kaggle house price

kaggle 竞赛入门
导入常用的数据分析以及模型的库
数据处理
Data fields
Exploratory Data Analysis
- Correlation matrix
- BsmtQual
- BsmtCond
- BsmtExplosure
- BsmtFinType1
- BsmtFinSF1
- BsmtFinType2
- BsmtFinSF2
- BsmtUnfSF
- TotalBsmtSF
- 1stFlrSF
- 2ndFlrSF
- LowQualFinSF
- BsmtHalfBath BsmtFullBath HalfBath FullBath
- TotRmsAbvGrd
- Fireplaces
- FireplaceQu
- GrLivArea
- MSSubClass
- BldgType
- HouseStyle
- OverallQual
- OverallCond
- YearRemodAdd
- YearBuilt
- Foundation
- Functional
- RoofStyle
- RoofMatl
- Exterior1st & Exterior2nd
- MasVnrType
- MasVnrArea
- ExterQual
- ExterCond
- GarageType
- GarageYrBlt
- GarageFinish
- GarageCars
- GarageArea
- GarageQual
- GarageCond
- WoodDeckSF
- TotalPorchSF
- PoolArea
- PoolQC
- Fence
- MSZoning
- Neighborhood
- Condition1 & Condition2
- LotFrontage
- LotArea
- LotShape
- LandContour
- LotConfig
- LandSlope
- Street
- Alley
- PvaeDrive
- Heating
- HeatingQC
- CentralAir
- Electrical
- MoSold and YrSold
- SaleType
- SaleCondition
目标值转换
- 处理数据中偏态的特征
准备模型训练的数据
- feature importance
训练不同的模型
- 优化参数
stacking method

kaggle 竞赛入门

对于刚刚入门机器学习的的同学来说，kaggle竞赛通常是他们学习和跟其他的全世界范围内的参赛选手切磋的一个大的平台，这个平台上提供了一些入门的竞赛，可以供刚入门的同学一展拳脚
本文针对房价预测的这个竞赛展开，从EDA，特征工程，到模型调参开始讲述一些竞赛中的小的trick，希望对大家有些帮助,本人基础一般，如果有贻笑大方的地方，可以随意拍砖

from IPython.display import HTML
from IPython.display import Image

HTML('''<script>
code_show=true; 
function code_toggle() {
 if (code_show){
 $('div.input').hide();
 } else {
 $('div.input').show();
 }
 code_show = !code_show
} 
$( document ).ready(code_toggle);
</script>
<form action="javascript:code_toggle()"><input type="submit" value="Click here to toggle on/off the raw code."></form>''')

导入常用的数据分析以及模型的库

import pandas as pd
import numpy as np

!ls

data_description.txt
data_description.zip
kaggle house price.ipynb
sample_submission.csv
stacking-house-prices-walkthrough-to-top-5.ipynb
test.csv
train.csv

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

train.head()

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

5 rows × 81 columns

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

print(train.shape)
print(test.shape)

(1460, 81)
(1459, 80)

数据结构类似于波士顿房屋的价格数据，其中该数据集中有79个特征，来描述房屋，可以通过数据描述来查看对应字段的意义
同时本文也将缺失值处理的方法进行阐述
PoolQC 7 non-null object
Fence 281 non-null object
MiscFeature 54 non-null object 以上三个特征缺失较为明显，后文将有对应的对缺失值处理的方法

数据处理

处理异常值

异常值通常是指在预期的值之外，至于如何处理异常值，怎么界定异常值，取决于个人和特定的问题
对于异常值通常会在数据分布点之外，因此通常会让计算的结果和数据的分布
以下图为例

with open ('data_description.txt','r') as f:
    for i in f.readlines():
        print(i)
        break

MSSubClass: Identifies the type of dwelling involved in the sale.

Data fields

Here's a brief version of what you'll find in the data description file.

SalePrice - the property's sale price in dollars. This is the target variable that you're trying to predict.
MSSubClass: The building class
MSZoning: The general zoning classification
LotFrontage: Linear feet of street connected to property
LotArea: Lot size in square feet
Street: Type of road access
Alley: Type of alley access
LotShape: General shape of property
LandContour: Flatness of the property
Utilities: Type of utilities available
LotConfig: Lot configuration
LandSlope: Slope of property
Neighborhood: Physical locations within Ames city limits
Condition1: Proximity to main road or railroad
Condition2: Proximity to main road or railroad (if a second is present)
BldgType: Type of dwelling
HouseStyle: Style of dwelling
OverallQual: Overall material and finish quality
OverallCond: Overall condition rating
YearBuilt: Original construction date
YearRemodAdd: Remodel date
RoofStyle: Type of roof
RoofMatl: Roof material
Exterior1st: Exterior covering on house
Exterior2nd: Exterior covering on house (if more than one material)
MasVnrType: Masonry veneer type
MasVnrArea: Masonry veneer area in square feet
ExterQual: Exterior material quality
ExterCond: Present condition of the material on the exterior
Foundation: Type of foundation
BsmtQual: Height of the basement
BsmtCond: General condition of the basement
BsmtExposure: Walkout or garden level basement walls
BsmtFinType1: Quality of basement finished area
BsmtFinSF1: Type 1 finished square feet
BsmtFinType2: Quality of second finished area (if present)
BsmtFinSF2: Type 2 finished square feet
BsmtUnfSF: Unfinished square feet of basement area
TotalBsmtSF: Total square feet of basement area
Heating: Type of heating
HeatingQC: Heating quality and condition
CentralAir: Central air conditioning
Electrical: Electrical system
1stFlrSF: First Floor square feet
2ndFlrSF: Second floor square feet
LowQualFinSF: Low quality finished square feet (all floors)
GrLivArea: Above grade (ground) living area square feet
BsmtFullBath: Basement full bathrooms
BsmtHalfBath: Basement half bathrooms
FullBath: Full bathrooms above grade
HalfBath: Half baths above grade
Bedroom: Number of bedrooms above basement level
Kitchen: Number of kitchens
KitchenQual: Kitchen quality
TotRmsAbvGrd: Total rooms above grade (does not include bathrooms)
Functional: Home functionality rating
Fireplaces: Number of fireplaces
FireplaceQu: Fireplace quality
GarageType: Garage location
GarageYrBlt: Year garage was built
GarageFinish: Interior finish of the garage
GarageCars: Size of garage in car capacity
GarageArea: Size of garage in square feet
GarageQual: Garage quality
GarageCond: Garage condition
PavedDrive: Paved driveway
WoodDeckSF: Wood deck area in square feet
OpenPorchSF: Open porch area in square feet
EnclosedPorch: Enclosed porch area in square feet
3SsnPorch: Three season porch area in square feet
ScreenPorch: Screen porch area in square feet
PoolArea: Pool area in square feet
PoolQC: Pool quality
Fence: Fence quality
MiscFeature: Miscellaneous feature not covered in other categories
MiscVal: $Value of miscellaneous feature
MoSold: Month Sold
YrSold: Year Sold
SaleType: Type of sale
SaleCondition: Condition of sale
首先看这个特征 GrLivArea: Above grade (ground) living area square feet,是指居住面积平方英尺

去除异常值

import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
sns.set(style='white', context='notebook', palette='deep')

plt.subplots(figsize=(15,8))
plt.subplot(1,2,1)
g= sns.regplot(x=train['GrLivArea'],y= train['SalePrice'],fit_reg=False).set_title('Before')
plt.subplot(1,2,2)
train= train.drop(train[train['GrLivArea']>4000].index)
g=sns.regplot(x=train['GrLivArea'],y=train['SalePrice'],fit_reg=False).set_title('After')

png

从以上图中可以发现，居住面积大于4000的样本总共有4个，且这个四个属于严重的偏离分布

处理缺失值

缺失值可能是由于人工输入错误，机器误差等问题导致的
有些例子中的缺失值可以使用0进行填充，前提是需要知道该特征代表的意义，缺失即代表0
实际情况中，填充0并不总是最好的办法，而且针对不同的算法，对于缺失值处理的能力不同，本文需要使用多种算法进行拟合房价，因此如何正确处理缺失值呢，一般有两种方法：
- 直接删掉带有缺失值的列
- 填充缺失值

# 首先先把训练数据与测试数据的长度保持，以备后用
ntrain = train.shape[0]
ntest = test.shape[0]

# 保持训练集的目标值数据即 SalePrice
y_train = train.SalePrice.values
all_data = pd.concat((train,test)).reset_index(drop=True)
all_data.drop(['SalePrice'],axis=1,inplace=True)
all_data.drop(['Id'],axis=1,inplace=True)
print('all data shape:{}'.format(all_data.shape))

all data shape:(2915, 79)


/Users/aihuishou/anaconda3/envs/work/lib/python3.6/site-packages/ipykernel_launcher.py:7: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version
of pandas will change to not sort by default.

To accept the future behavior, pass 'sort=False'.

To retain the current behavior and silence the warning, pass 'sort=True'.

  import sys

all_data_na = all_data.isnull().sum()

all_data_na.sort_values(ascending=False)

PoolQC           2907
MiscFeature      2810
Alley            2717
Fence            2345
FireplaceQu      1420
LotFrontage       486
GarageFinish      159
GarageQual        159
GarageYrBlt       159
GarageCond        159
GarageType        157
BsmtCond           82
BsmtExposure       82
BsmtQual           81
BsmtFinType2       80
BsmtFinType1       79
MasVnrType         24
MasVnrArea         23
MSZoning            4
BsmtHalfBath        2
Utilities           2
Functional          2
BsmtFullBath        2
Electrical          1
Exterior2nd         1
KitchenQual         1
GarageCars          1
Exterior1st         1
GarageArea          1
TotalBsmtSF         1
                 ... 
GrLivArea           0
YearRemodAdd        0
YearBuilt           0
WoodDeckSF          0
TotRmsAbvGrd        0
Street              0
ScreenPorch         0
SaleCondition       0
RoofStyle           0
RoofMatl            0
PoolArea            0
PavedDrive          0
OverallQual         0
OverallCond         0
OpenPorchSF         0
Neighborhood        0
MoSold              0
MiscVal             0
MSSubClass          0
LowQualFinSF        0
LotShape            0
LotConfig           0
LotArea             0
LandSlope           0
LandContour         0
KitchenAbvGr        0
HouseStyle          0
HeatingQC           0
Heating             0
1stFlrSF            0
Length: 79, dtype: int64

all_data_na = all_data_na.drop(all_data_na[all_data_na==0].index).sort_values(ascending=False)

plt.subplots(figsize=(12,6))
all_data_na.plot(kind='Bar')

<matplotlib.axes._subplots.AxesSubplot at 0x128568710>

png

参考链接

!pip install xgboost

Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple
Requirement already satisfied: xgboost in /Users/aihuishou/anaconda3/envs/work/lib/python3.6/site-packages (0.90)
Requirement already satisfied: numpy in /Users/aihuishou/anaconda3/envs/work/lib/python3.6/site-packages (from xgboost) (1.16.2)
Requirement already satisfied: scipy in /Users/aihuishou/anaconda3/envs/work/lib/python3.6/site-packages (from xgboost) (1.2.1)

train[all_data_na.index[:25]].info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 1456 entries, 0 to 1459
Data columns (total 25 columns):
PoolQC          5 non-null object
MiscFeature     54 non-null object
Alley           91 non-null object
Fence           280 non-null object
FireplaceQu     766 non-null object
LotFrontage     1197 non-null float64
GarageQual      1375 non-null object
GarageCond      1375 non-null object
GarageFinish    1375 non-null object
GarageYrBlt     1375 non-null float64
GarageType      1375 non-null object
BsmtExposure    1418 non-null object
BsmtCond        1419 non-null object
BsmtQual        1419 non-null object
BsmtFinType2    1418 non-null object
BsmtFinType1    1419 non-null object
MasVnrType      1448 non-null object
MasVnrArea      1448 non-null float64
MSZoning        1456 non-null object
BsmtFullBath    1456 non-null int64
BsmtHalfBath    1456 non-null int64
Utilities       1456 non-null object
Functional      1456 non-null object
Electrical      1455 non-null object
BsmtUnfSF       1456 non-null int64
dtypes: float64(3), int64(3), object(19)
memory usage: 295.8+ KB

for category feature we,fill these missing values with "None"
for float feature and the number of missing values seemingly much larger ,we fill these missing values with median of the feature
for float feature and the number of missing values smaller, we will fill these missing values with mode

for col in ("PoolQC", 'MiscFeature', 'Alley', 'Fence', 'FireplaceQu', 'GarageQual', 'GarageCond',
            'GarageFinish', 'GarageType','BsmtExposure','BsmtCond','BsmtQual','BsmtFinType2','BsmtFinType1',
           'MasVnrType'):
    all_data[col] = all_data[col].fillna('None')

    
print('处理object类型缺失值，根据特征的描述，特征缺失值补充为"None"，已完成')
    
for col in ("GarageYrBlt", "GarageArea", "GarageCars", "BsmtFinSF1", 
           "BsmtFinSF2", "BsmtUnfSF", "TotalBsmtSF", "MasVnrArea",
           "BsmtFullBath", "BsmtHalfBath"):
    all_data[col] = all_data[col].fillna(0)

print('处理数值类型的缺失值，根据特征的描述，选择特征缺失值补充为0，已完成')


all_data['MSZoning'] = all_data['MSZoning'].fillna(all_data['MSZoning'].mode()[0])
all_data['Electrical'] = all_data['Electrical'].fillna(all_data['Electrical'].mode()[0])
all_data['KitchenQual'] = all_data['KitchenQual'].fillna(all_data['KitchenQual'].mode()[0])
all_data['Exterior1st'] = all_data['Exterior1st'].fillna(all_data['Exterior1st'].mode()[0])
all_data['Exterior2nd'] = all_data['Exterior2nd'].fillna(all_data['Exterior2nd'].mode()[0])
all_data['SaleType'] = all_data['SaleType'].fillna(all_data['SaleType'].mode()[0])
all_data["Functional"] = all_data["Functional"].fillna(all_data['Functional'].mode()[0])

print('处理缺失值较少的缺失值，数据类型为数值，填充缺失值为该特征的众数，已完成')

all_data_na = all_data.isnull().sum()
print("Features with missing values: ", all_data_na.drop(all_data_na[all_data_na == 0].index))

处理object类型缺失值，根据特征的描述，特征缺失值补充为"None"，已完成
处理数值类型的缺失值，根据特征的描述，选择特征缺失值补充为0，已完成
处理缺失值较少的缺失值，数据类型为数值，填充缺失值为该特征的众数，已完成
Features with missing values:  LotFrontage    486
Utilities        2
dtype: int64

all_data.groupby(["Neighborhood"])['LotFrontage'].sum()

Neighborhood
Blmngtn      938.0
Blueste      273.0
BrDale       645.0
BrkSide     5300.0
ClearCr     1763.0
CollgCr    15694.0
Crawfor     5806.0
Edwards    11467.0
Gilbert     8237.0
IDOTRR      5415.0
MeadowV      845.0
Mitchel     6763.0
NAmes      28204.0
NPkVill      591.0
NWAmes      6929.0
NoRidge     4684.0
NridgHt    13722.0
OldTown    14147.0
SWISU       2599.0
Sawyer      7306.0
SawyerW     7491.0
Somerst    10457.0
StoneBr     2860.0
Timber      4626.0
Veenker     1152.0
Name: LotFrontage, dtype: float64

all_data['LotFrontage']=all_data.groupby("Neighborhood")["LotFrontage"].transform(
    lambda x: x.fillna(x.median()))

分析 Utilities

plt.subplots(figsize=(12,5))
plt.subplot(1,2,1)
g=sns.countplot(x='Utilities',data=train).set_title('Utilities_train')
plt.subplot(1,2,2)
g=sns.countplot(x='Utilities',data=test).set_title('Utilities_test')

png

train['Utilities'].value_counts()

AllPub    1455
NoSeWa       1
Name: Utilities, dtype: int64

test['Utilities'].value_counts()

AllPub    1457
Name: Utilities, dtype: int64

all_data = all_data.drop(['Utilities'], axis=1)

all_data_na = all_data.isnull().sum()
print("Features with missing values: ", len(all_data_na.drop(all_data_na[all_data_na == 0].index)))

Features with missing values:  0

Exploratory Data Analysis

Correlation matrix

异常值与缺失值已经处理完毕，进一步需要特征之间与特征与目标值之间的关系，相关系数矩阵就是提供了反应特征与目标值之间关系的一个参考

corr = train.corr()
plt.subplots(figsize=(30,30))
cmap = sns.diverging_palette(150, 250, as_cmap=True)
sns.heatmap(corr, cmap="RdYlBu", vmax=1, vmin=-0.6, center=0.2, square=True, linewidths=0, cbar_kws={"shrink": .5}, annot = True)

<matplotlib.axes._subplots.AxesSubplot at 0x12901bc18>

png

for raw highly influencing factors on SalePrice, we could do feature engineering
从相关系数矩阵中，我们挑选了一些跟最终售价相关性较高的做进一步的分析
主要的影响因素有以下几个:

OverallQual Overall material and finish quality 整体的物料以及完成质量
GrLivArea Above grade (ground) living area square feet 地面以上的居住面积平方英尺
GarageCars Size of garage in car capacity 停车场的大小，可以放几辆车
GarageArea Size of garage in square feet 停车场的面积大小
TotalBsmtSF Total square feet of basement area 地下室的面积平方英尺
1stFlrSF First Floor square feet 一楼的面积平方英尺
FullBath Full bathrooms above grade 地上卫生间
TotRmsAbvGrd Total rooms above grade (does not include bathrooms) 地上去掉卫生间的房屋数
Fireplaces 壁炉数量
MasVnrArea Masonry veneer area in square feet 粗略可以理解为石灰结构的建筑面积
BsmtFinSF1 Quality of basement finished area Type 1 finished square feet地下室的完成面积
LotFrontage Linear feet of street connected to property 距离街道的距离
WoodDeckSF Wood deck area in square feet 木质结构的建筑面积
OpenPorchSF Open porch area in square feet 开放式门廊的面积
2ndFlrSF Second floor square feet 二楼的面积

# Quadratic
all_data["OverallQual-2"] = all_data["OverallQual"] ** 2
all_data["GrLivArea-2"] = all_data["GrLivArea"] ** 2
all_data["GarageCars-2"] = all_data["GarageCars"] ** 2
all_data["GarageArea-2"] = all_data["GarageArea"] ** 2
all_data["TotalBsmtSF-2"] = all_data["TotalBsmtSF"] ** 2
all_data["1stFlrSF-2"] = all_data["1stFlrSF"] ** 2
all_data["FullBath-2"] = all_data["FullBath"] ** 2
all_data["TotRmsAbvGrd-2"] = all_data["TotRmsAbvGrd"] ** 2
all_data["Fireplaces-2"] = all_data["Fireplaces"] ** 2
all_data["MasVnrArea-2"] = all_data["MasVnrArea"] ** 2
all_data["BsmtFinSF1-2"] = all_data["BsmtFinSF1"] ** 2
all_data["LotFrontage-2"] = all_data["LotFrontage"] ** 2
all_data["WoodDeckSF-2"] = all_data["WoodDeckSF"] ** 2
all_data["OpenPorchSF-2"] = all_data["OpenPorchSF"] ** 2
all_data["2ndFlrSF-2"] = all_data["2ndFlrSF"] ** 2
print("Quadratics done!...")


# Cubic
all_data["OverallQual-23"] = all_data["OverallQual"] ** 3
all_data["GrLivArea-3"] = all_data["GrLivArea"] ** 3
all_data["GarageCars-3"] = all_data["GarageCars"] **3
all_data["GarageArea-3"] = all_data["GarageArea"] ** 3
all_data["TotalBsmtSF-3"] = all_data["TotalBsmtSF"] ** 3
all_data["1stFlrSF-3"] = all_data["1stFlrSF"] ** 3
all_data["FullBath-3"] = all_data["FullBath"] ** 3
all_data["TotRmsAbvGrd-3"] = all_data["TotRmsAbvGrd"] ** 3
all_data["Fireplaces-3"] = all_data["Fireplaces"] ** 3
all_data["MasVnrArea-3"] = all_data["MasVnrArea"] ** 3
all_data["BsmtFinSF1-3"] = all_data["BsmtFinSF1"] ** 3
all_data["LotFrontage-3"] = all_data["LotFrontage"] ** 3
all_data["WoodDeckSF-3"] = all_data["WoodDeckSF"] ** 3
all_data["OpenPorchSF-3"]=all_data["OpenPorchSF"] ** 3
all_data["2ndFlrSF-3"]= all_data["2ndFlrSF"] ** 3
print("Quadratics done!...")



# Square Root
all_data["OverallQual-Sq"] = np.sqrt(all_data["OverallQual"])
all_data["GrLivArea-Sq"] = np.sqrt(all_data["GrLivArea"])
all_data["GarageCars-Sq"] = np.sqrt(all_data["GarageCars"])
all_data["GarageArea-Sq"] = np.sqrt(all_data["GarageArea"])
all_data["TotalBsmtSF-Sq"] = np.sqrt(all_data["TotalBsmtSF"])
all_data["1stFlrSF-Sq"] = np.sqrt(all_data["1stFlrSF"])
all_data["FullBath-Sq"] = np.sqrt(all_data["FullBath"])
all_data["TotRmsAbvGrd-Sq"] = np.sqrt(all_data["TotRmsAbvGrd"])
all_data["Fireplaces-Sq"] = np.sqrt(all_data["Fireplaces"])
all_data["MasVnrArea-Sq"] = np.sqrt(all_data["MasVnrArea"])
all_data["BsmtFinSF1-Sq"] = np.sqrt(all_data["BsmtFinSF1"])
all_data["LotFrontage-Sq"] = np.sqrt(all_data["LotFrontage"])
all_data["WoodDeckSF-Sq"] = np.sqrt(all_data["WoodDeckSF"])
all_data["OpenPorchSF-Sq"] = np.sqrt(all_data["OpenPorchSF"])
all_data["2ndFlrSF-Sq"] = np.sqrt(all_data["2ndFlrSF"])
print("Roots done!...")

Quadratics done!...
Quadratics done!...
Roots done!...

BsmtQual

train['BsmtQual'].value_counts()

TA    649
Gd    618
Ex    117
Fa     35
Name: BsmtQual, dtype: int64

train.groupby(['BsmtQual'])['SalePrice'].mean()
"""
BsmtQual: Evaluates the height of the basement

       Ex	Excellent (100+ inches)	
       Gd	Good (90-99 inches)
       TA	Typical (80-89 inches)
       Fa	Fair (70-79 inches)
       Po	Poor (<70 inches
       NA	No Basement
"""

'\nBsmtQual: Evaluates the height of the basement\n\n       Ex\tExcellent (100+ inches)\t\n       Gd\tGood (90-99 inches)\n       TA\tTypical (80-89 inches)\n       Fa\tFair (70-79 inches)\n       Po\tPoor (<70 inches\n       NA\tNo Basement\n'

plt.subplots(figsize=(20,6))
plt.subplot(1,3,1)# 箱形图
sns.boxplot(x='BsmtQual',y='SalePrice',data=train,order= ['Fa', 'TA', 'Gd', 'Ex'])


plt.subplot(1,3,2) # x轴里的类别进行分类
sns.stripplot(x='BsmtQual',y='SalePrice',data=train,size=5,jitter=True,order= ['Fa', 'TA', 'Gd', 'Ex'])


plt.subplot(1,3,3) # 柱状图
sns.barplot(x='BsmtQual',y='SalePrice',data=train,order= ['Fa', 'TA', 'Gd', 'Ex'],estimator=np.mean)

<matplotlib.axes._subplots.AxesSubplot at 0x1263d5e10>

png

all_data['BsmtQual'] = all_data['BsmtQual'].map({"None":0, "Fa":1, "TA":2, "Gd":3, "Ex":4})
all_data['BsmtQual'].unique()

array([3, 2, 4, 0, 1])

all_data['BsmtQual'].value_counts()

2    1283
3    1209
4     254
1      88
0      81
Name: BsmtQual, dtype: int64

很明显，该特征能够显著的影响销售价格，而且越高的的地下室，对应的价格也越高
typical and good 两个分部数量较大，占比较高
可以将该特征的变量是有高低好坏之分的，也就是category 特征的顺序性，可以转化为数字(个人觉得意义不大）

BsmtCond

"""
BsmtCond: Evaluates the general condition of the basement

       Ex	Excellent
       Gd	Good
       TA	Typical - slight dampness allowed
       Fa	Fair - dampness or some cracking or settling
       Po	Poor - Severe cracking, settling, or wetness
       NA	No Basement
"""

'\nBsmtCond: Evaluates the general condition of the basement\n\n       Ex\tExcellent\n       Gd\tGood\n       TA\tTypical - slight dampness allowed\n       Fa\tFair - dampness or some cracking or settling\n       Po\tPoor - Severe cracking, settling, or wetness\n       NA\tNo Basement\n'

plt.subplots(figsize=(20,5))
plt.subplot(1,3,1)
sns.boxplot(x='BsmtCond',y='SalePrice',data=train,order=['Po','Fa','TA','Gd'])
plt.subplot(1,3,2)


sns.stripplot(x='BsmtCond',y='SalePrice',data=train,size=5,jitter=True,order= ['Po','Fa','TA','Gd'])


plt.subplot(1,3,3)


sns.barplot(x='BsmtCond',y='SalePrice',data=train,order=['Po','Fa','TA','Gd'])

<matplotlib.axes._subplots.AxesSubplot at 0x12ab8d6d8>

png

train['BsmtCond'].value_counts()

TA    1307
Gd      65
Fa      45
Po       2
Name: BsmtCond, dtype: int64

图二中的Typical样本数据占比较高，从barplot中可以看出该特征能够很明显的影响售出价格
针对图一种的TA价格较为分散，价格分布离散

all_data['BsmtCond'] = all_data['BsmtCond'].map({"None":0, "Po":1, "Fa":2, "TA":3,"Gd":4, "Ex":5})
all_data['BsmtCond'].unique()

array([3, 4, 0, 2, 1])

BsmtExplosure

"""
BsmtExposure: Refers to walkout or garden level walls

       Gd	Good Exposure
       Av	Average Exposure (split levels or foyers typically score average or above)	
       Mn	Mimimum Exposure
       No	No Exposure
       NA	No Basement

"""

'\nBsmtExposure: Refers to walkout or garden level walls\n\n       Gd\tGood Exposure\n       Av\tAverage Exposure (split levels or foyers typically score average or above)\t\n       Mn\tMimimum Exposure\n       No\tNo Exposure\n       NA\tNo Basement\n\n'

plt.subplots(figsize=(20,5))
plt.subplot(1,3,1)
sns.boxplot(x='BsmtExposure',y='SalePrice',data=train,order=['No','Mn','Av','Gd'])
plt.subplot(1,3,2)
sns.stripplot(x='BsmtExposure',y='SalePrice',data=train,size=5,jitter=True,order= ['No','Mn','Av','Gd'])
plt.subplot(1,3,3)
sns.barplot(x='BsmtExposure',y='SalePrice',data=train,order=['No','Mn','Av','Gd'])

<matplotlib.axes._subplots.AxesSubplot at 0x12b8e4470>

png

all_data['BsmtExposure'] = all_data['BsmtExposure'].map({"None":0, "No":1, "Mn":2, "Av":3,"Gd":4})
all_data['BsmtExposure'].unique()

array([1, 4, 2, 3, 0])

BsmtFinType1

"""
BsmtFinType1: Rating of basement finished area

       GLQ	Good Living Quarters
       ALQ	Average Living Quarters
       BLQ	Below Average Living Quarters	
       Rec	Average Rec Room
       LwQ	Low Quality
       Unf	Unfinshed
       NA	No Basement
"""

'\nBsmtFinType1: Rating of basement finished area\n\n       GLQ\tGood Living Quarters\n       ALQ\tAverage Living Quarters\n       BLQ\tBelow Average Living Quarters\t\n       Rec\tAverage Rec Room\n       LwQ\tLow Quality\n       Unf\tUnfinshed\n       NA\tNo Basement\n'

plt.subplots(figsize =(20, 5))

plt.subplot(1, 3, 1)
sns.boxplot(x="BsmtFinType1", y="SalePrice", data=train, order=["Unf", "LwQ", "Rec", "BLQ", "ALQ", "GLQ"]);

plt.subplot(1, 3, 2)
sns.stripplot(x="BsmtFinType1", y="SalePrice", data=train, size = 5, jitter = True, order=["Unf", "LwQ", "Rec", "BLQ", "ALQ", "GLQ"]);

plt.subplot(1, 3, 3)
sns.barplot(x="BsmtFinType1", y="SalePrice", data=train, order=["Unf", "LwQ", "Rec", "BLQ", "ALQ", "GLQ"]);

png

可以从图一中看出，很多没有装修完的地下室房屋的价格很高
从图三中可以看到，这些category 不是按照顺序的提高，房屋的销售价提高与category的顺序没有必然关系
因此将这个特征进行one-hot转化，可以使用pandas 中的get_dummy函数进行转化

all_data = pd.get_dummies(all_data, columns = ["BsmtFinType1"], prefix="BsmtFinType1")
all_data.head(3)

	1stFlrSF	2ndFlrSF	Alley	BedroomAbvGr	BldgType	BsmtCond	BsmtExposure	BsmtFinSF1	...	WoodDeckSF-Sq	OpenPorchSF-Sq	2ndFlrSF-Sq	BsmtFinType1_ALQ	BsmtFinType1_GLQ
0	856	854	None	3	1Fam	3	1	706.0	...	0.000000	7.810250	29.223278	0	1
1	1262	0	None	3	1Fam	3	4	978.0	...	17.262677	0.000000	0.000000	1	0
2	920	866	None	3	1Fam	3	2	486.0	...	0.000000	6.480741	29.427878	0	1

3 rows × 129 columns

BsmtFinSF1

BsmtFinSF1: Type 1 finished square feet

from scipy.stats.stats import pearsonr
grid = plt.GridSpec(2,3,wspace=0.15,hspace=0.25) 
# 创建画布指定子图将放置的网格的几何位置。 需要设置网格的行数和列数。 子图布局参数（例如，左，右等）可以选择性调整。
plt.subplots(figsize=(30,15))
plt.subplot(grid[0,0])


g = sns.regplot(x=train['BsmtFinSF1'], y=train['SalePrice'], fit_reg=False, label = "corr: %2f"%(pearsonr(train['BsmtFinSF1'], train['SalePrice'])[0]))
# g= sns.regplot(x=train['BsmtFinSF1'],y=train["SalePrice"],fit_reg==False,label= "Corr:%2f" %(pearsonr(train['BsmtFinType1'],train['SalePrice'])[0]))
g.legend(loc='best')

plt.subplot(grid[0,1:])

sns.boxplot(x='Neighborhood',y='BsmtFinSF1',data=train)

plt.subplot(grid[1,0])
sns.barplot(x='BldgType',y= 'BsmtFinSF1',data=train)


plt.subplot(grid[1,1])

sns.barplot(x='HouseStyle',y ='BsmtFinSF1',data=train)

plt.subplot(grid[1,2])


sns.barplot(x='LotShape',y='BsmtFinSF1',data=train)

<matplotlib.axes._subplots.AxesSubplot at 0x129034e10>

png

地下室完成面积对于销售价格来说影响很大，但是对于Neighborhood以及BldgType houseType LotShape 影响各异，这三个因素对于完成面积影响没有规律可循
但是特征是连续的数值特质，因此考虑将其进行切割分组

bins = [-5,1000,2000,3000,float('inf')]
all_data['BsmtFinSF1_Band'] = pd.cut(all_data['BsmtFinSF1'], bins,labels=['1','2','3','4'])

all_data['BsmtFinSF1_Band'].unique()
all_data.drop('BsmtFinSF1',axis=1,inplace=True)

all_data = pd.get_dummies(all_data, columns = ["BsmtFinSF1_Band"], prefix="BsmtFinSF1")
all_data.head()

	1stFlrSF	2ndFlrSF	Alley	BedroomAbvGr	BldgType	BsmtCond	BsmtExposure	BsmtFinType2	...	BsmtFinType1_GLQ	BsmtFinSF1_1
0	856	854	None	3	1Fam	3	1	Unf	...	1	1
1	1262	0	None	3	1Fam	3	4	Unf	...	0	1
2	920	866	None	3	1Fam	3	2	Unf	...	1	1
3	961	756	None	3	1Fam	4	1	Unf	...	0	1
4	1145	1053	None	4	1Fam	3	3	Unf	...	1	1

5 rows × 132 columns

BsmtFinType2

"""
BsmtFinType2: Rating of basement finished area (if multiple types)

       GLQ	Good Living Quarters
       ALQ	Average Living Quarters
       BLQ	Below Average Living Quarters	
       Rec	Average Rec Room
       LwQ	Low Quality
       Unf	Unfinshed
       NA	No Basement

"""

'\nBsmtFinType2: Rating of basement finished area (if multiple types)\n\n       GLQ\tGood Living Quarters\n       ALQ\tAverage Living Quarters\n       BLQ\tBelow Average Living Quarters\t\n       Rec\tAverage Rec Room\n       LwQ\tLow Quality\n       Unf\tUnfinshed\n       NA\tNo Basement\n\n'

plt.subplots(figsize =(20, 5))

plt.subplot(1, 3, 1)
sns.boxplot(x="BsmtFinType2", y="SalePrice", data=train, order=["Unf", "LwQ", "Rec", "BLQ", "ALQ", "GLQ"]);

plt.subplot(1, 3, 2)
sns.stripplot(x="BsmtFinType2", y="SalePrice", data=train, size = 5, jitter = True, order=["Unf", "LwQ", "Rec", "BLQ", "ALQ", "GLQ"]);

plt.subplot(1, 3, 3)
sns.barplot(x="BsmtFinType2", y="SalePrice", data=train, order=["Unf", "LwQ", "Rec", "BLQ", "ALQ", "GLQ"]);

png

很多房子的第二个地下室没有装修完工，且价格分化很大
第二个装修的地下室的装修好坏对于价格影响没有像之前的那样的顺序关系(图三)
因此，需要将该特征转化为one-hot哑变量

all_data = pd.get_dummies(all_data, columns = ["BsmtFinType2"], prefix="BsmtFinType2")  # columns 参数要传入列表

all_data.head(3)
"""
columns : list-like, default None
Column names in the DataFrame to be encoded. If columns is None then all the columns with object or category dtype will be converted.

"""

'\ncolumns : list-like, default None\nColumn names in the DataFrame to be encoded. If columns is None then all the columns with object or category dtype will be converted.\n\n'

BsmtFinSF2

"""
BsmtFinSF2: Type 2 finished square feet
"""
grid = plt.GridSpec(2,3,wspace=0.15,hspace=0.25) 
# 创建画布指定子图将放置的网格的几何位置。 需要设置网格的行数和列数。 子图布局参数（例如，左，右等）可以选择性调整。
plt.subplots(figsize=(30,15))
plt.subplot(grid[0,0])


g = sns.regplot(x=train['BsmtFinSF2'], y=train['SalePrice'], fit_reg=False, label = "corr: %2f"%(pearsonr(train['BsmtFinSF2'], train['SalePrice'])[0]))
# g= sns.regplot(x=train['BsmtFinSF1'],y=train["SalePrice"],fit_reg==False,label= "Corr:%2f" %(pearsonr(train['BsmtFinType1'],train['SalePrice'])[0]))
g.legend(loc='best')

plt.subplot(grid[0,1:])

sns.boxplot(x='Neighborhood',y='BsmtFinSF2',data=train)

plt.subplot(grid[1,0])
sns.barplot(x='BldgType',y= 'BsmtFinSF2',data=train)


plt.subplot(grid[1,1])

sns.barplot(x='HouseStyle',y ='BsmtFinSF2',data=train)

plt.subplot(grid[1,2])


sns.barplot(x='LotShape',y='BsmtFinSF2',data=train)

<matplotlib.axes._subplots.AxesSubplot at 0x12c7a68d0>

png

已装修完成的第二个地下室的面积与销售价格没有明显的关系
而且大部分的数据都是未完成装修的，与上一个特征相关性较高
可以采用是否完成装修来转化该特征（类似于缺失值的补充，变成是否缺失）

all_data['BsmtFinType2_None'].value_counts()

0    2835
1      80
Name: BsmtFinType2_None, dtype: int64

all_data['BsmtFinSf2_Flag'] = all_data['BsmtFinSF2'].map(lambda x:0 if x==0 else 1)
all_data.drop('BsmtFinSF2', axis=1, inplace=True)

all_data['BsmtFinSf2_Flag'].value_counts()

0    2568
1     347
Name: BsmtFinSf2_Flag, dtype: int64

BsmtUnfSF

"""
Unfinished square feet of basement area

"""
grid = plt.GridSpec(2,3,wspace=0.15,hspace=0.25) 
# 创建画布指定子图将放置的网格的几何位置。 需要设置网格的行数和列数。 子图布局参数（例如，左，右等）可以选择性调整。
plt.subplots(figsize=(30,15))
plt.subplot(grid[0,0])


g = sns.regplot(x=train['BsmtUnfSF'], y=train['SalePrice'], fit_reg=False, label = "corr: %2f"%(pearsonr(train['BsmtUnfSF'], train['SalePrice'])[0]))
# g= sns.regplot(x=train['BsmtFinSF1'],y=train["SalePrice"],fit_reg==False,label= "Corr:%2f" %(pearsonr(train['BsmtFinType1'],train['SalePrice'])[0]))
g.legend(loc='best')

plt.subplot(grid[0,1:])

sns.boxplot(x='Neighborhood',y='BsmtUnfSF',data=train)

plt.subplot(grid[1,0])
sns.barplot(x='BldgType',y= 'BsmtUnfSF',data=train)


plt.subplot(grid[1,1])

sns.barplot(x='HouseStyle',y ='BsmtUnfSF',data=train)

plt.subplot(grid[1,2])


sns.barplot(x='LotShape',y='BsmtUnfSF',data=train)

<matplotlib.axes._subplots.AxesSubplot at 0x118d8b940>

png

"""
This feature has a significant positive correlation with SalePrice, with a small proportion of data points having a value of 0.
This tells me that most houses will have some amount of square feet unfinished within the basement, and this actually positively contributes towards SalePrice.
The amount of unfinished square feet also varies widely based on location and style.
Whereas the average unfinished square feet within the basement is fairly consistent across the different lot shapes.
Since this is a continuous numeric feature with a significant correlation, I will bin this and create dummy variables.
与售价正相关，
Unfinished square feet of basement area 与lot shape 没啥关系
连续值变量，需要进行封箱操作，然后将封箱之后的特征进行one-hot转化
"""
all_data['BsmtUnfSF_Band'] = pd.cut(all_data['BsmtUnfSF'], 3,labels=['1','2','3'])
all_data.drop('BsmtUnfSF',axis=1,inplace=True)
all_data['BsmtUnfSF_Band'].unique()
all_data['BsmtUnfSF_Band'] = all_data['BsmtUnfSF_Band'].astype(int)
all_data = pd.get_dummies(all_data, columns = ["BsmtUnfSF_Band"], prefix="BsmtUnfSF")
all_data.head()

	1stFlrSF	2ndFlrSF	Alley	BedroomAbvGr	BldgType	BsmtCond	BsmtExposure	BsmtFullBath	BsmtHalfBath	...	BsmtFinType2_Unf	BsmtUnfSF_1
0	856	854	None	3	1Fam	3	1	1.0	0.0	...	1	1
1	1262	0	None	3	1Fam	3	4	0.0	1.0	...	1	1
2	920	866	None	3	1Fam	3	2	1.0	0.0	...	1	1
3	961	756	None	3	1Fam	4	1	1.0	0.0	...	1	1
4	1145	1053	None	4	1Fam	3	3	1.0	0.0	...	1	1

5 rows × 140 columns

TotalBsmtSF

"""
Total square feet of basement area.
"""
grid = plt.GridSpec(2,3,wspace=0.15,hspace=0.25) 
# 创建画布指定子图将放置的网格的几何位置。 需要设置网格的行数和列数。 子图布局参数（例如，左，右等）可以选择性调整。
plt.subplots(figsize=(30,15))
plt.subplot(grid[0,0])


g = sns.regplot(x=train['TotalBsmtSF'], y=train['SalePrice'], fit_reg=False, label = "corr: %2f"%(pearsonr(train['TotalBsmtSF'], train['SalePrice'])[0]))
# g= sns.regplot(x=train['BsmtFinSF1'],y=train["SalePrice"],fit_reg==False,label= "Corr:%2f" %(pearsonr(train['BsmtFinType1'],train['SalePrice'])[0]))
g.legend(loc='best')

plt.subplot(grid[0,1:])

sns.boxplot(x='Neighborhood',y='TotalBsmtSF',data=train)

plt.subplot(grid[1,0])
sns.barplot(x='BldgType',y= 'TotalBsmtSF',data=train)


plt.subplot(grid[1,1])

sns.barplot(x='HouseStyle',y ='TotalBsmtSF',data=train)

plt.subplot(grid[1,2])


sns.barplot(x='LotShape',y='TotalBsmtSF',data=train)

<matplotlib.axes._subplots.AxesSubplot at 0x12d9b3d30>

png

def get_feature_corr(feature_name):
    grid = plt.GridSpec(2,3,wspace=0.15,hspace=0.25) 
# 创建画布指定子图将放置的网格的几何位置。 需要设置网格的行数和列数。 子图布局参数（例如，左，右等）可以选择性调整。
    plt.subplots(figsize=(30,15))
    plt.subplot(grid[0,0])


    g = sns.regplot(x=train[feature_name], y=train['SalePrice'], fit_reg=False, label = "corr: %2f"%(pearsonr(train[feature_name], train['SalePrice'])[0]))
    # g= sns.regplot(x=train['BsmtFinSF1'],y=train["SalePrice"],fit_reg==False,label= "Corr:%2f" %(pearsonr(train['BsmtFinType1'],train['SalePrice'])[0]))
    g.legend(loc='best')

    plt.subplot(grid[0,1:])

    sns.boxplot(x='Neighborhood',y=feature_name,data=train)

    plt.subplot(grid[1,0])
    sns.barplot(x='BldgType',y= feature_name,data=train)


    plt.subplot(grid[1,1])

    sns.barplot(x='HouseStyle',y =feature_name,data=train)

    plt.subplot(grid[1,2])


    sns.barplot(x='LotShape',y=feature_name,data=train)
    plt.show()

1stFlrSF

get_feature_corr('1stFlrSF')
"""
First floor square feet.
"""

png

'\nFirst floor square feet.\n'

第一层的面积与售价有着很强的相关性
不同的街区对于第一层的面积分布范围变化很大
对于不同的房型，第一层的面积变化不大
该特征为连续值，需要进行封箱然后one-hot转化

all_data['1stFlrSF_Band'] = pd.cut(all_data['1stFlrSF'], 6,labels=['1','2','3','4','5','6'])
all_data['1stFlrSF_Band'].unique()
all_data['1stFlrSF_Band'] = all_data['1stFlrSF_Band'].astype(int)

all_data.drop('1stFlrSF', axis=1, inplace=True)
all_data = pd.get_dummies(all_data, columns = ["1stFlrSF_Band"], prefix="1stFlrSF")
all_data.head(3)

	2ndFlrSF	Alley	BedroomAbvGr	BldgType	BsmtCond	BsmtExposure	BsmtFullBath	BsmtHalfBath	BsmtQual	...	BsmtUnfSF_1	1stFlrSF_1	1stFlrSF_2
0	854	None	3	1Fam	3	1	1.0	0.0	3	...	1	1	0
1	0	None	3	1Fam	3	4	0.0	1.0	3	...	1	0	1
2	866	None	3	1Fam	3	2	1.0	0.0	3	...	1	1	0

3 rows × 145 columns

2ndFlrSF

get_feature_corr('2ndFlrSF')
"""
Second floor square feet.
"""

png

'\nSecond floor square feet.\n'

很多房子没有第二层，所有很多房子的第二层面积为0
第二层面积与街区的变化很大
对于不同的房型，第二层的面积变化很大
连续值变量，进行封箱，然后进行one-hot转化

all_data['2ndFlrSF_Band'] = pd.cut(all_data['2ndFlrSF'], 6,labels=list('123456'))
all_data['2ndFlrSF_Band'].unique()
all_data=pd.get_dummies(all_data,columns=['2ndFlrSF_Band'],prefix="2ndFlrSF")
all_data.drop('2ndFlrSF', axis=1, inplace=True)
all_data.head()

	Alley	BedroomAbvGr	BldgType	BsmtCond	BsmtExposure	BsmtFullBath	BsmtHalfBath	BsmtQual	CentralAir	...	2ndFlrSF_1	2ndFlrSF_3	2ndFlrSF_4
0	None	3	1Fam	3	1	1.0	0.0	3	Y	...	0	1	0
1	None	3	1Fam	3	4	0.0	1.0	3	Y	...	1	0	0
2	None	3	1Fam	3	2	1.0	0.0	3	Y	...	0	1	0
3	None	3	1Fam	4	1	1.0	0.0	2	Y	...	0	1	0
4	None	4	1Fam	3	3	1.0	0.0	3	Y	...	0	0	1

5 rows × 150 columns

LowQualFinSF

get_feature_corr('LowQualFinSF')

'''
Low quality finished square feet (all floors)
'''

png

'\nLow quality finished square feet (all floors)\n'

针对该特征可以将特征转化为0-1

all_data['LowQualFinSF_Flag'] = all_data['LowQualFinSF'].map(lambda x:0 if x==0 else 1)
all_data.drop('LowQualFinSF', axis=1, inplace=True)

BsmtHalfBath BsmtFullBath HalfBath FullBath

all_data['TotalBathrooms'] = all_data['BsmtHalfBath'] + all_data['BsmtFullBath'] + all_data['HalfBath'] + all_data['FullBath']

columns = ['BsmtHalfBath', 'BsmtFullBath', 'HalfBath', 'FullBath']
all_data.drop(columns, axis=1, inplace=True)

def get_feature_corr1(feature_name,order=None):
    plt.subplots(figsize =(20, 5))

    plt.subplot(1, 3, 1)
    sns.boxplot(x=feature_name, y="SalePrice", data=train,order=order)

    plt.subplot(1, 3, 2)
    sns.stripplot(x=feature_name, y="SalePrice", data=train, size = 5, jitter = True ,order=order);

    plt.subplot(1, 3, 3)
    sns.barplot(x=feature_name, y="SalePrice", data=train,order=order)
    plt.show()

get_feature_corr1('BedroomAbvGr',order=None)
"""
Bedrooms above grade (does not include basement bedrooms)
"""

png

'\nBedrooms above grade (does not include basement bedrooms)\n'

get_feature_corr1('KitchenAbvGr',order=None)

png

get_feature_corr1('KitchenQual',order=['Fa','TA','Gd','Ex'])
print("""
该特征需要转化category with order
""")

png

该特征需要转化category with order

all_data['KitchenQual'] = all_data['KitchenQual'].map({"Fa":1, "TA":2, "Gd":3, "Ex":4})
all_data['KitchenQual'].unique()

array([3, 2, 4, 1])

TotRmsAbvGrd

get_feature_corr1('TotRmsAbvGrd')

png

Fireplaces

get_feature_corr1('Fireplaces')

png

FireplaceQu

get_feature_corr1('FireplaceQu',order=['Po','Fa','TA','Gd','Ex'])

png

all_data['FireplaceQu'] = all_data['FireplaceQu'].map({"None":0, "Po":1, "Fa":2, "TA":3, "Gd":4, "Ex":5})
all_data['FireplaceQu'].unique()

array([0, 3, 4, 2, 5, 1])

GrLivArea

get_feature_corr('GrLivArea')

png

特征为连续值，且与售价相关性非常强
封箱然后转化为one-hot特征

all_data['GrLivArea_Band'] = pd.cut(all_data['GrLivArea'], 6,labels=list('123456'))
all_data['GrLivArea_Band'].unique()
all_data['GrLivArea_Band'] = all_data['GrLivArea_Band'].astype(int)
all_data.drop('GrLivArea',axis=1,inplace=True)
all_data = pd.get_dummies(all_data, columns = ["GrLivArea_Band"], prefix="GrLivArea")
all_data.head(3)

	Alley	BedroomAbvGr	BldgType	BsmtCond	BsmtExposure	BsmtQual	CentralAir	Condition1	Condition2	...	TotalBathrooms	GrLivArea_2
0	None	3	1Fam	3	1	3	Y	Norm	Norm	...	4.0	1
1	None	3	1Fam	3	4	3	Y	Feedr	Norm	...	3.0	1
2	None	3	1Fam	3	2	3	Y	Norm	Norm	...	4.0	1

3 rows × 152 columns

MSSubClass

get_feature_corr1('MSSubClass')

png

all_data['MSSubClass'] = all_data['MSSubClass'].astype(str)

all_data = pd.get_dummies(all_data, columns = ["MSSubClass"], prefix="MSSubClass")
all_data.head(3)

	Alley	BedroomAbvGr	BldgType	BsmtCond	BsmtExposure	BsmtQual	CentralAir	Condition1	Condition2	...	MSSubClass_60
0	None	3	1Fam	3	1	3	Y	Norm	Norm	...	1
1	None	3	1Fam	3	4	3	Y	Feedr	Norm	...	0
2	None	3	1Fam	3	2	3	Y	Norm	Norm	...	1

3 rows × 167 columns

BldgType

get_feature_corr1('BldgType')

png

all_data['BldgType'] = all_data['BldgType'].astype(str)

all_data = pd.get_dummies(all_data, columns = ["BldgType"], prefix="BldgType")
all_data.head(3)

	Alley	BedroomAbvGr	BsmtCond	BsmtExposure	BsmtQual	CentralAir	Condition1	Condition2	Electrical	...	BldgType_1Fam
0	None	3	3	1	3	Y	Norm	Norm	SBrkr	...	1
1	None	3	3	4	3	Y	Feedr	Norm	SBrkr	...	1
2	None	3	3	2	3	Y	Norm	Norm	SBrkr	...	1

3 rows × 171 columns

HouseStyle

get_feature_corr1('HouseStyle')

png

all_data['HouseStyle'] = all_data['HouseStyle'].map({"2Story":"2Story", "1Story":"1Story", "1.5Fin":"1.5Story", "1.5Unf":"1.5Story", 
                                                     "SFoyer":"SFoyer", "SLvl":"SLvl", "2.5Unf":"2.5Story", "2.5Fin":"2.5Story"})

all_data = pd.get_dummies(all_data, columns = ["HouseStyle"], prefix="HouseStyle")
all_data.head(3)

	Alley	BedroomAbvGr	BsmtCond	BsmtExposure	BsmtQual	CentralAir	Condition1	Condition2	Electrical	...	HouseStyle_1Story	HouseStyle_2Story
0	None	3	3	1	3	Y	Norm	Norm	SBrkr	...	0	1
1	None	3	3	4	3	Y	Feedr	Norm	SBrkr	...	1	0
2	None	3	3	2	3	Y	Norm	Norm	SBrkr	...	0	1

3 rows × 176 columns

OverallQual

get_feature_corr1('OverallQual')

png

OverallCond

get_feature_corr1('OverallCond')

png

YearRemodAdd

get_feature_corr1('YearRemodAdd')

png

train['Remod_Diff'] = train['YearRemodAdd'] - train['YearBuilt']

plt.subplots(figsize =(40, 10))
sns.barplot(x="Remod_Diff", y="SalePrice", data=train);

png

all_data['Remod_Diff'] = all_data['YearRemodAdd'] - all_data['YearBuilt']

all_data.drop('YearRemodAdd', axis=1, inplace=True)

YearBuilt

get_feature_corr1('YearBuilt')

png

all_data['YearBuilt_Band'] = pd.cut(all_data['YearBuilt'], 7,labels=list('1234567'))
all_data['YearBuilt_Band'].unique()
all_data['YearBuilt_Band'] = all_data['YearBuilt_Band'].astype(int)
all_data.drop('YearBuilt',axis=1,inplace=True)
all_data = pd.get_dummies(all_data, columns = ["YearBuilt_Band"], prefix="YearBuilt")
all_data.head(3)

	Alley	BedroomAbvGr	BsmtCond	BsmtExposure	BsmtQual	CentralAir	Condition1	Condition2	Electrical	...	Remod_Diff	YearBuilt_6	YearBuilt_7
0	None	3	3	1	3	Y	Norm	Norm	SBrkr	...	0	0	1
1	None	3	3	4	3	Y	Feedr	Norm	SBrkr	...	0	1	0
2	None	3	3	2	3	Y	Norm	Norm	SBrkr	...	1	0	1

3 rows × 182 columns

Foundation

get_feature_corr1('Foundation')

png

all_data = pd.get_dummies(all_data, columns = ["Foundation"], prefix="Foundation")
all_data.head(3)

	Alley	BedroomAbvGr	BsmtCond	BsmtExposure	BsmtQual	CentralAir	Condition1	Condition2	Electrical	...	YearBuilt_6	YearBuilt_7	Foundation_CBlock	Foundation_PConc
0	None	3	3	1	3	Y	Norm	Norm	SBrkr	...	0	1	0	1
1	None	3	3	4	3	Y	Feedr	Norm	SBrkr	...	1	0	1	0
2	None	3	3	2	3	Y	Norm	Norm	SBrkr	...	0	1	0	1

3 rows × 187 columns

Functional

get_feature_corr1('Functional')

png

all_data['Functional'] = all_data['Functional'].map({"Sev":1, "Maj2":2, "Maj1":3, "Mod":4, "Min2":5, "Min1":6, "Typ":7})
all_data['Functional'].unique()

array([7, 6, 3, 5, 4, 2, 1])

RoofStyle

get_feature_corr1('RoofStyle')

png

all_data = pd.get_dummies(all_data, columns = ["RoofStyle"], prefix="RoofStyle")
all_data.head(3)

	Alley	BedroomAbvGr	BsmtCond	BsmtExposure	BsmtQual	CentralAir	Condition1	Condition2	Electrical	...	Foundation_PConc	RoofStyle_Gable
0	None	3	3	1	3	Y	Norm	Norm	SBrkr	...	1	1
1	None	3	3	4	3	Y	Feedr	Norm	SBrkr	...	0	1
2	None	3	3	2	3	Y	Norm	Norm	SBrkr	...	1	1

3 rows × 192 columns

RoofMatl

"""
Roof material.
"""

get_feature_corr1('RoofMatl')

png

all_data = pd.get_dummies(all_data, columns = ["RoofMatl"], prefix="RoofMatl")
all_data.head(3)

	Alley	BedroomAbvGr	BsmtCond	BsmtExposure	BsmtQual	CentralAir	Condition1	Condition2	Electrical	...	RoofMatl_CompShg
0	None	3	3	1	3	Y	Norm	Norm	SBrkr	...	1
1	None	3	3	4	3	Y	Feedr	Norm	SBrkr	...	1
2	None	3	3	2	3	Y	Norm	Norm	SBrkr	...	1

3 rows × 198 columns

Exterior1st & Exterior2nd

get_feature_corr1('Exterior1st')

png

get_feature_corr1('Exterior2nd')

png

def Exter2(col):
    if col['Exterior2nd'] == col['Exterior1st']:
        return 1
    else:
        return 0
    
all_data['ExteriorMatch_Flag'] = all_data.apply(Exter2, axis=1)
all_data.drop('Exterior2nd', axis=1, inplace=True)

all_data = pd.get_dummies(all_data, columns = ["Exterior1st"], prefix="Exterior1st")
all_data.head(3)

	Alley	BedroomAbvGr	BsmtCond	BsmtExposure	BsmtQual	CentralAir	Condition1	Condition2	Electrical	...	Exterior1st_MetalSd	Exterior1st_VinylSd
0	None	3	3	1	3	Y	Norm	Norm	SBrkr	...	0	1
1	None	3	3	4	3	Y	Feedr	Norm	SBrkr	...	1	0
2	None	3	3	2	3	Y	Norm	Norm	SBrkr	...	0	1

3 rows × 212 columns

MasVnrType

get_feature_corr1('MasVnrType')

png

all_data = pd.get_dummies(all_data, columns = ["MasVnrType"], prefix="MasVnrType")
all_data.head(3)

	Alley	BedroomAbvGr	BsmtCond	BsmtExposure	BsmtQual	CentralAir	Condition1	Condition2	Electrical	...	Exterior1st_VinylSd	MasVnrType_BrkFace	MasVnrType_None
0	None	3	3	1	3	Y	Norm	Norm	SBrkr	...	1	1	0
1	None	3	3	4	3	Y	Feedr	Norm	SBrkr	...	0	0	1
2	None	3	3	2	3	Y	Norm	Norm	SBrkr	...	1	1	0

3 rows × 215 columns

MasVnrArea

get_feature_corr('MasVnrArea')

png

这个特征没啥意义，各个维度与这个特征的相关性都不是很大，变化都很大，且没有规律

all_data.drop('MasVnrArea', axis=1, inplace=True)

ExterQual

get_feature_corr1('ExterQual',order=['Fa','TA','Gd', 'Ex'])

png

all_data['ExterQual'] = all_data['ExterQual'].map({"Fa":1, "TA":2, "Gd":3, "Ex":4})
all_data['ExterQual'].unique()

array([3, 2, 4, 1])

ExterCond

"""
Evaluates the present condition of the material on the exterior.
"""

'\nEvaluates the present condition of the material on the exterior.\n'

get_feature_corr1('ExterCond',order=['Po','Fa',"TA",'Gd','Ex'])

png

all_data = pd.get_dummies(all_data, columns = ["ExterCond"], prefix="ExterCond")
all_data.head(3)

	Alley	BedroomAbvGr	BsmtCond	BsmtExposure	BsmtQual	CentralAir	Condition1	Condition2	Electrical	...	MasVnrType_BrkFace	MasVnrType_None	ExterCond_TA
0	None	3	3	1	3	Y	Norm	Norm	SBrkr	...	1	0	1
1	None	3	3	4	3	Y	Feedr	Norm	SBrkr	...	0	1	1
2	None	3	3	2	3	Y	Norm	Norm	SBrkr	...	1	0	1

3 rows × 218 columns

GarageType

"""
location of the Garage
"""
get_feature_corr1('GarageType')

png

如果观察了该特征，其实可以发现这些现象值是有优劣关系的，但是售价并没有跟特征的优劣值进行对应，因此可以简单将这些特征进行one-hot转化也可以实现，
builtin 的车库房屋售价平均值最高

all_data = pd.get_dummies(all_data, columns = ["GarageType"], prefix="GarageType")
all_data.head(3)

	Alley	BedroomAbvGr	BsmtCond	BsmtExposure	BsmtQual	CentralAir	Condition1	Condition2	Electrical	...	ExterCond_TA	GarageType_Attchd
0	None	3	3	1	3	Y	Norm	Norm	SBrkr	...	1	1
1	None	3	3	4	3	Y	Feedr	Norm	SBrkr	...	1	1
2	None	3	3	2	3	Y	Norm	Norm	SBrkr	...	1	1

3 rows × 224 columns

GarageYrBlt

"""
Year Garage was built
"""
get_feature_corr1('GarageYrBlt')

png

年代越近，售价有逐步走高的趋势

plt.subplots(figsize =(50, 10))

sns.boxplot(x="GarageYrBlt", y="SalePrice", data=train);

png

plt.subplots(figsize =(50, 10))
sns.violinplot(x = 'GarageYrBlt', y = 'SalePrice', data = train,
               linewidth = 2, #线宽
               width = 0.8,   #箱之间的间隔比例
               palette = 'hls', #设置调色板
#                order = {'Thur', 'Fri', 'Sat','Sun'}, #筛选类别
#                scale = 'count',  #测度小提琴图的宽度： area-面积相同,count-按照样本数量决定宽度,width-宽度一样
               gridsize = 50, #设置小提琴图的平滑度，越高越平滑
               inner = 'box', #设置内部显示类型 --> 'box','quartile','point','stick',None
               #bw = 0.8      #控制拟合程度，一般可以不设置
               )
### 新学到的seaborn中的一些新图

<matplotlib.axes._subplots.AxesSubplot at 0x12e2cec50>

png

train['GarageYrBlt'].value_counts()
sns.distplot(train['GarageYrBlt'].dropna(), kde=True, bins=5, rug=True)

<matplotlib.axes._subplots.AxesSubplot at 0x12945c940>

png

all_data['GarageYrBlt_Band']  = pd.qcut(all_data['GarageYrBlt'],3,labels=list('123'))
# qcut是根据这些值的频率来选择箱子的均匀间隔，即每个箱子中含有的数的数量是相同的
# cut将根据值本身来选择箱子均匀间隔，即每个箱子的间距都是相同的

all_data['GarageYrBlt_Band'] = all_data['GarageYrBlt_Band'].astype(int)
all_data.drop(['GarageYrBlt'],axis=1,inplace=True)

all_data = pd.get_dummies(all_data, columns = ["GarageYrBlt_Band"], prefix="GarageYrBlt")  # 默认删除掉原来的特征，因此不必删除旧值
all_data.head(3)

	Alley	BedroomAbvGr	BsmtCond	BsmtExposure	BsmtQual	CentralAir	Condition1	Condition2	Electrical	...	GarageType_Attchd	GarageYrBlt_2	GarageYrBlt_3
0	None	3	3	1	3	Y	Norm	Norm	SBrkr	...	1	0	1
1	None	3	3	4	3	Y	Feedr	Norm	SBrkr	...	1	1	0
2	None	3	3	2	3	Y	Norm	Norm	SBrkr	...	1	0	1

3 rows × 226 columns

GarageFinish

get_feature_corr1('GarageFinish')

png

all_data = pd.get_dummies(all_data, columns = ["GarageFinish"], prefix="GarageFinish")
all_data.head(3)

	Alley	BedroomAbvGr	BsmtCond	BsmtExposure	BsmtQual	CentralAir	Condition1	Condition2	Electrical	...	GarageYrBlt_2	GarageYrBlt_3	GarageFinish_RFn
0	None	3	3	1	3	Y	Norm	Norm	SBrkr	...	0	1	1
1	None	3	3	4	3	Y	Feedr	Norm	SBrkr	...	1	0	1
2	None	3	3	2	3	Y	Norm	Norm	SBrkr	...	0	1	1

3 rows × 229 columns

GarageCars

"""
size of the Garage in car capacity 
默认是的数字不用其他操作，3辆车容量的车库售价最高，四辆车的转手频率较低(5个样本)
"""
get_feature_corr1('GarageCars')

png

GarageArea

get_feature_corr('GarageArea')

png

all_data['GarageArea_Band']  = pd.cut(all_data['GarageArea'],3,labels=list('123'))
all_data['GarageArea_Band'] =all_data['GarageArea_Band'].astype('int')
all_data.drop(['GarageArea'],axis=1,inplace=True)

all_data = pd.get_dummies(all_data, columns = ["GarageArea_Band"], prefix="GarageArea")
all_data.head(3)

	Alley	BedroomAbvGr	BsmtCond	BsmtExposure	BsmtQual	CentralAir	Condition1	Condition2	Electrical	...	GarageYrBlt_2	GarageYrBlt_3	GarageFinish_RFn	GarageArea_1	GarageArea_2
0	None	3	3	1	3	Y	Norm	Norm	SBrkr	...	0	1	1	0	1
1	None	3	3	4	3	Y	Feedr	Norm	SBrkr	...	1	0	1	1	0
2	None	3	3	2	3	Y	Norm	Norm	SBrkr	...	0	1	1	0	1

3 rows × 231 columns

GarageQual

"""
Garage  quality
"""

get_feature_corr1('GarageQual',order=['Po','Fa','TA','Gd','Ex'])

png

"TA"的出售的价格有较高的值以及数量较为集中，而两端的数据却很分散，因此可以两边的特征进行合并

all_data['GarageQual'] = all_data['GarageQual'].map({"None":"None", "Po":"Low", "Fa":"Low", "TA":"TA", "Gd":"High", "Ex":"High"})
all_data['GarageQual'].unique()

array(['TA', 'Low', 'High', 'None'], dtype=object)

all_data = pd.get_dummies(all_data, columns = ["GarageQual"], prefix="GarageQual")
all_data.head(3)

	Alley	BedroomAbvGr	BsmtCond	BsmtExposure	BsmtQual	CentralAir	Condition1	Condition2	Electrical	...	GarageFinish_RFn	GarageArea_1	GarageArea_2	GarageQual_TA
0	None	3	3	1	3	Y	Norm	Norm	SBrkr	...	1	0	1	1
1	None	3	3	4	3	Y	Feedr	Norm	SBrkr	...	1	1	0	1
2	None	3	3	2	3	Y	Norm	Norm	SBrkr	...	1	0	1	1

3 rows × 234 columns

GarageCond

"""
Garage condition.
"""

get_feature_corr1('GarageCond',order=['Po','Fa','TA','Gd','Ex'])

png

该特征与garage quality 特征处理方式类似

all_data['GarageCond']= all_data['GarageCond'].map({"None":'None',"Po":'Low','Fa':'Low','TA':'TA','Gd':'High','Ex':'High'})
all_data['GarageCond'].unique()

array(['TA', 'Low', 'None', 'High'], dtype=object)

all_data = pd.get_dummies(all_data, columns = ["GarageCond"], prefix="GarageCond")
all_data.head(3)

	Alley	BedroomAbvGr	BsmtCond	BsmtExposure	BsmtQual	CentralAir	Condition1	Condition2	Electrical	...	GarageArea_2	GarageQual_TA	GarageCond_TA
0	None	3	3	1	3	Y	Norm	Norm	SBrkr	...	1	1	1
1	None	3	3	4	3	Y	Feedr	Norm	SBrkr	...	0	1	1
2	None	3	3	2	3	Y	Norm	Norm	SBrkr	...	1	1	1

3 rows × 237 columns

WoodDeckSF

"""
Wood deck area in SF.
"""

get_feature_corr('WoodDeckSF')

png

high correlation with salesPrice
很多的0值，需要单独创建一个特征，来说明是否伟木质材料构建
对于非0值，进行封箱操作，然后转化为one-hot特征

def WoodDeckFlag(col):
    if col['WoodDeckSF'] == 0:
        return 1
    else:
        return 0
    
all_data['NoWoodDeck_Flag'] = all_data.apply(WoodDeckFlag, axis=1)  # new feature

all_data['WoodDeckSF_Band'] = pd.cut(all_data['WoodDeckSF'], 4,labels=list('1234'))  ## bin 

all_data['WoodDeckSF_Band'] = all_data['WoodDeckSF_Band'].astype(int)

all_data.drop('WoodDeckSF', axis=1, inplace=True)

all_data = pd.get_dummies(all_data, columns = ["WoodDeckSF_Band"], prefix="WoodDeckSF")
all_data.head(3)

	Alley	BedroomAbvGr	BsmtCond	BsmtExposure	BsmtQual	CentralAir	Condition1	Condition2	Electrical	...	GarageQual_TA	GarageCond_TA	NoWoodDeck_Flag	WoodDeckSF_1
0	None	3	3	1	3	Y	Norm	Norm	SBrkr	...	1	1	1	1
1	None	3	3	4	3	Y	Feedr	Norm	SBrkr	...	1	1	0	1
2	None	3	3	2	3	Y	Norm	Norm	SBrkr	...	1	1	1	1

3 rows × 241 columns

TotalPorchSF

"""
OpenPorchSF, EnclosedPorch, 3SsnPorch & ScreenPorch

I will sum these features together to create a total porch in square feet feature.
"""
all_data['TotalPorchSF'] = all_data['OpenPorchSF'] + all_data['OpenPorchSF'] + all_data['EnclosedPorch'] + all_data['3SsnPorch'] + all_data['ScreenPorch'] 
train['TotalPorchSF'] = train['OpenPorchSF'] + train['OpenPorchSF'] + train['EnclosedPorch'] + train['3SsnPorch'] + train['ScreenPorch']

get_feature_corr('TotalPorchSF')

png

def PorchFlag(col):
    if col['TotalPorchSF'] == 0:
        return 1
    else:
        return 0
    
all_data['NoPorch_Flag'] = all_data.apply(PorchFlag, axis=1)

all_data['TotalPorchSF_Band'] = pd.cut(all_data['TotalPorchSF'], 4,labels=list('1234'))
all_data['TotalPorchSF_Band'].unique()
all_data['TotalPorchSF_Band'] = all_data['TotalPorchSF_Band'].astype(int)

all_data.drop('TotalPorchSF', axis=1, inplace=True)

all_data = pd.get_dummies(all_data, columns = ["TotalPorchSF_Band"], prefix="TotalPorchSF")
all_data.head(3)

	Alley	BedroomAbvGr	BsmtCond	BsmtExposure	BsmtQual	CentralAir	Condition1	Condition2	Electrical	...	NoWoodDeck_Flag	WoodDeckSF_1	NoPorch_Flag	TotalPorchSF_1
0	None	3	3	1	3	Y	Norm	Norm	SBrkr	...	1	1	0	1
1	None	3	3	4	3	Y	Feedr	Norm	SBrkr	...	0	1	1	1
2	None	3	3	2	3	Y	Norm	Norm	SBrkr	...	1	1	0	1

3 rows × 246 columns

PoolArea

"""
PoolArea Pool area in square feet.
"""
get_feature_corr('PoolArea')

png

def PoolFlag(col):
    if col['PoolArea'] == 0:
        return 0
    else:
        return 1
    
all_data['HasPool_Flag'] = all_data.apply(PoolFlag, axis=1)
all_data.drop('PoolArea', axis=1, inplace=True)

PoolQC

"""
Pool quality.
"""
get_feature_corr1('PoolQC',order=['Fa','Gd','Ex'])

png

all_data['PoolQC'].value_counts()  #  总共8个数据带pool，其他的都是不带的，所以拿到的这个quality数据意义不大

None    2907
Gd         3
Ex         3
Fa         2
Name: PoolQC, dtype: int64

all_data.drop('PoolQC', axis=1, inplace=True)

Fence

'''
Fence: Fence quality
		
       GdPrv	Good Privacy
       MnPrv	Minimum Privacy
       GdWo	Good Wood
       MnWw	Minimum Wood/Wire
       NA	No Fence
'''

get_feature_corr1('Fence',order=['MnWw','GdWo','MnPrv','GdPrv'])

png

all_data = pd.get_dummies(all_data, columns = ["Fence"], prefix="Fence")
all_data.head(3)

	Alley	BedroomAbvGr	BsmtCond	BsmtExposure	BsmtQual	CentralAir	Condition1	Condition2	Electrical	...	TotalPorchSF_1	Fence_None
0	None	3	3	1	3	Y	Norm	Norm	SBrkr	...	1	1
1	None	3	3	4	3	Y	Feedr	Norm	SBrkr	...	1	1
2	None	3	3	2	3	Y	Norm	Norm	SBrkr	...	1	1

3 rows × 249 columns

MSZoning

"""
MSZoning: Identifies the general zoning classification of the sale.
		
       A	Agriculture
       C	Commercial
       FV	Floating Village Residential
       I	Industrial
       RH	Residential High Density
       RL	Residential Low Density
       RP	Residential Low Density Park 
       RM	Residential Medium Density
"""
get_feature_corr1('MSZoning')
all_data['MSZoning'].value_counts()

png

RL         2265
RM          460
FV          139
RH           26
C (all)      25
Name: MSZoning, dtype: int64

all_data = pd.get_dummies(all_data, columns = ["MSZoning"], prefix="MSZoning")
all_data.head(3)

	Alley	BedroomAbvGr	BsmtCond	BsmtExposure	BsmtQual	CentralAir	Condition1	Condition2	Electrical	...	Fence_None	MSZoning_RL
0	None	3	3	1	3	Y	Norm	Norm	SBrkr	...	1	1
1	None	3	3	4	3	Y	Feedr	Norm	SBrkr	...	1	1
2	None	3	3	2	3	Y	Norm	Norm	SBrkr	...	1	1

3 rows × 253 columns

Neighborhood

"""
this feature has lots of values,and SalePrice varies a lot in the values of the feature,
we  just use one-hot to transform this feature

"""

get_feature_corr1('Neighborhood')
all_data = pd.get_dummies(all_data, columns = ["Neighborhood"], prefix="Neighborhood")
all_data.head(3)

png

	Alley	BedroomAbvGr	BsmtCond	BsmtExposure	BsmtQual	CentralAir	Condition1	Condition2	Electrical	...	Neighborhood_Veenker
0	None	3	3	1	3	Y	Norm	Norm	SBrkr	...	0
1	None	3	3	4	3	Y	Feedr	Norm	SBrkr	...	1
2	None	3	3	2	3	Y	Norm	Norm	SBrkr	...	0

3 rows × 277 columns

Condition1 & Condition2

print('condition1')
get_feature_corr1('Condition1')
print('condition2')
get_feature_corr1('Condition2')

condition1

png

condition2

png

'''
Condition1: Proximity to various conditions
       Artery	Adjacent to arterial street
       Feedr	Adjacent to feeder street
       Norm	Normal
       RRNn	Within 200' of North-South Railroad
       RRAn	Adjacent to North-South Railroad
       PosN	Near positive off-site feature--park, greenbelt, etc.
       PosA	Adjacent to postive off-site feature
       RRNe	Within 200' of East-West Railroad
       RRAe	Adjacent to East-West Railroad

'''
all_data['Condition1'] = all_data['Condition1'].map({"Norm":"Norm", "Feedr":"Street", "PosN":"Pos", "Artery":"Street", "RRAe":"Train",
                                                    "RRNn":"Train", "RRAn":"Train", "PosA":"Pos", "RRNe":"Train"})
all_data['Condition2'] = all_data['Condition2'].map({"Norm":"Norm", "Feedr":"Street", "PosN":"Pos", "Artery":"Street", "RRAe":"Train",
                                                    "RRNn":"Train", "RRAn":"Train", "PosA":"Pos", "RRNe":"Train"})

def ConditionMatch(col):
    if col['Condition1'] == col['Condition2']:
        return 0
    else:
        return 1
    
all_data['Diff2ndCondition_Flag'] = all_data.apply(ConditionMatch, axis=1)
all_data.drop('Condition2', axis=1, inplace=True)

all_data = pd.get_dummies(all_data, columns = ["Condition1"], prefix="Condition1")
all_data.head(3)

	Alley	BedroomAbvGr	BsmtCond	BsmtExposure	BsmtQual	CentralAir	Electrical	ExterQual	...	Neighborhood_Veenker	Diff2ndCondition_Flag	Condition1_Norm	Condition1_Street
0	None	3	3	1	3	Y	SBrkr	3	...	0	0	1	0
1	None	3	3	4	3	Y	SBrkr	2	...	1	1	0	1
2	None	3	3	2	3	Y	SBrkr	3	...	0	0	1	0

3 rows × 280 columns

LotFrontage

"""
Linear feet of street connected to property.
"""

get_feature_corr('LotFrontage')

png

该特征与saleprice 没有明显的相关性，可以考虑去掉该特征

LotArea

'''
Lot size in square feet.
'''
get_feature_corr('LotArea')

png

该特征与saleprice有着明显的相关性，且该特征与saleprice呈现一个正偏态（峰左移，右偏，正偏）

all_data['LotArea_Band'] = pd.qcut(all_data['LotArea'], 8,labels=list('12345678'))  # 针对分布不均匀的特征使用qcut进行封箱
all_data['LotArea_Band'].unique()
all_data['LotArea_Band'] = all_data['LotArea_Band'].astype(int)

all_data.drop('LotArea', axis=1, inplace=True)

all_data = pd.get_dummies(all_data, columns = ["LotArea_Band"], prefix="LotArea")
all_data.head(3)

	Alley	BedroomAbvGr	BsmtCond	BsmtExposure	BsmtQual	CentralAir	Electrical	ExterQual	...	Condition1_Street	LotArea_3	LotArea_5	LotArea_6
0	None	3	3	1	3	Y	SBrkr	3	...	0	1	0	0
1	None	3	3	4	3	Y	SBrkr	2	...	1	0	1	0
2	None	3	3	2	3	Y	SBrkr	3	...	0	0	0	1

3 rows × 287 columns

LotShape

"""
LotShape: General shape of property

       Reg	Regular	
       IR1	Slightly irregular
       IR2	Moderately Irregular
       IR3	Irregula
该特征能够明显的影响售价，在国外，不仅仅要有大的面积数，而且尺寸也要合理，否则也很能卖出高价 
"""
get_feature_corr1('LotShape')

png

all_data = pd.get_dummies(all_data, columns = ["LotShape"], prefix="LotShape")
all_data.head(3)
print("地皮的形状主要集中在Reg，Reg1两个值里面，而且salerice在不同的属性里面变化很大")

地皮的形状主要集中在Reg，Reg1两个值里面，而且salerice在不同的属性里面变化很大

LandContour

"""
LandContour: Flatness of the property

       Lvl	Near Flat/Level	
       Bnk	Banked - Quick and significant rise from street grade to building
       HLS	Hillside - Significant slope from side to side
       Low	Depression

"""
get_feature_corr1('LandContour')
all_data = pd.get_dummies(all_data, columns = ["LandContour"], prefix="LandContour")
all_data.head(3)

png

	Alley	BedroomAbvGr	BsmtCond	BsmtExposure	BsmtQual	CentralAir	Electrical	ExterQual	...	LotShape_IR1	LotShape_Reg	LandContour_Lvl
0	None	3	3	1	3	Y	SBrkr	3	...	0	1	1
1	None	3	3	4	3	Y	SBrkr	2	...	0	1	1
2	None	3	3	2	3	Y	SBrkr	3	...	1	0	1

3 rows × 293 columns

LotConfig

"""
LotConfig: Lot configuration

       Inside	Inside lot 内部
       Corner	Corner lot 角落
       CulDSac	Cul-de-sac 死胡同
       FR2	Frontage on 2 sides of property 前排
       FR3	Frontage on 3 sides of property  前排
房子周围的环境 
"""
get_feature_corr1('LotConfig')
all_data['LotConfig'] = all_data['LotConfig'].map({"Inside":"Inside", "FR2":"FR", "Corner":"Corner", "CulDSac":"CulDSac", "FR3":"FR"})

all_data = pd.get_dummies(all_data, columns = ["LotConfig"], prefix="LotConfig")
all_data.head(3)

png

	Alley	BedroomAbvGr	BsmtCond	BsmtExposure	BsmtQual	CentralAir	Electrical	ExterQual	...	LotShape_Reg	LandContour_Lvl	LotConfig_FR	LotConfig_Inside
0	None	3	3	1	3	Y	SBrkr	3	...	1	1	0	1
1	None	3	3	4	3	Y	SBrkr	2	...	1	1	1	0
2	None	3	3	2	3	Y	SBrkr	3	...	0	1	0	1

3 rows × 296 columns

LandSlope

"""
LandSlope: Slope of property
       Gtl	Gentle slope
       Mod	Moderate Slope
       Sev	Severe Slope
"""
get_feature_corr1('LandSlope')

png

all_data['LandSlope'] = all_data['LandSlope'].map({"Gtl":1, "Mod":0, "Sev":0})
'''
Mod and Sev saleprice 处于同一区间，可以将两者合并
'''

'\nMod and Sev saleprice 处于同一区间，可以将两者合并\n'

all_data['LandSlope'].value_counts()

1    2774
0     141
Name: LandSlope, dtype: int64

Street

get_feature_corr1('Street')

png

Pave中价格变化很大，且Grvl数量太少，所以该特征意义不大，直接去掉

all_data.drop('Street', axis=1, inplace=True)

Alley

get_feature_corr1('Alley')

png

all_data['Alley'].value_counts()

None    2717
Grvl     120
Pave      78
Name: Alley, dtype: int64

all_data = pd.get_dummies(all_data, columns = ["Alley"], prefix="Alley")
all_data.head(3)

	BedroomAbvGr	BsmtCond	BsmtExposure	BsmtQual	CentralAir	Electrical	ExterQual	FireplaceQu	...	LandContour_Lvl	LotConfig_FR	LotConfig_Inside	Alley_None
0	3	3	1	3	Y	SBrkr	3	0	...	1	0	1	1
1	3	3	4	3	Y	SBrkr	2	3	...	1	1	0	1
2	3	3	2	3	Y	SBrkr	3	3	...	1	0	1	1

3 rows × 297 columns

PvaeDrive

"""
PavedDrive: Paved driveway

       Y	Paved 价格差异较大，且没有明显的顺序关系，需要转化为one-hot特征
       P	Partial Pavement
       N	Dirt/Gravel
"""
get_feature_corr1('PavedDrive')

png

all_data=pd.get_dummies(all_data,columns=['PavedDrive'],prefix='PavedDrive')
all_data.head()

	BedroomAbvGr	BsmtCond	BsmtExposure	BsmtQual	CentralAir	Electrical	EnclosedPorch	ExterQual	FireplaceQu	...	LotConfig_Corner	LotConfig_FR	LotConfig_Inside	Alley_None	PavedDrive_Y
0	3	3	1	3	Y	SBrkr	0	3	0	...	0	0	1	1	1
1	3	3	4	3	Y	SBrkr	0	2	3	...	0	1	0	1	1
2	3	3	2	3	Y	SBrkr	0	3	3	...	0	0	1	1	1
3	3	4	1	2	Y	SBrkr	272	2	4	...	1	0	0	1	1
4	4	3	3	3	Y	SBrkr	0	3	3	...	0	1	0	1	1

5 rows × 299 columns

Heating

get_feature_corr1('Heating')

png

"""
大量集中在GasA，其余的数据量非常小，可以转化为天然气供暖，和其他方式供暖
"""
all_data['Heating']  = all_data['Heating'].map({'GasA':1,'GasW':0,'Grav':0,'Wall':0,'OthW':0,'Floor':0})

all_data.drop('Heating', axis=1, inplace=True)
all_data.head(3)

	BedroomAbvGr	BsmtCond	BsmtExposure	BsmtQual	CentralAir	Electrical	ExterQual	FireplaceQu	...	LotConfig_FR	LotConfig_Inside	Alley_None	PavedDrive_Y
0	3	3	1	3	Y	SBrkr	3	0	...	0	1	1	1
1	3	3	4	3	Y	SBrkr	2	3	...	1	0	1	1
2	3	3	2	3	Y	SBrkr	3	3	...	0	1	1	1

3 rows × 298 columns

HeatingQC

"""
Heating quality and condition.
"""
get_feature_corr1('HeatingQC',order=['Po','Fa','TA','Gd','Ex'])

png

all_data['HeatingQC'] = all_data['HeatingQC'].map({"Po":1, "Fa":2, "TA":3, "Gd":4, "Ex":5})
all_data['HeatingQC'].unique()

array([5, 4, 3, 2, 1])

CentralAir

"""
Central air conditioning.

"""
get_feature_corr1('CentralAir')

png

all_data['CentralAir'] = all_data['CentralAir'].map({"Y":1,"N":0})

Electrical

"""
Electrical system.

"""

get_feature_corr1('Electrical')

png

all_data['Electrical'] = all_data['Electrical'].map({'SBrkr':'SBrkr','FuseF':'Fuse','FuseA':'Fuse','FuseP':'Fuse','Mix':'Mix'})
all_data = pd.get_dummies(all_data, columns = ["Electrical"], prefix="Electrical")
all_data.head(3)

	BedroomAbvGr	BsmtCond	BsmtExposure	BsmtQual	CentralAir	ExterQual	FireplaceQu	Fireplaces	...	LotConfig_Inside	Alley_None	PavedDrive_Y	Electrical_SBrkr
0	3	3	1	3	1	3	0	0	...	1	1	1	1
1	3	3	4	3	1	2	3	1	...	0	1	1	1
2	3	3	2	3	1	3	3	1	...	1	1	1	1

3 rows × 300 columns

all_data['MiscFeature'].value_counts()  #

None    2810
Shed      95
Gar2       5
Othr       4
TenC       1
Name: MiscFeature, dtype: int64

get_feature_corr1('MiscFeature')
'''
有效数据太少，剔除该特征
'''

png

'\n有效数据太少，剔除该特征\n'

get_feature_corr1('MiscVal')

png

all_data['MiscVal'].value_counts()
"""
有效数据过少，剔除该特征
"""

'\n有效数据过少，剔除该特征\n'

all_data.drop(['MiscVal','MiscFeature'],axis=1,inplace=True)

MoSold and YrSold

"""
month sold,Year Sold
"""
get_feature_corr1('MoSold')

png

get_feature_corr1('YrSold')

png

all_data = pd.get_dummies(all_data, columns = ["MoSold"], prefix="MoSold")
all_data = pd.get_dummies(all_data,columns=['YrSold'],prefix='YrSold')
all_data.head(3)

	BedroomAbvGr	BsmtCond	BsmtExposure	BsmtQual	CentralAir	ExterQual	FireplaceQu	Fireplaces	...	MoSold_9	YrSold_2007	YrSold_2008
0	3	3	1	3	1	3	0	0	...	0	0	1
1	3	3	4	3	1	2	3	1	...	0	1	0
2	3	3	2	3	1	3	3	1	...	1	0	1

3 rows × 313 columns

SaleType

"""
SaleType: Type of sale
		
       WD 	Warranty Deed - Conventional
       CWD	Warranty Deed - Cash
       VWD	Warranty Deed - VA Loan
       New	Home just constructed and sold
       COD	Court Officer Deed/Estate
       Con	Contract 15% Down payment regular terms
       ConLw	Contract Low Down payment and low interest
       ConLI	Contract Low Interest
       ConLD	Contract Low Down
       Oth	Other

"""
get_feature_corr1('SaleType')

png

all_data['SaleType'] = all_data['SaleType'].map({'WD':"WD",'New':"New",'COD':"COD",'CWD':'Oth','ConLD':'Oth','ConLI':'Oth',
                                                "ConLW":'Oth','Con':'Oth','Oth':'Oth'})
all_data=  pd.get_dummies(all_data,columns=['SaleType'],prefix='SaleType')
all_data.head()

	BedroomAbvGr	BsmtCond	BsmtExposure	BsmtQual	CentralAir	EnclosedPorch	ExterQual	FireplaceQu	Fireplaces	...	MoSold_12	YrSold_2006	YrSold_2007	YrSold_2008	SaleType_WD
0	3	3	1	3	1	0	3	0	0	...	0	0	0	1	1
1	3	3	4	3	1	0	2	3	1	...	0	0	1	0	1
2	3	3	2	3	1	0	3	3	1	...	0	0	0	1	1
3	3	4	1	2	1	272	2	4	1	...	0	1	0	0	1
4	4	3	3	3	1	0	3	3	1	...	1	0	0	1	1

5 rows × 316 columns

SaleCondition

"""
Condition of sale.

"""

get_feature_corr1('SaleCondition')

png

all_data = pd.get_dummies(all_data, columns = ["SaleCondition"], prefix="SaleCondition")
all_data.head(3)

	BedroomAbvGr	BsmtCond	BsmtExposure	BsmtQual	CentralAir	ExterQual	FireplaceQu	Fireplaces	...	SaleType_WD	SaleCondition_Normal
0	3	3	1	3	1	3	0	0	...	1	1
1	3	3	4	3	1	2	3	1	...	1	1
2	3	3	2	3	1	3	3	1	...	1	1

3 rows × 321 columns

目标值转换

与分类算法不同，回归是用算法拟合连续值
通常需要对目标值进行分布进行分析，机器学习的算法对于正态分布的数据一般都有很高的拟合度，如果目标值为偏正态分布，需要将目标值转化为正态分布

from scipy.stats import skew, norm
plt.subplots(figsize=(15,12))
g = sns.distplot(train['SalePrice'],fit=norm,label="Skewness:%.2f" % (train['SalePrice'].skew()))
g.legend(loc='best')

<matplotlib.legend.Legend at 0x12f5f5cc0>

png

目标变量为正偏态，可以是用numpy中的函数，将其转化

train["SalePrice"] = np.log1p(train["SalePrice"])
y_train = train["SalePrice"]

#Check the new distribution 
plt.subplots(figsize=(15,10))
g = sns.distplot(train['SalePrice'], fit=norm, label = "Skewness : %.2f"%(train['SalePrice'].skew()));
g = g.legend(loc="best")

png

处理数据中偏态的特征

numeric_feats = all_data.dtypes[all_data.dtypes != "object"].index

# Check how skewed they are
skewed_feats = all_data[numeric_feats].apply(lambda x: skew(x.dropna())).sort_values(ascending=False)

plt.subplots(figsize =(65, 20))
skewed_feats.plot(kind='bar');

png


from scipy.special import boxcox1p

skewness = skewed_feats[abs(skewed_feats) > 0.5]

skewed_features = skewness.index
lam = 0.15
for feat in skewed_features:
    all_data[feat] = boxcox1p(all_data[feat], lam)

print(skewness.shape[0],  "skewed numerical features have been Box-Cox transformed")

294 skewed numerical features have been Box-Cox transformed

准备模型训练的数据

train = all_data[:ntrain]
test = all_data[ntrain:]
print(train.shape)
print(test.shape)

(1456, 321)
(1459, 321)

y_train.shape

(1456,)

feature importance

import xgboost as xgb

model = xgb.XGBRegressor()
model.fit(train, y_train)


# Sort feature importances from GBC model trained earlier
indices = np.argsort(model.feature_importances_)[::-1]
indices = indices[:75]

# Visualise these with a barplot
plt.subplots(figsize=(20, 15))
g = sns.barplot(y=train.columns[indices], x = model.feature_importances_[indices], orient='h')
g.set_xlabel("Relative importance",fontsize=12)
g.set_ylabel("Features",fontsize=12)
g.tick_params(labelsize=9)
g.set_title("XGB feature importance");

/Users/aihuishou/anaconda3/envs/work/lib/python3.6/site-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version
  if getattr(data, 'base', None) is not None and \
/Users/aihuishou/anaconda3/envs/work/lib/python3.6/site-packages/xgboost/core.py:588: FutureWarning: Series.base is deprecated and will be removed in a future version
  data.base is not None and isinstance(data, np.ndarray) \


[11:04:46] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.

png

xgb_train = train.copy()
xgb_test = test.copy()
from sklearn.feature_selection import SelectFromModel

xgb_feat_red = SelectFromModel(model,prefit=True)
# reduce estimation validation and test datasets
xgb_train = xgb_feat_red.transform(xgb_train)
xgb_test = xgb_feat_red.transform(xgb_test)
print('X_train: ', xgb_train.shape, '\nX_test: ', xgb_test.shape)

X_train:  (1456, 47) 
X_test:  (1459, 47)


from sklearn import model_selection

X_train, X_test, Y_train, Y_test = model_selection.train_test_split(xgb_train, y_train, test_size=0.3, random_state=42)

# X_train = predictor features for estimation dataset
# X_test = predictor variables for validation dataset
# Y_train = target variable for the estimation dataset
# Y_test = target variable for the estimation dataset

print('X_train: ', X_train.shape, '\nX_test: ', X_test.shape, '\nY_train: ', Y_train.shape, '\nY_test: ', Y_test.shape)

X_train:  (1019, 47) 
X_test:  (437, 47) 
Y_train:  (1019,) 
Y_test:  (437,)

X_train

array([[0.73046315, 3.        , 0.73046315, ..., 0.        , 0.        ,
        0.        ],
       [0.73046315, 3.        , 0.73046315, ..., 0.        , 0.        ,
        0.        ],
       [1.19431764, 2.        , 0.73046315, ..., 0.        , 0.        ,
        0.        ],
       ...,
       [1.8203341 , 3.        , 0.73046315, ..., 0.73046315, 0.        ,
        0.        ],
       [0.73046315, 3.        , 0.73046315, ..., 0.        , 0.        ,
        0.        ],
       [1.54096276, 3.        , 0.73046315, ..., 0.        , 0.        ,
        0.        ]])

训练不同的模型

# 从sklearn 导入不同的回归模型
from sklearn.linear_model import ElasticNet, Lasso,  BayesianRidge, LassoLarsIC
from sklearn.ensemble import RandomForestRegressor,  GradientBoostingRegressor, ExtraTreesRegressor
from sklearn.kernel_ridge import KernelRidge
import xgboost as xgb
print('Algorithm packages imported!')

Algorithm packages imported!

# Model selection packages used for sampling dataset and optimising parameters
from sklearn import model_selection
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score, train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import ShuffleSplit
print('Model selection packages imported!')

Model selection packages imported!

models = [KernelRidge(),ElasticNet(),Lasso(),GradientBoostingRegressor(),BayesianRidge(),LassoLarsIC(),RandomForestRegressor(),xgb.XGBRegressor()]
# 随机取样，其实可以使用正常的split，然后选择里面的shuffle = True
# https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html
shuff =ShuffleSplit(n_splits=5,test_size=0.2,random_state=42)
# 创建一个数据框，用于保存模型的指标
columns = ['Name','Parameters','Train mean_squared_error','Test mean_squared_error']
before_model_compare = pd.DataFrame(columns=columns)

# 将模型的参数以及结果添加到DataFrame中
row_index=0
for alg in models:
    model_name = alg.__class__.__name__
    before_model_compare.loc[row_index,'Name'] = model_name
    before_model_compare.loc[row_index,'Parameters'] = str(alg.get_params())
    alg.fit(X_train,Y_train)
    # for cross_validation  but the results are negative,we need to convert it to postive,均方误差
    training_results = np.sqrt((-cross_val_score(alg,X_train,Y_train,cv=shuff,scoring='neg_mean_squared_error')).mean())
    test_results = np.sqrt(((Y_test-alg.predict(X_test))**2).mean())
    before_model_compare.loc[row_index,"Train mean_squared_error"] = training_results*100
    before_model_compare.loc[row_index,'Test mean_squared_error'] = test_results*100
    row_index+=1
    print(row_index,model_name,"trained>>>>")

    
decimals = 3
before_model_compare['Train mean_squared_error'] = before_model_compare['Train mean_squared_error'].apply(lambda x:round(x,decimals))
before_model_compare['Test mean_squared_error'] = before_model_compare['Train mean_squared_error'].apply(lambda x:round(x,decimals))
before_model_compare

1 KernelRidge trained>>>>
2 ElasticNet trained>>>>
3 Lasso trained>>>>
4 GradientBoostingRegressor trained>>>>
5 BayesianRidge trained>>>>
6 LassoLarsIC trained>>>>
7 RandomForestRegressor trained>>>>
[12:04:14] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[12:04:14] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[12:04:14] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.


/Users/aihuishou/anaconda3/envs/work/lib/python3.6/site-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version
  if getattr(data, 'base', None) is not None and \
/Users/aihuishou/anaconda3/envs/work/lib/python3.6/site-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version
  if getattr(data, 'base', None) is not None and \
/Users/aihuishou/anaconda3/envs/work/lib/python3.6/site-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version
  if getattr(data, 'base', None) is not None and \
/Users/aihuishou/anaconda3/envs/work/lib/python3.6/site-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version
  if getattr(data, 'base', None) is not None and \
/Users/aihuishou/anaconda3/envs/work/lib/python3.6/site-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version
  if getattr(data, 'base', None) is not None and \


[12:04:14] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[12:04:14] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[12:04:14] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
8 XGBRegressor trained>>>>

	Name	Parameters	Train mean_squared_error	Test mean_squared_error
0	KernelRidge	{'alpha': 1, 'coef0': 1, 'degree': 3, 'gamma':...	31.424	31.424
1	ElasticNet	{'alpha': 1.0, 'copy_X': True, 'fit_intercept'...	23.245	23.245
2	Lasso	{'alpha': 1.0, 'copy_X': True, 'fit_intercept'...	28.008	28.008
3	GradientBoostingRegressor	{'alpha': 0.9, 'criterion': 'friedman_mse', 'i...	12.381	12.381
4	BayesianRidge	{'alpha_1': 1e-06, 'alpha_2': 1e-06, 'compute_...	11.118	11.118
5	LassoLarsIC	{'copy_X': True, 'criterion': 'aic', 'eps': 2....	11.818	11.818
6	RandomForestRegressor	{'bootstrap': True, 'criterion': 'mse', 'max_d...	14.299	14.299
7	XGBRegressor	{'base_score': 0.5, 'booster': 'gbtree', 'cols...	12.466	12.466

优化参数

开始的时候，我们准备了不同模型简单的看了模型的评价以及训练结果
实际上，这些模型都需要进一步的参数优化
下一步需要是用GridSearch进行参数的调整

models = [KernelRidge(),ElasticNet(),Lasso(),GradientBoostingRegressor(),BayesianRidge(),LassoLarsIC(),RandomForestRegressor(),
         xgb.XGBRegressor()]
KR_param_grid = {'alpha': [0.1], 'coef0': [100], 'degree': [1], 'gamma': [None], 'kernel': ['polynomial']}
EN_param_grid = {'alpha': [0.001], 'copy_X': [True], 'l1_ratio': [0.6], 'fit_intercept': [True], 'normalize': [False], 
                         'precompute': [False], 'max_iter': [300], 'tol': [0.001], 'selection': ['random'], 'random_state': [None]}
LASS_param_grid = {'alpha': [0.0005], 'copy_X': [True], 'fit_intercept': [True], 'normalize': [False], 'precompute': [False], 
                    'max_iter': [300], 'tol': [0.01], 'selection': ['random'], 'random_state': [None]}
GB_param_grid = {'loss': ['huber'], 'learning_rate': [0.1], 'n_estimators': [300], 'max_depth': [3], 
                                        'min_samples_split': [0.0025], 'min_samples_leaf': [5]}
BR_param_grid = {'n_iter': [200], 'tol': [0.00001], 'alpha_1': [0.00000001], 'alpha_2': [0.000005], 'lambda_1': [0.000005], 
                 'lambda_2': [0.00000001], 'copy_X': [True]}
LL_param_grid = {'criterion': ['aic'], 'normalize': [True], 'max_iter': [100], 'copy_X': [True], 'precompute': ['auto'], 'eps': [0.000001]}
RFR_param_grid = {'n_estimators': [50], 'max_features': ['auto'], 'max_depth': [None], 'min_samples_split': [5], 'min_samples_leaf': [2]}
XGB_param_grid = {'max_depth': [3], 'learning_rate': [0.1], 'n_estimators': [300], 'booster': ['gbtree'], 'gamma': [0], 'reg_alpha': [0.1],
                  'reg_lambda': [0.7], 'max_delta_step': [0], 'min_child_weight': [1], 'colsample_bytree': [0.5], 'colsample_bylevel': [0.2],
                  'scale_pos_weight': [1]}
params_grid = [KR_param_grid, EN_param_grid, LASS_param_grid, GB_param_grid, BR_param_grid, LL_param_grid, RFR_param_grid, XGB_param_grid]

after_model_compare = pd.DataFrame(columns=columns)
row_index= 0

for alg in models:
    gs_alg = GridSearchCV(alg,param_grid=params_grid[0],cv=shuff,scoring='neg_mean_squared_error',n_jobs=-1)
    params_grid.pop(0)
    
    
    model_name = alg.__class__.__name__
    after_model_compare.loc[row_index,'Name'] = model_name
    gs_alg.fit(X_train,Y_train)
    gs_best=gs_alg.best_estimator_
    after_model_compare.loc[row_index,"Parameters"] = str(gs_alg.best_params_)
    after_training_results = np.sqrt(-gs_alg.best_score_)
    after_test_results = np.sqrt((Y_test-gs_alg.predict(X_test)**2).mean())
    after_model_compare.loc[row_index,"Train mean_squared_error"] = after_training_results*100
    after_model_compare.loc[row_index,'Test mean_squared_error']= after_test_results*100
    row_index+=1
    print(row_index,model_name,"trained>>>>>")


    
decimals = 3
after_model_compare['Train mean_squared_error'] = after_model_compare['Train mean_squared_error'].apply(lambda x:round(x,decimals))
after_model_compare['Test mean_squared_error'] = after_model_compare['Train mean_squared_error'].apply(lambda x:round(x,decimals))
after_model_compare

/Users/aihuishou/anaconda3/envs/work/lib/python3.6/site-packages/ipykernel_launcher.py:33: RuntimeWarning: invalid value encountered in sqrt
/Users/aihuishou/anaconda3/envs/work/lib/python3.6/site-packages/ipykernel_launcher.py:33: RuntimeWarning: invalid value encountered in sqrt
/Users/aihuishou/anaconda3/envs/work/lib/python3.6/site-packages/ipykernel_launcher.py:33: RuntimeWarning: invalid value encountered in sqrt


1 KernelRidge trained>>>>>
2 ElasticNet trained>>>>>
3 Lasso trained>>>>>


/Users/aihuishou/anaconda3/envs/work/lib/python3.6/site-packages/ipykernel_launcher.py:33: RuntimeWarning: invalid value encountered in sqrt
/Users/aihuishou/anaconda3/envs/work/lib/python3.6/site-packages/ipykernel_launcher.py:33: RuntimeWarning: invalid value encountered in sqrt
/Users/aihuishou/anaconda3/envs/work/lib/python3.6/site-packages/ipykernel_launcher.py:33: RuntimeWarning: invalid value encountered in sqrt


4 GradientBoostingRegressor trained>>>>>
5 BayesianRidge trained>>>>>
6 LassoLarsIC trained>>>>>


/Users/aihuishou/anaconda3/envs/work/lib/python3.6/site-packages/ipykernel_launcher.py:33: RuntimeWarning: invalid value encountered in sqrt
/Users/aihuishou/anaconda3/envs/work/lib/python3.6/site-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version
  if getattr(data, 'base', None) is not None and \


7 RandomForestRegressor trained>>>>>
[19:23:22] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
8 XGBRegressor trained>>>>>

	Name	Parameters	Train mean_squared_error	Test mean_squared_error
0	KernelRidge	{'alpha': 0.1, 'coef0': 100, 'degree': 1, 'gam...	11.140	11.140
1	ElasticNet	{'alpha': 0.001, 'copy_X': True, 'fit_intercep...	11.234	11.234
2	Lasso	{'alpha': 0.0005, 'copy_X': True, 'fit_interce...	11.203	11.203
3	GradientBoostingRegressor	{'learning_rate': 0.1, 'loss': 'huber', 'max_d...	11.966	11.966
4	BayesianRidge	{'alpha_1': 1e-08, 'alpha_2': 5e-06, 'copy_X':...	11.118	11.118
5	LassoLarsIC	{'copy_X': True, 'criterion': 'aic', 'eps': 1e...	11.818	11.818
6	RandomForestRegressor	{'max_depth': None, 'max_features': 'auto', 'm...	13.735	13.735
7	XGBRegressor	{'booster': 'gbtree', 'colsample_bylevel': 0.2...	11.964	11.964

stacking method

准备一系列的算法模型
将train训练数据分割为训练数据和验证数据(X_trian，Y_train，X_test,Y_test)
在X_train数据集中进行算法拟合，然后将训练出来的模型去拟合X_test(验证集），将模型拟合出的验证集的结果和实际的Y_test组成的新的训练数据（new_train datasets）
将训练出来的模型去拟合test数据集，得到每个模型预测的结果，组成醒的test数据集，new_test dataset
用一个相对简单或者使用不同的模型（meta-model），比如说lasso，将新的训练进行拟合，然后将拟合后的模型预测新的测试集new_test_dataset，得到新的模型
将新的模型去拟合新的测试集（new_test_dataset），得到预测的结果

models  = [KernelRidge(),ElasticNet(),Lasso(),GradientBoostingRegressor(),BayesianRidge(),LassoLarsIC(),RandomForestRegressor(),xgb.XGBRegressor()]
names = ['KernelRidge','ElasticNet','Lasso','GradientBoostingRegressor','BayesianRidge','LassoLarsIC','RandomForest','XGBoost']
params_grid = [KR_param_grid, EN_param_grid, LASS_param_grid, GB_param_grid, BR_param_grid, LL_param_grid, RFR_param_grid, XGB_param_grid]
stacked_validation_train = pd.DataFrame()
stacked_test_train = pd.DataFrame()

row_index= 0

for alg in models:
    gs_alg = GridSearchCV(alg,param_grid=params_grid[0],cv=shuff,scoring='neg_mean_squared_error',n_jobs=-1)
    params_grid.pop(0)
    gs_alg.fit(X_train,Y_train)
    gs_best = gs_alg.best_estimator_
    stacked_validation_train.insert(loc= row_index,column=names[0],value=gs_best.predict(X_test))
    """  dataFrme insert (loc 表示的是列的序号，column 列名，value 插入的内容)"""
    print(row_index+1,alg.__class__.__name__,"将验证集的预测的结果堆砌，组成新的训练集")
    stacked_test_train.insert(loc=row_index,column=names[0],value=gs_best.predict(xgb_test))
    print(row_index+1,alg.__class__.__name__,"将测试集的预测的结果堆砌，组成新的测试集")
    print("---"*50)
    names.pop(0)
    row_index+=1
    
print("第一层数据处理完成，新的训练集与测试集完成")

1 KernelRidge 将验证集的预测的结果堆砌，组成新的训练集
1 KernelRidge 将测试集的预测的结果堆砌，组成新的测试集
------------------------------------------------------------------------------------------------------------------------------------------------------
2 ElasticNet 将验证集的预测的结果堆砌，组成新的训练集
2 ElasticNet 将测试集的预测的结果堆砌，组成新的测试集
------------------------------------------------------------------------------------------------------------------------------------------------------
3 Lasso 将验证集的预测的结果堆砌，组成新的训练集
3 Lasso 将测试集的预测的结果堆砌，组成新的测试集
------------------------------------------------------------------------------------------------------------------------------------------------------
4 GradientBoostingRegressor 将验证集的预测的结果堆砌，组成新的训练集
4 GradientBoostingRegressor 将测试集的预测的结果堆砌，组成新的测试集
------------------------------------------------------------------------------------------------------------------------------------------------------
5 BayesianRidge 将验证集的预测的结果堆砌，组成新的训练集
5 BayesianRidge 将测试集的预测的结果堆砌，组成新的测试集
------------------------------------------------------------------------------------------------------------------------------------------------------
6 LassoLarsIC 将验证集的预测的结果堆砌，组成新的训练集
6 LassoLarsIC 将测试集的预测的结果堆砌，组成新的测试集
------------------------------------------------------------------------------------------------------------------------------------------------------
7 RandomForestRegressor 将验证集的预测的结果堆砌，组成新的训练集
7 RandomForestRegressor 将测试集的预测的结果堆砌，组成新的测试集
------------------------------------------------------------------------------------------------------------------------------------------------------
[15:23:01] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
8 XGBRegressor 将验证集的预测的结果堆砌，组成新的训练集
8 XGBRegressor 将测试集的预测的结果堆砌，组成新的测试集
------------------------------------------------------------------------------------------------------------------------------------------------------
第一层数据处理完成，新的训练集与测试集完成


/Users/aihuishou/anaconda3/envs/work/lib/python3.6/site-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version
  if getattr(data, 'base', None) is not None and \

print(stacked_validation_train.shape)
stacked_validation_train.head()
# Y_test的数据结果

(437, 8)

	KernelRidge	ElasticNet	Lasso	GradientBoostingRegressor	BayesianRidge	LassoLarsIC	RandomForest	XGBoost
0	12.096814	12.095574	12.095347	12.103610	12.095675	12.104932	12.170897	12.084927
1	11.952395	11.966939	11.964576	12.027570	11.957859	11.999328	12.066678	12.071651
2	11.798390	11.800390	11.807569	11.842686	11.807968	11.787126	11.880778	11.789903
3	11.834224	11.814334	11.820662	11.806835	11.840026	11.837654	11.755137	11.753889
4	11.287412	11.267859	11.271162	11.150576	11.289689	11.290524	11.328786	11.278980

print(stacked_test_train.shape)
stacked_test_train.head()

(1459, 8)

	KernelRidge	ElasticNet	Lasso	GradientBoostingRegressor	BayesianRidge	LassoLarsIC	RandomForest	XGBoost
0	11.655653	11.666206	11.661235	11.717153	11.664298	11.639410	11.735618	11.754628
1	12.033653	12.042914	12.039875	11.950150	12.032724	12.007921	11.956780	11.985191
2	12.121196	12.121925	12.124266	12.138572	12.125334	12.072644	12.097413	12.115376
3	12.194246	12.200128	12.201113	12.166538	12.196015	12.143436	12.095009	12.139894
4	12.171520	12.180859	12.179168	12.145913	12.167523	12.168576	12.178091	12.176064

stacked_validation_train.drop('Lasso',axis=1,inplace=True)
stacked_test_train.drop('Lasso',axis=1,inplace=True)
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import RobustScaler



meta_model = make_pipeline(RobustScaler(),Lasso(alpha=0.00001,copy_X=True,fit_intercept=True,normalize=False,precompute=False,
                                               max_iter=10000,tol=0.0001,selection='random',random_state=42))
meta_model.fit(stacked_validation_train,Y_test)
meta_model_pred= np.expm1(meta_model.predict(stacked_test_train))
print("meta_model 完成训练，并预测测试集的数据")

meta_model 完成训练，并预测测试集的数据


/Users/aihuishou/anaconda3/envs/work/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.7538551527086552, tolerance: 0.006483051719467419
  positive)

models = [KernelRidge(), ElasticNet(), Lasso(), GradientBoostingRegressor(), BayesianRidge(), LassoLarsIC(), RandomForestRegressor(), xgb.XGBRegressor()]
names = ['KernelRidge', 'ElasticNet', 'Lasso', 'Gradient Boosting', 'Bayesian Ridge', 'Lasso Lars IC', 'Random Forest', 'XGBoost']
params_grid = [KR_param_grid, EN_param_grid, LASS_param_grid, GB_param_grid, BR_param_grid, LL_param_grid, RFR_param_grid, XGB_param_grid]
final_predictions = pd.DataFrame()

row_index=0

for alg in models:
    
    gs_alg = GridSearchCV(alg, param_grid = params_grid[0], cv = shuff, scoring = 'neg_mean_squared_error', n_jobs=-1)
    params_grid.pop(0)
    
    gs_alg.fit(stacked_validation_train, Y_test)
    gs_best = gs_alg.best_estimator_
    final_predictions.insert(loc = row_index, column = names[0], value = np.expm1(gs_best.predict(stacked_test_train)))
    print(row_index+1, alg.__class__.__name__, 'final results predicted added to table...')
    names.pop(0)
    
    row_index+=1

print("-"*50)
print("已经完成")
final_predictions.head()

1 KernelRidge final results predicted added to table...
2 ElasticNet final results predicted added to table...
3 Lasso final results predicted added to table...
4 GradientBoostingRegressor final results predicted added to table...
5 BayesianRidge final results predicted added to table...
6 LassoLarsIC final results predicted added to table...
7 RandomForestRegressor final results predicted added to table...
[18:03:42] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
8 XGBRegressor final results predicted added to table...
--------------------------------------------------
已经完成


/Users/aihuishou/anaconda3/envs/work/lib/python3.6/site-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version
  if getattr(data, 'base', None) is not None and \

	KernelRidge	ElasticNet	Lasso	Gradient Boosting	Bayesian Ridge	Lasso Lars IC	Random Forest	XGBoost
0	120698.786728	121126.968875	120569.541877	119545.552352	121817.672344	121618.593011	120774.731602	117987.320312
1	162778.261755	162293.616103	163198.661456	154034.245333	162888.953970	162663.194168	154944.085742	154422.265625
2	184187.690046	183822.395933	184145.902661	181996.954345	185167.984485	184643.383928	181824.224304	174336.687500
3	193128.541814	192388.040730	193035.580999	195110.109361	193760.580424	193069.794744	188563.541259	181933.593750
4	192957.823204	192839.290437	193289.070140	192292.299199	192910.466862	192890.725826	190770.891456	192144.093750

ensemble = meta_model_pred*(1/10) + final_predictions['XGBoost']*(1.5/10) + final_predictions['Gradient Boosting']*(2/10) + final_predictions['Bayesian Ridge']*(1/10) + final_predictions['Lasso']*(1/10) + final_predictions['KernelRidge']*(1/10) + final_predictions['Lasso Lars IC']*(1/10) + final_predictions['Random Forest']*(1.5/10)

submission = pd.DataFrame()
test1 = pd.read_csv('test.csv',index_col=False)
test_ID = test1['Id']
submission['Id'] = test_ID
submission['SalePrice'] = ensemble
submission.to_csv('final_submission.csv',index=False)
print("Submission file, created!")

Submission file, created!

posted on 2019-07-24 09:53 多一点阅读(471) 评论(0) 编辑收藏举报

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	BedroomAbvGr	BsmtCond	BsmtExposure	BsmtQual	CentralAir	ExterQual	FireplaceQu	Fireplaces	...	LotConfig_Inside	Alley_None	PavedDrive_Y	Electrical_SBrkr
0	3	3	1	3	1	3	0	0	...	1	1	1	1
1	3	3	4	3	1	2	3	1	...	0	1	1	1
2	3	3	2	3	1	3	3	1	...	1	1	1	1

	BedroomAbvGr	BsmtCond	BsmtExposure	BsmtQual	CentralAir	ExterQual	FireplaceQu	Fireplaces	...	MoSold_9	YrSold_2007	YrSold_2008
0	3	3	1	3	1	3	0	0	...	0	0	1
1	3	3	4	3	1	2	3	1	...	0	1	0
2	3	3	2	3	1	3	3	1	...	1	0	1

	BedroomAbvGr	BsmtCond	BsmtExposure	BsmtQual	CentralAir	EnclosedPorch	ExterQual	FireplaceQu	Fireplaces	...	MoSold_12	YrSold_2006	YrSold_2007	YrSold_2008	SaleType_WD
0	3	3	1	3	1	0	3	0	0	...	0	0	0	1	1
1	3	3	4	3	1	0	2	3	1	...	0	0	1	0	1
2	3	3	2	3	1	0	3	3	1	...	0	0	0	1	1
3	3	4	1	2	1	272	2	4	1	...	0	1	0	0	1
4	4	3	3	3	1	0	3	3	1	...	1	0	0	1	1

	BedroomAbvGr	BsmtCond	BsmtExposure	BsmtQual	CentralAir	ExterQual	FireplaceQu	Fireplaces	...	SaleType_WD	SaleCondition_Normal
0	3	3	1	3	1	3	0	0	...	1	1
1	3	3	4	3	1	2	3	1	...	1	1
2	3	3	2	3	1	3	3	1	...	1	1

	BedroomAbvGr	BsmtCond	BsmtExposure	BsmtQual	CentralAir	ExterQual	FireplaceQu	Fireplaces	...	LotConfig_Inside	Alley_None	PavedDrive_Y	Electrical_SBrkr
0	3	3	1	3	1	3	0	0	...	1	1	1	1
1	3	3	4	3	1	2	3	1	...	0	1	1	1
2	3	3	2	3	1	3	3	1	...	1	1	1	1

	BedroomAbvGr	BsmtCond	BsmtExposure	BsmtQual	CentralAir	ExterQual	FireplaceQu	Fireplaces	...	MoSold_9	YrSold_2007	YrSold_2008
0	3	3	1	3	1	3	0	0	...	0	0	1
1	3	3	4	3	1	2	3	1	...	0	1	0
2	3	3	2	3	1	3	3	1	...	1	0	1

	BedroomAbvGr	BsmtCond	BsmtExposure	BsmtQual	CentralAir	EnclosedPorch	ExterQual	FireplaceQu	Fireplaces	...	MoSold_12	YrSold_2006	YrSold_2007	YrSold_2008	SaleType_WD
0	3	3	1	3	1	0	3	0	0	...	0	0	0	1	1
1	3	3	4	3	1	0	2	3	1	...	0	0	1	0	1
2	3	3	2	3	1	0	3	3	1	...	0	0	0	1	1
3	3	4	1	2	1	272	2	4	1	...	0	1	0	0	1
4	4	3	3	3	1	0	3	3	1	...	1	0	0	1	1

	BedroomAbvGr	BsmtCond	BsmtExposure	BsmtQual	CentralAir	ExterQual	FireplaceQu	Fireplaces	...	SaleType_WD	SaleCondition_Normal
0	3	3	1	3	1	3	0	0	...	1	1
1	3	3	4	3	1	2	3	1	...	1	1
2	3	3	2	3	1	3	3	1	...	1	1

	BedroomAbvGr	BsmtCond	BsmtExposure	BsmtQual	CentralAir	ExterQual	FireplaceQu	Fireplaces	...	LotConfig_Inside	Alley_None	PavedDrive_Y	Electrical_SBrkr
0	3	3	1	3	1	3	0	0	...	1	1	1	1
1	3	3	4	3	1	2	3	1	...	0	1	1	1
2	3	3	2	3	1	3	3	1	...	1	1	1	1

	BedroomAbvGr	BsmtCond	BsmtExposure	BsmtQual	CentralAir	ExterQual	FireplaceQu	Fireplaces	...	MoSold_9	YrSold_2007	YrSold_2008
0	3	3	1	3	1	3	0	0	...	0	0	1
1	3	3	4	3	1	2	3	1	...	0	1	0
2	3	3	2	3	1	3	3	1	...	1	0	1

	BedroomAbvGr	BsmtCond	BsmtExposure	BsmtQual	CentralAir	EnclosedPorch	ExterQual	FireplaceQu	Fireplaces	...	MoSold_12	YrSold_2006	YrSold_2007	YrSold_2008	SaleType_WD
0	3	3	1	3	1	0	3	0	0	...	0	0	0	1	1
1	3	3	4	3	1	0	2	3	1	...	0	0	1	0	1
2	3	3	2	3	1	0	3	3	1	...	0	0	0	1	1
3	3	4	1	2	1	272	2	4	1	...	0	1	0	0	1
4	4	3	3	3	1	0	3	3	1	...	1	0	0	1	1

	BedroomAbvGr	BsmtCond	BsmtExposure	BsmtQual	CentralAir	ExterQual	FireplaceQu	Fireplaces	...	SaleType_WD	SaleCondition_Normal
0	3	3	1	3	1	3	0	0	...	1	1
1	3	3	4	3	1	2	3	1	...	1	1
2	3	3	2	3	1	3	3	1	...	1	1