 N 0 0 248 0 0 0 0.00000 0 1 2010 COD Normal
455 1916 30 NaN 21780 Grvl Reg Lvl NaN Inside Gtl IDOTRR Norm Norm 1Fam 1Story 2 4 1910 1950 Gable CompShg Wd Sdng Wd Sdng None 0.00000 Fa Fa CBlock 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 GasA TA N FuseA 810 0 0 810 0.00000 0.00000 1 0 1 1 TA 4 Min1 0 0.00000 1.00000 75.00000 1.00000 1.00000 280.00000 3.00000 3.00000 N 119 24 0 0 0 0 0.00000 0 3 2009 ConLD Normal
455 1916 30 NaN 21780 Grvl Reg Lvl NaN Inside Gtl IDOTRR Norm Norm 1Fam 1Story 2 4 1910 1950 Gable CompShg Wd Sdng Wd Sdng None 0.00000 Fa Fa CBlock 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 GasA TA N FuseA 810 0 0 810 0.00000 0.00000 1 0 1 1 TA 4 Min1 0 0.00000 1.00000 75.00000 1.00000 1.00000 280.00000 3.00000 3.00000 N 119 24 0 0 0 0 0.00000 0 3 2009 ConLD Normal
485 1946 20 RL 31220 Pave IR1 Bnk NaN FR2 Gtl Gilbert Feedr Norm 1Fam 1Story 6 2 1952 1952 Hip CompShg BrkFace BrkFace None 0.00000 TA TA CBlock 3.00000 3.00000 1.00000 1.00000 0.00000 1.00000 0.00000 1632.00000 1632.00000 GasA TA Y FuseA 1474 0 0 1474 0.00000 0.00000 1 0 3 1 TA 7 Min2 2 4.00000 5.00000 52.00000 1.00000 2.00000 495.00000 3.00000 3.00000 Y 0 0 144 0 0 0 0.00000 750 5 2008 WD Normal
660 2121 20 RM 5940 Pave IR1 Lvl AllPub FR3 Gtl BrkSide Feedr Norm 1Fam 1Story 4 7 1946 1950 Gable CompShg MetalSd CBlock None 0.00000 TA TA PConc 0.00000 0.00000 0.00000 0.00000 nan 0.00000 nan nan nan GasA TA Y FuseA 896 0 0 896 nan nan 1 0 2 1 TA 4 Typ 0 0.00000 1.00000 46.00000 1.00000 1.00000 280.00000 3.00000 3.00000 Y 0 0 0 0 0 0 3.00000 0 4 2008 ConLD Abnorml
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
1013 2474 50 RM 10320 Pave Reg Lvl AllPub Corner Gtl IDOTRR Artery Norm 1Fam 1.5Fin 4 1 1910 1950 Gable CompShg Wd Sdng Wd Sdng None 0.00000 Fa Fa CBlock 3.00000 2.00000 1.00000 1.00000 0.00000 1.00000 0.00000 771.00000 771.00000 GasA Fa Y SBrkr 866 504 114 1484 0.00000 0.00000 2 0 3 1 TA 6 NaN 0 0.00000 1.00000 10.00000 1.00000 1.00000 264.00000 3.00000 2.00000 N 14 211 0 0 84 0 0.00000 0 9 2007 COD Abnorml
1029 2490 20 RL 13770 Pave Reg Lvl AllPub Corner Gtl Sawyer Feedr Norm 1Fam 1Story 5 6 1958 1998 Gable CompShg Plywood Plywood BrkFace 340.00000 TA TA CBlock 3.00000 3.00000 2.00000 3.00000 190.00000 4.00000 873.00000 95.00000 1158.00000 GasA TA Y SBrkr 1176 0 0 1176 1.00000 0.00000 1 0 3 1 TA 6 Typ 2 4.00000 5.00000 58.00000 1.00000 1.00000 303.00000 3.00000 3.00000 Y 0 0 0 0 0 0 0.00000 0 10 2007 NaN Normal
1116 2577 70 RM 9060 Pave Reg Lvl AllPub Inside Gtl IDOTRR Norm Norm 1Fam 2Story 5 6 1923 1999 Gable CompShg Wd Sdng Plywood None 0.00000 TA TA BrkTil 4.00000 3.00000 1.00000 5.00000 548.00000 1.00000 0.00000 311.00000 859.00000 GasA Ex Y SBrkr 942 886 0 1828 0.00000 0.00000 2 0 3 1 Gd 6 Typ 0 0.00000 1.00000 0.00000 0.00000 nan nan 0.00000 0.00000 Y 174 0 212 0 0 0 3.00000 0 3 2007 WD Alloca
1116 2577 70 RM 9060 Pave Reg Lvl AllPub Inside Gtl IDOTRR Norm Norm 1Fam 2Story 5 6 1923 1999 Gable CompShg Wd Sdng Plywood None 0.00000 TA TA BrkTil 4.00000 3.00000 1.00000 5.00000 548.00000 1.00000 0.00000 311.00000 859.00000 GasA Ex Y SBrkr 942 886 0 1828 0.00000 0.00000 2 0 3 1 Gd 6 Typ 0 0.00000 1.00000 0.00000 0.00000 nan nan 0.00000 0.00000 Y 174 0 212 0 0 0 3.00000 0 3 2007 WD Alloca
1444 2905 20 NaN 31250 Pave Reg Lvl AllPub Inside Gtl Mitchel Artery Norm 1Fam 1Story 1 3 1951 1951 Gable CompShg CBlock VinylSd None 0.00000 TA Fa CBlock 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 GasA TA Y FuseA 1600 0 0 1600 0.00000 0.00000 1 1 3 1 TA 6 Mod 0 0.00000 5.00000 51.00000 1.00000 1.00000 270.00000 2.00000 3.00000 N 0 0 135 0 0 0 0.00000 0 5 2006 WD Normal

22 rows × 76 columns

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for i in test_df.columns.values.tolist():
    freq_port = test_df[i].dropna().mode()[0] # 返回出现次数最多的值（众数)
    test_df[i] = test_df[i].fillna(freq_port)

字符串类型 feature 重编码

处理完缺失值后观察下还有那些feature是字符串形式的。

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train_df.describe(include="O")
MSZoning Street LotShape LandContour Utilities LotConfig LandSlope Neighborhood Condition1 Condition2 BldgType HouseStyle RoofStyle RoofMatl Exterior1st Exterior2nd MasVnrType ExterQual ExterCond Foundation Heating HeatingQC CentralAir Electrical KitchenQual Functional PavedDrive SaleType SaleCondition
count 1460 1460 1460 1460 1460 1460 1460 1460 1460 1460 1460 1460 1460 1460 1460 1460 1460 1460 1460 1460 1460 1460 1460 1460 1460 1460 1460 1460 1460
unique 5 2 4 4 2 5 3 25 9 8 5 8 6 8 15 16 4 4 5 6 6 5 2 5 4 7 3 9 6
top RL Pave Reg Lvl AllPub Inside Gtl NAmes Norm Norm 1Fam 1Story Gable CompShg VinylSd VinylSd None TA TA PConc GasA Ex Y SBrkr TA Typ Y WD Normal
freq 1151 1454 925 1311 1459 1052 1382 225 1260 1445 1220 726 1141 1434 515 504 872 906 1282 647 1428 741 1365 1335 735 1360 1340 1267 1198

目前还有以下的 feature 需要编码

[‘MSZoning’, ‘Street’, ‘LotShape’, ‘LandContour’, ‘Utilities’, ‘LotConfig’, ‘LandSlope’, ‘Neighborhood’, ‘Condition1’, ‘Condition2’, ‘BldgType’, ‘HouseStyle’, ‘RoofStyle’, ‘RoofMatl’, ‘Exterior1st’, ‘Exterior2nd’, ‘MasVnrType’, ‘ExterQual’, ‘ExterCond’, ‘Foundation’, ‘Heating’, ‘HeatingQC’, ‘CentralAir’, ‘Electrical’, ‘KitchenQual’, ‘Functional’, ‘PavedDrive’, ‘SaleType’, ‘SaleCondition’]

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feature = ['MSZoning', 'Street', 'LotShape', 'LandContour', 'Utilities', 'LotConfig', 'LandSlope', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType', 'HouseStyle', 'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType', 'ExterQual', 'ExterCond', 'Foundation', 'Heating', 'HeatingQC', 'CentralAir', 'Electrical', 'KitchenQual', 'Functional', 'PavedDrive', 'SaleType', 'SaleCondition']
for i in feature:
    print(set(train_df[i]))
{'RL', 'FV', 'C (all)', 'RM', 'RH'}
{'Grvl', 'Pave'}
{'IR2', 'Reg', 'IR3', 'IR1'}
{'Lvl', 'Low', 'HLS', 'Bnk'}
{'AllPub', 'NoSeWa'}
{'CulDSac', 'FR2', 'Corner', 'FR3', 'Inside'}
{'Gtl', 'Mod', 'Sev'}
{'BrDale', 'NoRidge', 'Blmngtn', 'Sawyer', 'NPkVill', 'NridgHt', 'SawyerW', 'Mitchel', 'OldTown', 'NWAmes', 'NAmes', 'Somerst', 'Veenker', 'SWISU', 'CollgCr', 'BrkSide', 'ClearCr', 'IDOTRR', 'Crawfor', 'StoneBr', 'Timber', 'Gilbert', 'Blueste', 'MeadowV', 'Edwards'}
{'Artery', 'PosN', 'PosA', 'Norm', 'RRNn', 'RRAe', 'RRNe', 'Feedr', 'RRAn'}
{'Artery', 'PosN', 'PosA', 'Norm', 'RRNn', 'RRAe', 'Feedr', 'RRAn'}
{'2fmCon', 'Duplex', 'Twnhs', 'TwnhsE', '1Fam'}
{'SFoyer', '1.5Fin', '2Story', '1.5Unf', '2.5Fin', '2.5Unf', '1Story', 'SLvl'}
{'Mansard', 'Shed', 'Gable', 'Flat', 'Gambrel', 'Hip'}
{'Roll', 'Metal', 'ClyTile', 'WdShngl', 'CompShg', 'Tar&Grv', 'Membran', 'WdShake'}
{'BrkFace', 'Plywood', 'MetalSd', 'Stucco', 'WdShing', 'CBlock', 'AsphShn', 'Stone', 'ImStucc', 'CemntBd', 'Wd Sdng', 'VinylSd', 'BrkComm', 'AsbShng', 'HdBoard'}
{'BrkFace', 'Plywood', 'Wd Shng', 'MetalSd', 'CmentBd', 'Stucco', 'Other', 'CBlock', 'AsphShn', 'ImStucc', 'Stone', 'Wd Sdng', 'VinylSd', 'AsbShng', 'Brk Cmn', 'HdBoard'}
{'BrkFace', 'None', 'BrkCmn', 'Stone'}
{'Ex', 'Gd', 'Fa', 'TA'}
{'Fa', 'Gd', 'Po', 'TA', 'Ex'}
{'BrkTil', 'PConc', 'CBlock', 'Stone', 'Slab', 'Wood'}
{'GasA', 'GasW', 'Wall', 'Floor', 'Grav', 'OthW'}
{'Fa', 'Gd', 'Po', 'TA', 'Ex'}
{'Y', 'N'}
{'SBrkr', 'Mix', 'FuseA', 'FuseP', 'FuseF'}
{'Ex', 'Gd', 'Fa', 'TA'}
{'Mod', 'Min1', 'Maj1', 'Min2', 'Maj2', 'Typ', 'Sev'}
{'Y', 'N', 'P'}
{'Con', 'ConLD', 'ConLw', 'CWD', 'WD', 'New', 'ConLI', 'Oth', 'COD'}
{'Abnorml', 'Alloca', 'Normal', 'AdjLand', 'Partial', 'Family'}

观察到这些 feature 即有优劣等级划分的，也有没有等级的，既然如此，把有等级区分的编码为序列，没有等级的使用独热码。

有等级的：

MSZoning
ExterQual
ExterCond
HeatingQC
KitchenQual

其余的使用onehot编码。

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order_feature_set ={'MSZoning',
'ExterQual',
'ExterCond',
'HeatingQC',
'KitchenQual',}

onehot_feature_set = set(feature) - order_feature_set

Order 编码

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def preprocess_order_feature(dataset):
    for i in order_feature_set:
        order_map = generate_map(description_dict[i], 0)
        dataset[i] = dataset[i].map(order_map)
        dataset[i] = dataset[i].fillna(0)
    return dataset
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train_df = preprocess_order_feature(train_df)
test_df = preprocess_order_feature(test_df)
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train_df.describe()
Id MSSubClass MSZoning LotArea OverallQual OverallCond YearBuilt YearRemodAdd MasVnrArea ExterQual ExterCond BsmtQual BsmtCond BsmtExposure BsmtFinType1 BsmtFinSF1 BsmtFinType2 BsmtFinSF2 BsmtUnfSF TotalBsmtSF HeatingQC 1stFlrSF 2ndFlrSF LowQualFinSF GrLivArea BsmtFullBath BsmtHalfBath FullBath HalfBath BedroomAbvGr KitchenAbvGr KitchenQual TotRmsAbvGrd Fireplaces FireplaceQu GarageType GarageYrBlt GarageFinish GarageCars GarageArea GarageQual GarageCond WoodDeckSF OpenPorchSF EnclosedPorch 3SsnPorch ScreenPorch PoolArea Fence MiscVal MoSold YrSold SalePrice
count 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000
mean 730.50000 56.89726 2.82534 10516.82808 6.09932 5.57534 1971.26781 1984.86575 103.11712 3.39589 3.08356 3.48904 2.93493 1.63014 3.54589 443.63973 1.24726 46.54932 567.24041 1057.42945 4.14521 1162.62671 346.99247 5.84452 1515.46370 0.42534 0.05753 1.56507 0.38288 2.86644 1.04658 3.51164 6.51781 0.61301 1.82534 3.51438 74.15068 1.71575 1.76712 472.98014 2.81027 2.80890 94.24452 46.66027 21.95411 3.40959 15.06096 2.75890 0.56575 43.48904 6.32192 2007.81575 180921.19589
std 421.61001 42.30057 1.02017 9981.26493 1.38300 1.11280 30.20290 20.64541 180.73137 0.57428 0.35105 0.87648 0.55216 1.06739 2.10778 456.09809 0.89233 161.31927 441.86696 438.70532 0.95950 386.58774 436.52844 48.62308 525.48038 0.51891 0.23875 0.55092 0.50289 0.81578 0.22034 0.66376 1.62539 0.64467 1.81088 1.93321 29.98205 0.89283 0.74732 213.80484 0.72290 0.71969 125.33879 66.25603 61.11915 29.31733 55.75742 40.17731 1.20448 496.12302 2.70363 1.32810 79442.50288
min 1.00000 20.00000 0.00000 1300.00000 1.00000 1.00000 1872.00000 1950.00000 0.00000 2.00000 1.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 1.00000 334.00000 0.00000 0.00000 334.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 2.00000 2.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 1.00000 2006.00000 34900.00000
25% 365.75000 20.00000 3.00000 7553.50000 5.00000 5.00000 1954.00000 1967.00000 0.00000 3.00000 3.00000 3.00000 3.00000 1.00000 1.00000 0.00000 1.00000 0.00000 223.00000 795.75000 3.00000 882.00000 0.00000 0.00000 1129.50000 0.00000 0.00000 1.00000 0.00000 2.00000 1.00000 3.00000 5.00000 0.00000 0.00000 1.00000 58.00000 1.00000 1.00000 334.50000 3.00000 3.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 5.00000 2007.00000 129975.00000
50% 730.50000 50.00000 3.00000 9478.50000 6.00000 5.00000 1973.00000 1994.00000 0.00000 3.00000 3.00000 4.00000 3.00000 1.00000 4.00000 383.50000 1.00000 0.00000 477.50000 991.50000 5.00000 1087.00000 0.00000 0.00000 1464.00000 0.00000 0.00000 2.00000 0.00000 3.00000 1.00000 3.00000 6.00000 1.00000 2.00000 5.00000 77.00000 2.00000 2.00000 480.00000 3.00000 3.00000 0.00000 25.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 6.00000 2008.00000 163000.00000
75% 1095.25000 70.00000 3.00000 11601.50000 7.00000 6.00000 2000.00000 2004.00000 164.25000 4.00000 3.00000 4.00000 3.00000 2.00000 6.00000 712.25000 1.00000 0.00000 808.00000 1298.25000 5.00000 1391.25000 728.00000 0.00000 1776.75000 1.00000 0.00000 2.00000 1.00000 3.00000 1.00000 4.00000 7.00000 1.00000 4.00000 5.00000 101.00000 2.00000 2.00000 576.00000 3.00000 3.00000 168.00000 68.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 8.00000 2009.00000 214000.00000
max 1460.00000 190.00000 6.00000 215245.00000 10.00000 9.00000 2010.00000 2010.00000 1600.00000 5.00000 5.00000 5.00000 4.00000 4.00000 6.00000 5644.00000 6.00000 1474.00000 2336.00000 6110.00000 5.00000 4692.00000 2065.00000 572.00000 5642.00000 3.00000 2.00000 3.00000 2.00000 8.00000 3.00000 5.00000 14.00000 3.00000 5.00000 6.00000 110.00000 3.00000 4.00000 1418.00000 5.00000 5.00000 857.00000 547.00000 552.00000 508.00000 480.00000 738.00000 4.00000 15500.00000 12.00000 2010.00000 755000.00000

One hot 编码

1
train_df.shape, test_df.shape
((1460, 77), (1459, 76))

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def preprocess_onehot_feature(dataset):
    dataset_len = len(dataset)
    result = pd.DataFrame()
    for i in onehot_feature_set:
        tmp_pd = pd.get_dummies(dataset[i], prefix=i, dtype=float)
        result = pd.concat([result, tmp_pd], axis=1)
    return result
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train_onehot_df = preprocess_onehot_feature(train_df)
test_onehot_df = preprocess_onehot_feature(test_df)
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print(train_onehot_df.shape, test_onehot_df.shape)
test_missing = set(train_onehot_df.columns.values.tolist()) - set(test_onehot_df.columns.values.tolist())
test_missing
(1460, 168) (1459, 153)





{'Condition2_RRAe',
 'Condition2_RRAn',
 'Condition2_RRNn',
 'Electrical_Mix',
 'Exterior1st_ImStucc',
 'Exterior1st_Stone',
 'Exterior2nd_Other',
 'Heating_Floor',
 'Heating_OthW',
 'HouseStyle_2.5Fin',
 'RoofMatl_ClyTile',
 'RoofMatl_Membran',
 'RoofMatl_Metal',
 'RoofMatl_Roll',
 'Utilities_NoSeWa'}

又遇到个坑，测试集比训练集的情况少。

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# 生成一个缺失的 dagafarm
test_missing_zeros = pd.Series(np.zeros(1459))
test_missing_fd = pd.DataFrame({k: test_missing_zeros for k in test_missing} )

# 把缺失的填进去
test_onehot_df = pd.concat([test_onehot_df, test_missing_fd], axis=1)
1
print(train_onehot_df.shape, test_onehot_df.shape)
(1460, 168) (1459, 168)

把 新 one hot 编码的 feature 添加到 原集合中，并删除原来的 feature

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7
# 把one hot的feature添加到训练集和测试集
train_df = pd.concat([train_df, train_onehot_df], axis=1)
test_df = pd.concat([test_df, test_onehot_df], axis=1)

# 同步删除训练集和测试集中的原feature
train_df = train_df.drop(onehot_feature_set, axis=1)
test_df = test_df.drop(onehot_feature_set, axis=1)
1
train_df.describe()
Id MSSubClass MSZoning LotArea OverallQual OverallCond YearBuilt YearRemodAdd MasVnrArea ExterQual ExterCond BsmtQual BsmtCond BsmtExposure BsmtFinType1 BsmtFinSF1 BsmtFinType2 BsmtFinSF2 BsmtUnfSF TotalBsmtSF HeatingQC 1stFlrSF 2ndFlrSF LowQualFinSF GrLivArea BsmtFullBath BsmtHalfBath FullBath HalfBath BedroomAbvGr KitchenAbvGr KitchenQual TotRmsAbvGrd Fireplaces FireplaceQu GarageType GarageYrBlt GarageFinish GarageCars GarageArea ... Exterior1st_BrkFace Exterior1st_CBlock Exterior1st_CemntBd Exterior1st_HdBoard Exterior1st_ImStucc Exterior1st_MetalSd Exterior1st_Plywood Exterior1st_Stone Exterior1st_Stucco Exterior1st_VinylSd Exterior1st_Wd Sdng Exterior1st_WdShing RoofMatl_ClyTile RoofMatl_CompShg RoofMatl_Membran RoofMatl_Metal RoofMatl_Roll RoofMatl_Tar&Grv RoofMatl_WdShake RoofMatl_WdShngl Electrical_FuseA Electrical_FuseF Electrical_FuseP Electrical_Mix Electrical_SBrkr SaleCondition_Abnorml SaleCondition_AdjLand SaleCondition_Alloca SaleCondition_Family SaleCondition_Normal SaleCondition_Partial Condition1_Artery Condition1_Feedr Condition1_Norm Condition1_PosA Condition1_PosN Condition1_RRAe Condition1_RRAn Condition1_RRNe Condition1_RRNn
count 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 ... 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000 1460.00000
mean 730.50000 56.89726 2.82534 10516.82808 6.09932 5.57534 1971.26781 1984.86575 103.11712 3.39589 3.08356 3.48904 2.93493 1.63014 3.54589 443.63973 1.24726 46.54932 567.24041 1057.42945 4.14521 1162.62671 346.99247 5.84452 1515.46370 0.42534 0.05753 1.56507 0.38288 2.86644 1.04658 3.51164 6.51781 0.61301 1.82534 3.51438 74.15068 1.71575 1.76712 472.98014 ... 0.03425 0.00068 0.04178 0.15205 0.00068 0.15068 0.07397 0.00137 0.01712 0.35274 0.14110 0.01781 0.00068 0.98219 0.00068 0.00068 0.00068 0.00753 0.00342 0.00411 0.06438 0.01849 0.00205 0.00068 0.91438 0.06918 0.00274 0.00822 0.01370 0.82055 0.08562 0.03288 0.05548 0.86301 0.00548 0.01301 0.00753 0.01781 0.00137 0.00342
std 421.61001 42.30057 1.02017 9981.26493 1.38300 1.11280 30.20290 20.64541 180.73137 0.57428 0.35105 0.87648 0.55216 1.06739 2.10778 456.09809 0.89233 161.31927 441.86696 438.70532 0.95950 386.58774 436.52844 48.62308 525.48038 0.51891 0.23875 0.55092 0.50289 0.81578 0.22034 0.66376 1.62539 0.64467 1.81088 1.93321 29.98205 0.89283 0.74732 213.80484 ... 0.18192 0.02617 0.20016 0.35920 0.02617 0.35786 0.26182 0.03700 0.12978 0.47799 0.34824 0.13230 0.02617 0.13230 0.02617 0.02617 0.02617 0.08650 0.05844 0.06400 0.24552 0.13477 0.04530 0.02617 0.27989 0.25384 0.05229 0.09032 0.11628 0.38386 0.27989 0.17837 0.22899 0.34395 0.07385 0.11337 0.08650 0.13230 0.03700 0.05844
min 1.00000 20.00000 0.00000 1300.00000 1.00000 1.00000 1872.00000 1950.00000 0.00000 2.00000 1.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 1.00000 334.00000 0.00000 0.00000 334.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 2.00000 2.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 ... 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000
25% 365.75000 20.00000 3.00000 7553.50000 5.00000 5.00000 1954.00000 1967.00000 0.00000 3.00000 3.00000 3.00000 3.00000 1.00000 1.00000 0.00000 1.00000 0.00000 223.00000 795.75000 3.00000 882.00000 0.00000 0.00000 1129.50000 0.00000 0.00000 1.00000 0.00000 2.00000 1.00000 3.00000 5.00000 0.00000 0.00000 1.00000 58.00000 1.00000 1.00000 334.50000 ... 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 1.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 1.00000 0.00000 0.00000 0.00000 0.00000 1.00000 0.00000 0.00000 0.00000 1.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000
50% 730.50000 50.00000 3.00000 9478.50000 6.00000 5.00000 1973.00000 1994.00000 0.00000 3.00000 3.00000 4.00000 3.00000 1.00000 4.00000 383.50000 1.00000 0.00000 477.50000 991.50000 5.00000 1087.00000 0.00000 0.00000 1464.00000 0.00000 0.00000 2.00000 0.00000 3.00000 1.00000 3.00000 6.00000 1.00000 2.00000 5.00000 77.00000 2.00000 2.00000 480.00000 ... 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 1.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 1.00000 0.00000 0.00000 0.00000 0.00000 1.00000 0.00000 0.00000 0.00000 1.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000
75% 1095.25000 70.00000 3.00000 11601.50000 7.00000 6.00000 2000.00000 2004.00000 164.25000 4.00000 3.00000 4.00000 3.00000 2.00000 6.00000 712.25000 1.00000 0.00000 808.00000 1298.25000 5.00000 1391.25000 728.00000 0.00000 1776.75000 1.00000 0.00000 2.00000 1.00000 3.00000 1.00000 4.00000 7.00000 1.00000 4.00000 5.00000 101.00000 2.00000 2.00000 576.00000 ... 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 1.00000 0.00000 0.00000 0.00000 1.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 1.00000 0.00000 0.00000 0.00000 0.00000 1.00000 0.00000 0.00000 0.00000 1.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000
max 1460.00000 190.00000 6.00000 215245.00000 10.00000 9.00000 2010.00000 2010.00000 1600.00000 5.00000 5.00000 5.00000 4.00000 4.00000 6.00000 5644.00000 6.00000 1474.00000 2336.00000 6110.00000 5.00000 4692.00000 2065.00000 572.00000 5642.00000 3.00000 2.00000 3.00000 2.00000 8.00000 3.00000 5.00000 14.00000 3.00000 5.00000 6.00000 110.00000 3.00000 4.00000 1418.00000 ... 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000

8 rows × 221 columns

1
train_df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1460 entries, 0 to 1459
Columns: 221 entries, Id to Condition1_RRNn
dtypes: float64(182), int64(39)
memory usage: 2.5 MB

终于处理完了，已经没有 object 类型的数据了。接下来就能建模了。

模型预测

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train_df.sample(frac=1)
X_train = train_df.drop(['SalePrice', 'Id'], axis=1)
Y_train = train_df['SalePrice']
X_test = test_df.drop('Id', axis=1).copy()
X_train.shape, Y_train.shape, X_test.shape
((1460, 219), (1460,), (1459, 219))

先用逻辑回归看看

1
X_train
Id MSSubClass MSZoning LotArea OverallQual OverallCond YearBuilt YearRemodAdd MasVnrArea ExterQual ExterCond BsmtQual BsmtCond BsmtExposure BsmtFinType1 BsmtFinSF1 BsmtFinType2 BsmtFinSF2 BsmtUnfSF TotalBsmtSF HeatingQC 1stFlrSF 2ndFlrSF LowQualFinSF GrLivArea BsmtFullBath BsmtHalfBath FullBath HalfBath BedroomAbvGr KitchenAbvGr KitchenQual TotRmsAbvGrd Fireplaces FireplaceQu GarageType GarageYrBlt GarageFinish GarageCars GarageArea ... Exterior1st_BrkFace Exterior1st_CBlock Exterior1st_CemntBd Exterior1st_HdBoard Exterior1st_ImStucc Exterior1st_MetalSd Exterior1st_Plywood Exterior1st_Stone Exterior1st_Stucco Exterior1st_VinylSd Exterior1st_Wd Sdng Exterior1st_WdShing RoofMatl_ClyTile RoofMatl_CompShg RoofMatl_Membran RoofMatl_Metal RoofMatl_Roll RoofMatl_Tar&Grv RoofMatl_WdShake RoofMatl_WdShngl Electrical_FuseA Electrical_FuseF Electrical_FuseP Electrical_Mix Electrical_SBrkr SaleCondition_Abnorml SaleCondition_AdjLand SaleCondition_Alloca SaleCondition_Family SaleCondition_Normal SaleCondition_Partial Condition1_Artery Condition1_Feedr Condition1_Norm Condition1_PosA Condition1_PosN Condition1_RRAe Condition1_RRAn Condition1_RRNe Condition1_RRNn
0 1 60 3.00000 8450 7 5 2003 2003 196.00000 4 3 4.00000 3.00000 1.00000 6.00000 706 1.00000 0 150 856 5 856 854 0 1710 1 0 2 1 3 1 4 8 0 0.00000 5.00000 103.00000 2.00000 2 548 ... 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 1.00000 0.00000 0.00000 0.00000 1.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 1.00000 0.00000 0.00000 0.00000 0.00000 1.00000 0.00000 0.00000 0.00000 1.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000
1 2 20 3.00000 9600 6 8 1976 1976 0.00000 3 3 4.00000 3.00000 4.00000 5.00000 978 1.00000 0 284 1262 5 1262 0 0 1262 0 1 2 0 3 1 3 6 1 3.00000 5.00000 76.00000 2.00000 2 460 ... 0.00000 0.00000 0.00000 0.00000 0.00000 1.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 1.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 1.00000 0.00000 0.00000 0.00000 0.00000 1.00000 0.00000 0.00000 1.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000
2 3 60 3.00000 11250 7 5 2001 2002 162.00000 4 3 4.00000 3.00000 2.00000 6.00000 486 1.00000 0 434 920 5 920 866 0 1786 1 0 2 1 3 1 4 6 1 3.00000 5.00000 101.00000 2.00000 2 608 ... 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 1.00000 0.00000 0.00000 0.00000 1.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 1.00000 0.00000 0.00000 0.00000 0.00000 1.00000 0.00000 0.00000 0.00000 1.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000
3 4 70 3.00000 9550 7 5 1915 1970 0.00000 3 3 3.00000 4.00000 1.00000 5.00000 216 1.00000 0 540 756 4 961 756 0 1717 1 0 1 0 3 1 4 7 1 4.00000 1.00000 98.00000 1.00000 3 642 ... 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 1.00000 0.00000 0.00000 1.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 1.00000 1.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 1.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000
4 5 60 3.00000 14260 8 5 2000 2000 350.00000 4 3 4.00000 3.00000 3.00000 6.00000 655 1.00000 0 490 1145 5 1145 1053 0 2198 1 0 2 1 4 1 4 9 1 3.00000 5.00000 100.00000 2.00000 3 836 ... 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 1.00000 0.00000 0.00000 0.00000 1.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 1.00000 0.00000 0.00000 0.00000 0.00000 1.00000 0.00000 0.00000 0.00000 1.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
1455 1456 60 3.00000 7917 6 5 1999 2000 0.00000 3 3 4.00000 3.00000 1.00000 1.00000 0 1.00000 0 953 953 5 953 694 0 1647 0 0 2 1 3 1 3 7 1 3.00000 5.00000 99.00000 2.00000 2 460 ... 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 1.00000 0.00000 0.00000 0.00000 1.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 1.00000 0.00000 0.00000 0.00000 0.00000 1.00000 0.00000 0.00000 0.00000 1.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000
1456 1457 20 3.00000 13175 6 6 1978 1988 119.00000 3 3 4.00000 3.00000 1.00000 5.00000 790 3.00000 163 589 1542 3 2073 0 0 2073 1 0 2 0 3 1 3 7 2 3.00000 5.00000 78.00000 1.00000 2 500 ... 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 1.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 1.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 1.00000 0.00000 0.00000 0.00000 0.00000 1.00000 0.00000 0.00000 0.00000 1.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000
1457 1458 70 3.00000 9042 7 9 1941 2006 0.00000 5 4 3.00000 4.00000 1.00000 6.00000 275 1.00000 0 877 1152 5 1188 1152 0 2340 0 0 2 0 4 1 4 9 2 4.00000 5.00000 41.00000 2.00000 1 252 ... 0.00000 0.00000 1.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 1.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 1.00000 0.00000 0.00000 0.00000 0.00000 1.00000 0.00000 0.00000 0.00000 1.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000
1458 1459 20 3.00000 9717 5 6 1950 1996 0.00000 3 3 3.00000 3.00000 2.00000 6.00000 49 3.00000 1029 0 1078 4 1078 0 0 1078 1 0 1 0 2 1 4 5 0 0.00000 5.00000 50.00000 1.00000 1 240 ... 0.00000 0.00000 0.00000 0.00000 0.00000 1.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 1.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 1.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 1.00000 0.00000 0.00000 0.00000 1.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000
1459 1460 20 3.00000 9937 5 6 1965 1965 0.00000 4 3 3.00000 3.00000 1.00000 4.00000 830 2.00000 290 136 1256 4 1256 0 0 1256 1 0 1 1 3 1 3 6 0 0.00000 5.00000 65.00000 3.00000 1 276 ... 0.00000 0.00000 0.00000 1.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 1.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 1.00000 0.00000 0.00000 0.00000 0.00000 1.00000 0.00000 0.00000 0.00000 1.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000

1460 rows × 220 columns

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7
# 逻辑回归

logreg = LogisticRegression()
logreg.fit(X_train, Y_train)
Y_pred = logreg.predict(X_test)
acc_log = round(logreg.score(X_train, Y_train) * 100, 2)
acc_log
/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.
  FutureWarning)
/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.
  "this warning.", FutureWarning)
/usr/local/lib/python3.6/dist-packages/sklearn/svm/base.py:929: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
  "the number of iterations.", ConvergenceWarning)





90.55

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8
# Random Forest

random_forest = RandomForestClassifier(n_estimators=100)
random_forest.fit(X_train, Y_train)
Y_pred = random_forest.predict(X_test)
random_forest.score(X_train, Y_train)
acc_random_forest = round(random_forest.score(X_train, Y_train) * 100, 2)
acc_random_forest
100.0

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10

X = X_train.loc[: 1000]
Y = Y_train.loc[: 1000]

X_t = X_train.loc[1000: ]
Y_t = Y_train.loc[1000: ]

logreg = RandomForestClassifier(n_estimators=100)
logreg.fit(X, Y)
pred = logreg.predict(X_t)
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5
plt.figure(figsize=(30, 5))
x = np.array(range(len(pred)))
plt.scatter(x, pred)
plt.scatter(x, Y_t)
plt.show()

png

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4
plt.figure(figsize=(30, 5))
plt.plot(pred - Y_t)
plt.show()
np.var(pred - Y_t)

png

2231563230.032489

1
np.array([range(len(pred))]*10).T
array([[  0,   0,   0, ...,   0,   0,   0],
       [  1,   1,   1, ...,   1,   1,   1],
       [  2,   2,   2, ...,   2,   2,   2],
       ...,
       [457, 457, 457, ..., 457, 457, 457],
       [458, 458, 458, ..., 458, 458, 458],
       [459, 459, 459, ..., 459, 459, 459]])

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# Support Vector Machines

svm = SVC()
svm.fit(X_train, Y_train)
Y_pred = svm.predict(X_test)
acc_svm = round(svm.score(X_train, Y_train) * 100, 2)
acc_svm
/usr/local/lib/python3.6/dist-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.
  "avoid this warning.", FutureWarning)





99.52

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# 保存结果

submission = pd.DataFrame({
    "Id": test_df["Id"],
    "SalePrice": Y_pred
    })
submission.to_csv("/submission.csv", index=False)
0%