In [102]:

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from google.colab import drive
import os
drive.mount('/content/drive')
# Establecer ruta de acceso en drive
import os
print(os.getcwd())
os.chdir("/content/drive/My Drive")
print(os.getcwd())

Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount("/content/drive", force_remount=True).
/content/drive/My Drive
/content/drive/My Drive

Lectura de archivos¶

In [103]:

train=pd.read_csv('train_datathon.csv',sep=',')
train.head()

Out[103]:

	Id	MSSubClass	MSZoning	LotFrontage	LotArea	Street	Alley	LotShape	LandContour	Utilities	LotConfig	LandSlope	Neighborhood	Condition1	Condition2	BldgType	HouseStyle	OverallQual	OverallCond	YearBuilt	YearRemodAdd	RoofStyle	RoofMatl	Exterior1st	Exterior2nd	MasVnrType	MasVnrArea	ExterQual	ExterCond	Foundation	BsmtQual	BsmtCond	BsmtExposure	BsmtFinType1	BsmtFinSF1	BsmtFinType2	BsmtUnfSF	TotalBsmtSF	Heating	...	CentralAir	Electrical	1stFlrSF	2ndFlrSF	GrLivArea	BsmtFullBath	BsmtHalfBath	FullBath	HalfBath	BedroomAbvGr	KitchenAbvGr	KitchenQual	TotRmsAbvGrd	Functional	Fireplaces	FireplaceQu	GarageType	GarageYrBlt	GarageFinish	GarageCars	GarageArea	GarageQual	GarageCond	PavedDrive	WoodDeckSF	OpenPorchSF	EnclosedPorch	PoolQC	Fence	MiscFeature	MoSold	YrSold	SaleType	SaleCondition	SalePrice
0	1	60	RL	65.0	8450	Pave	NaN	Reg	Lvl	AllPub	Inside	Gtl	CollgCr	Norm	Norm	1Fam	2Story	7	5	2003	2003	Gable	CompShg	VinylSd	VinylSd	BrkFace	196.0	Gd	TA	PConc	Gd	TA	No	GLQ	706	Unf	150	856	GasA	...	Y	SBrkr	856	854	1710	1	0	2	1	3	1	Gd	8	Typ	0	NaN	Attchd	2003.0	RFn	2	548	TA	TA	Y	0	61	0	NaN	NaN	NaN	2	2008	WD	Normal	208500
1	2	20	RL	80.0	9600	Pave	NaN	Reg	Lvl	AllPub	FR2	Gtl	Veenker	Feedr	Norm	1Fam	1Story	6	8	1976	1976	Gable	CompShg	MetalSd	MetalSd	None	0.0	TA	TA	CBlock	Gd	TA	Gd	ALQ	978	Unf	284	1262	GasA	...	Y	SBrkr	1262	0	1262	0	1	2	0	3	1	TA	6	Typ	1	TA	Attchd	1976.0	RFn	2	460	TA	TA	Y	298	0	0	NaN	NaN	NaN	5	2007	WD	Normal	181500
2	3	60	RL	68.0	11250	Pave	NaN	IR1	Lvl	AllPub	Inside	Gtl	CollgCr	Norm	Norm	1Fam	2Story	7	5	2001	2002	Gable	CompShg	VinylSd	VinylSd	BrkFace	162.0	Gd	TA	PConc	Gd	TA	Mn	GLQ	486	Unf	434	920	GasA	...	Y	SBrkr	920	866	1786	1	0	2	1	3	1	Gd	6	Typ	1	TA	Attchd	2001.0	RFn	2	608	TA	TA	Y	0	42	0	NaN	NaN	NaN	9	2008	WD	Normal	223500
3	4	70	RL	60.0	9550	Pave	NaN	IR1	Lvl	AllPub	Corner	Gtl	Crawfor	Norm	Norm	1Fam	2Story	7	5	1915	1970	Gable	CompShg	Wd Sdng	Wd Shng	None	0.0	TA	TA	BrkTil	TA	Gd	No	ALQ	216	Unf	540	756	GasA	...	Y	SBrkr	961	756	1717	1	0	1	0	3	1	Gd	7	Typ	1	Gd	Detchd	1998.0	Unf	3	642	TA	TA	Y	0	35	272	NaN	NaN	NaN	2	2006	WD	Abnorml	140000
4	5	60	RL	84.0	14260	Pave	NaN	IR1	Lvl	AllPub	FR2	Gtl	NoRidge	Norm	Norm	1Fam	2Story	8	5	2000	2000	Gable	CompShg	VinylSd	VinylSd	BrkFace	350.0	Gd	TA	PConc	Gd	TA	Av	GLQ	655	Unf	490	1145	GasA	...	Y	SBrkr	1145	1053	2198	1	0	2	1	4	1	Gd	9	Typ	1	TA	Attchd	2000.0	RFn	3	836	TA	TA	Y	192	84	0	NaN	NaN	NaN	12	2008	WD	Normal	250000

5 rows × 81 columns

In [104]:

test=pd.read_csv('test_datathon.csv',sep=',')
test.head()

Out[104]:

	Id	MSSubClass	MSZoning	LotFrontage	LotArea	Street	Alley	LotShape	LandContour	Utilities	LotConfig	LandSlope	Neighborhood	Condition1	Condition2	BldgType	HouseStyle	OverallQual	OverallCond	YearBuilt	YearRemodAdd	RoofStyle	RoofMatl	Exterior1st	Exterior2nd	MasVnrType	MasVnrArea	ExterQual	ExterCond	Foundation	BsmtQual	BsmtCond	BsmtExposure	BsmtFinType1	BsmtFinSF1	BsmtFinType2	BsmtFinSF2	BsmtUnfSF	TotalBsmtSF	Heating	HeatingQC	CentralAir	Electrical	1stFlrSF	2ndFlrSF	GrLivArea	FullBath	HalfBath	BedroomAbvGr	KitchenAbvGr	KitchenQual	TotRmsAbvGrd	Functional	Fireplaces	FireplaceQu	GarageType	GarageYrBlt	GarageFinish	GarageCars	GarageArea	GarageQual	GarageCond	PavedDrive	WoodDeckSF	OpenPorchSF	ScreenPorch	PoolQC	Fence	MiscFeature	MiscVal	MoSold	YrSold	SaleType	SaleCondition
0	1461	20	RH	80.0	11622	Pave	NaN	Reg	Lvl	AllPub	Inside	Gtl	NAmes	Feedr	Norm	1Fam	1Story	5	6	1961	1961	Gable	CompShg	VinylSd	VinylSd	None	0.0	TA	TA	CBlock	TA	TA	No	Rec	468.0	LwQ	144.0	270.0	882.0	GasA	TA	Y	SBrkr	896	0	896	1	0	2	1	TA	5	Typ	0	NaN	Attchd	1961.0	Unf	1.0	730.0	TA	TA	Y	140	0	120	NaN	MnPrv	NaN	0	6	2010	WD	Normal
1	1462	20	RL	81.0	14267	Pave	NaN	IR1	Lvl	AllPub	Corner	Gtl	NAmes	Norm	Norm	1Fam	1Story	6	6	1958	1958	Hip	CompShg	Wd Sdng	Wd Sdng	BrkFace	108.0	TA	TA	CBlock	TA	TA	No	ALQ	923.0	Unf	0.0	406.0	1329.0	GasA	TA	Y	SBrkr	1329	0	1329	1	1	3	1	Gd	6	Typ	0	NaN	Attchd	1958.0	Unf	1.0	312.0	TA	TA	Y	393	36	0	NaN	NaN	Gar2	12500	6	2010	WD	Normal
2	1463	60	RL	74.0	13830	Pave	NaN	IR1	Lvl	AllPub	Inside	Gtl	Gilbert	Norm	Norm	1Fam	2Story	5	5	1997	1998	Gable	CompShg	VinylSd	VinylSd	None	0.0	TA	TA	PConc	Gd	TA	No	GLQ	791.0	Unf	0.0	137.0	928.0	GasA	Gd	Y	SBrkr	928	701	1629	2	1	3	1	TA	6	Typ	1	TA	Attchd	1997.0	Fin	2.0	482.0	TA	TA	Y	212	34	0	NaN	MnPrv	NaN	0	3	2010	WD	Normal
3	1464	60	RL	78.0	9978	Pave	NaN	IR1	Lvl	AllPub	Inside	Gtl	Gilbert	Norm	Norm	1Fam	2Story	6	6	1998	1998	Gable	CompShg	VinylSd	VinylSd	BrkFace	20.0	TA	TA	PConc	TA	TA	No	GLQ	602.0	Unf	0.0	324.0	926.0	GasA	Ex	Y	SBrkr	926	678	1604	2	1	3	1	Gd	7	Typ	1	Gd	Attchd	1998.0	Fin	2.0	470.0	TA	TA	Y	360	36	0	NaN	NaN	NaN	0	6	2010	WD	Normal
4	1465	120	RL	43.0	5005	Pave	NaN	IR1	HLS	AllPub	Inside	Gtl	StoneBr	Norm	Norm	TwnhsE	1Story	8	5	1992	1992	Gable	CompShg	HdBoard	HdBoard	None	0.0	Gd	TA	PConc	Gd	TA	No	ALQ	263.0	Unf	0.0	1017.0	1280.0	GasA	Ex	Y	SBrkr	1280	0	1280	2	0	2	1	Gd	5	Typ	0	NaN	Attchd	1992.0	RFn	2.0	506.0	TA	TA	Y	0	82	144	NaN	NaN	NaN	0	1	2010	WD	Normal

In [4]:

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

(1460, 81)
(1459, 80)

Analisis exploratorio¶

In [5]:

train.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1460 entries, 0 to 1459
Data columns (total 81 columns):
 #   Column         Non-Null Count  Dtype  
---  ------         --------------  -----  
 0   Id             1460 non-null   int64  
 1   MSSubClass     1460 non-null   int64  
 2   MSZoning       1460 non-null   object 
 3   LotFrontage    1201 non-null   float64
 4   LotArea        1460 non-null   int64  
 5   Street         1460 non-null   object 
 6   Alley          91 non-null     object 
 7   LotShape       1460 non-null   object 
 8   LandContour    1460 non-null   object 
 9   Utilities      1460 non-null   object 
 10  LotConfig      1460 non-null   object 
 11  LandSlope      1460 non-null   object 
 12  Neighborhood   1460 non-null   object 
 13  Condition1     1460 non-null   object 
 14  Condition2     1460 non-null   object 
 15  BldgType       1460 non-null   object 
 16  HouseStyle     1460 non-null   object 
 17  OverallQual    1460 non-null   int64  
 18  OverallCond    1460 non-null   int64  
 19  YearBuilt      1460 non-null   int64  
 20  YearRemodAdd   1460 non-null   int64  
 21  RoofStyle      1460 non-null   object 
 22  RoofMatl       1460 non-null   object 
 23  Exterior1st    1460 non-null   object 
 24  Exterior2nd    1460 non-null   object 
 25  MasVnrType     1452 non-null   object 
 26  MasVnrArea     1452 non-null   float64
 27  ExterQual      1460 non-null   object 
 28  ExterCond      1460 non-null   object 
 29  Foundation     1460 non-null   object 
 30  BsmtQual       1423 non-null   object 
 31  BsmtCond       1423 non-null   object 
 32  BsmtExposure   1422 non-null   object 
 33  BsmtFinType1   1423 non-null   object 
 34  BsmtFinSF1     1460 non-null   int64  
 35  BsmtFinType2   1422 non-null   object 
 36  BsmtFinSF2     1460 non-null   int64  
 37  BsmtUnfSF      1460 non-null   int64  
 38  TotalBsmtSF    1460 non-null   int64  
 39  Heating        1460 non-null   object 
 40  HeatingQC      1460 non-null   object 
 41  CentralAir     1460 non-null   object 
 42  Electrical     1459 non-null   object 
 43  1stFlrSF       1460 non-null   int64  
 44  2ndFlrSF       1460 non-null   int64  
 45  LowQualFinSF   1460 non-null   int64  
 46  GrLivArea      1460 non-null   int64  
 47  BsmtFullBath   1460 non-null   int64  
 48  BsmtHalfBath   1460 non-null   int64  
 49  FullBath       1460 non-null   int64  
 50  HalfBath       1460 non-null   int64  
 51  BedroomAbvGr   1460 non-null   int64  
 52  KitchenAbvGr   1460 non-null   int64  
 53  KitchenQual    1460 non-null   object 
 54  TotRmsAbvGrd   1460 non-null   int64  
 55  Functional     1460 non-null   object 
 56  Fireplaces     1460 non-null   int64  
 57  FireplaceQu    770 non-null    object 
 58  GarageType     1379 non-null   object 
 59  GarageYrBlt    1379 non-null   float64
 60  GarageFinish   1379 non-null   object 
 61  GarageCars     1460 non-null   int64  
 62  GarageArea     1460 non-null   int64  
 63  GarageQual     1379 non-null   object 
 64  GarageCond     1379 non-null   object 
 65  PavedDrive     1460 non-null   object 
 66  WoodDeckSF     1460 non-null   int64  
 67  OpenPorchSF    1460 non-null   int64  
 68  EnclosedPorch  1460 non-null   int64  
 69  3SsnPorch      1460 non-null   int64  
 70  ScreenPorch    1460 non-null   int64  
 71  PoolArea       1460 non-null   int64  
 72  PoolQC         7 non-null      object 
 73  Fence          281 non-null    object 
 74  MiscFeature    54 non-null     object 
 75  MiscVal        1460 non-null   int64  
 76  MoSold         1460 non-null   int64  
 77  YrSold         1460 non-null   int64  
 78  SaleType       1460 non-null   object 
 79  SaleCondition  1460 non-null   object 
 80  SalePrice      1460 non-null   int64  
dtypes: float64(3), int64(35), object(43)
memory usage: 924.0+ KB

Analisis de nulos (OJO)

In [6]:

pd.set_option('display.max_rows', None)  # or 1000
serie=(train.isnull().sum()/train.shape[0])*100
serie=serie.sort_values(ascending=False)
serie= serie[serie>0]
serie

Out[6]:

PoolQC          99.520548
MiscFeature     96.301370
Alley           93.767123
Fence           80.753425
FireplaceQu     47.260274
LotFrontage     17.739726
GarageCond       5.547945
GarageType       5.547945
GarageYrBlt      5.547945
GarageFinish     5.547945
GarageQual       5.547945
BsmtExposure     2.602740
BsmtFinType2     2.602740
BsmtFinType1     2.534247
BsmtCond         2.534247
BsmtQual         2.534247
MasVnrArea       0.547945
MasVnrType       0.547945
Electrical       0.068493
dtype: float64

In [7]:

import matplotlib.pyplot as plt
plt.figure(figsize=(10,6))
serie.plot(kind='bar')

Out[7]:

<matplotlib.axes._subplots.AxesSubplot at 0x7f9f804067d0>

In [8]:

pip install missingno

Requirement already satisfied: missingno in /usr/local/lib/python3.7/dist-packages (0.5.0)
Requirement already satisfied: seaborn in /usr/local/lib/python3.7/dist-packages (from missingno) (0.11.2)
Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from missingno) (3.2.2)
Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from missingno) (1.4.1)
Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from missingno) (1.19.5)
Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->missingno) (0.11.0)
Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->missingno) (2.4.7)
Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->missingno) (1.3.2)
Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->missingno) (2.8.2)
Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib->missingno) (1.15.0)
Requirement already satisfied: pandas>=0.23 in /usr/local/lib/python3.7/dist-packages (from seaborn->missingno) (1.1.5)
Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.23->seaborn->missingno) (2018.9)

In [9]:

import missingno as msno
msno.matrix(train)

Out[9]:

<matplotlib.axes._subplots.AxesSubplot at 0x7f9f6fd84790>

In [10]:

msno.heatmap(train)

Out[10]:

<matplotlib.axes._subplots.AxesSubplot at 0x7f9f869e71d0>

In [11]:

msno.dendrogram(train)

Out[11]:

<matplotlib.axes._subplots.AxesSubplot at 0x7f9f6717f050>

Comenzar a mirar las variables con mayor cantidad de nulos y explorar si tienen algo que aportar

In [12]:

train.PoolQC.value_counts() # Decision: borrar

Out[12]:

Gd    3
Fa    2
Ex    2
Name: PoolQC, dtype: int64

In [13]:

train.MiscFeature.value_counts() # Decision: borrar

Out[13]:

Shed    49
Othr     2
Gar2     2
TenC     1
Name: MiscFeature, dtype: int64

In [14]:

train.Alley.value_counts() # Decision: borrar

Out[14]:

Grvl    50
Pave    41
Name: Alley, dtype: int64

In [15]:

train.Fence.value_counts() # Decision: borrar

Out[15]:

MnPrv    157
GdPrv     59
GdWo      54
MnWw      11
Name: Fence, dtype: int64

In [16]:

train.FireplaceQu.value_counts() # Decision: Con este se podria hacer otra tecnica en vez de borrar la columna

Out[16]:

Gd    380
TA    313
Fa     33
Ex     24
Po     20
Name: FireplaceQu, dtype: int64

In [17]:

train.LotFrontage.isnull().sum() # Decision: Reemplazar con alguna tecnica de interpolacion

Out[17]:

In [18]:

train.GarageFinish.value_counts() # Decision: Alguna tecnica de reemplazo

Out[18]:

Unf    605
RFn    422
Fin    352
Name: GarageFinish, dtype: int64

In [19]:

train.GarageQual.value_counts() # Decision: Alguna tecnica de reemplazo

Out[19]:

TA    1311
Fa      48
Gd      14
Po       3
Ex       3
Name: GarageQual, dtype: int64

In [20]:

train.GarageCond.value_counts() # Decision: Reemplazar con alguna tecnica

Out[20]:

TA    1326
Fa      35
Gd       9
Po       7
Ex       2
Name: GarageCond, dtype: int64

In [21]:

train.GarageType.value_counts() # Decision: Reemplazar con alguna tecnica

Out[21]:

Attchd     870
Detchd     387
BuiltIn     88
Basment     19
CarPort      9
2Types       6
Name: GarageType, dtype: int64

In [22]:

train.BsmtExposure.value_counts() # Decision: Tecnica reemplazro

Out[22]:

No    953
Av    221
Gd    134
Mn    114
Name: BsmtExposure, dtype: int64

In [23]:

train.GarageYrBlt.isnull().sum() # Decision: Reemplazar con alguna tecnica de interpolacion

Out[23]:

In [24]:

train.BsmtFinType2.value_counts() # Decision: Tecnica reemplazro

Out[24]:

Unf    1256
Rec      54
LwQ      46
BLQ      33
ALQ      19
GLQ      14
Name: BsmtFinType2, dtype: int64

In [25]:

train.BsmtFinType1.value_counts() # Decision: Tecnica reemplazro

Out[25]:

Unf    430
GLQ    418
ALQ    220
BLQ    148
Rec    133
LwQ     74
Name: BsmtFinType1, dtype: int64

In [26]:

train.BsmtCond.value_counts()# Decision: reemplzar moda

Out[26]:

TA    1311
Gd      65
Fa      45
Po       2
Name: BsmtCond, dtype: int64

In [27]:

train.BsmtQual.value_counts()# Decision: reemplzar moda

Out[27]:

TA    649
Gd    618
Ex    121
Fa     35
Name: BsmtQual, dtype: int64

In [28]:

train.MasVnrArea.isnull().sum()

Out[28]:

In [29]:

train.MasVnrType.value_counts()

Out[29]:

None       864
BrkFace    445
Stone      128
BrkCmn      15
Name: MasVnrType, dtype: int64

In [30]:

train.Electrical.value_counts()

Out[30]:

SBrkr    1334
FuseA      94
FuseF      27
FuseP       3
Mix         1
Name: Electrical, dtype: int64

In [31]:

train.shape

Out[31]:

(1460, 81)

Borrado de columnas¶

In [32]:

col_del=['PoolQC','MiscFeature','Alley','Fence','Id'] # Columnas a borrar que no aportan
col_inter= ['LotFrontage','GarageYrBlt','MasVnrArea'] # Columnas a interpolar
col_cat= ['FireplaceQu','GarageFinish','GarageCond','GarageType','BsmtExposure','BsmtFinType2','BsmtFinType1','BsmtCond',
          'BsmtQual','MasVnrType','Electrical'] # Columnas cateogoricas

In [33]:

# Borrar variables
train_x=train.drop(labels=col_del, axis=1) # Borrando las columnas en col_del
train_x.columns # Verificando las columnas que quedaron

Out[33]:

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

In [34]:

for i in col_inter: # reemplazar por la mediana en las columnas numericas en col_inter
  train_x[i] = train_x[i].fillna(train_x[i].median())

In [35]:

train_x.MasVnrArea.isnull().sum() # Verificar que funciono

Out[35]:

In [36]:

for i in col_cat: # Reemplazar los vacios por Desconocido
  train_x[i] = train_x[i].fillna('Desconocido')

In [37]:

train_x.Electrical.isnull().sum() # Verificando que funciona

Out[37]:

In [38]:

train_x.SaleCondition.dtype

Out[38]:

dtype('O')

In [39]:

train_x.SaleCondition.value_counts()

Out[39]:

Normal     1198
Partial     125
Abnorml     101
Family       20
Alloca       12
AdjLand       4
Name: SaleCondition, dtype: int64

Recodificar variables categoricas¶

In [40]:

alldata=[] # lista para guardar resultados de One hot Encoding
alldata1=[] # lista para guardar resultados de 
variables=[] # ir guardando las variables
for i in train_x.columns: # iterar sobre todas las columnas
  if train_x[i].dtype == 'O': # Quedarme con las que sean tipo object
    if len(train_x[i].unique()) ==2: # Si solo tienen dos categorias aplicar One Hot Encoding
      print('Variable 2 categorias:',i)
      y= pd.get_dummies(train_x[i]) # Aplicar One Hot Encoding
      alldata.append(y) # ir agregando las columnas resultantes en alldata
      variables.append(i)
    else:
      pass
    print('--------------------')
    if len(train_x[i].unique()) >2: # Si las categorias tienen mas de 2 categorias aplicar LabelEncoder
      print('Variable >2 categorias:',i)
      z= pd.DataFrame()
      z[i]=train_x[i].astype('category').cat.codes # Aplicar Label Encoder
      alldata1.append(z) # Agregar a la lista alldata1
      variables.append(i) #
  if train_x[i].dtype == 'float64': # Si la columna es numerica entonces normalizar z score
    train_x[i]=(train_x[i]-train_x[i].mean())/(train_x[i].std()) # yapo
data_y=pd.concat(alldata, axis=1) # concatenar las columnas obtenidas One hot Encoding
data_z=pd.concat(alldata1, axis=1) # concatenar las columnas de Label Encoder
train_y= pd.concat([data_y,data_z,train_x],axis=1) # Unir todos los datasets One Hot Encoding, LabelEncoder, Z score
train_y=train_y.drop(labels=variables, axis=1) # Borrar las columnas ya recodificadas
train_y.head() # mostrar que quedo

--------------------
Variable >2 categorias: MSZoning
Variable 2 categorias: Street
--------------------
--------------------
Variable >2 categorias: LotShape
--------------------
Variable >2 categorias: LandContour
Variable 2 categorias: Utilities
--------------------
--------------------
Variable >2 categorias: LotConfig
--------------------
Variable >2 categorias: LandSlope
--------------------
Variable >2 categorias: Neighborhood
--------------------
Variable >2 categorias: Condition1
--------------------
Variable >2 categorias: Condition2
--------------------
Variable >2 categorias: BldgType
--------------------
Variable >2 categorias: HouseStyle
--------------------
Variable >2 categorias: RoofStyle
--------------------
Variable >2 categorias: RoofMatl
--------------------
Variable >2 categorias: Exterior1st
--------------------
Variable >2 categorias: Exterior2nd
--------------------
Variable >2 categorias: MasVnrType
--------------------
Variable >2 categorias: ExterQual
--------------------
Variable >2 categorias: ExterCond
--------------------
Variable >2 categorias: Foundation
--------------------
Variable >2 categorias: BsmtQual
--------------------
Variable >2 categorias: BsmtCond
--------------------
Variable >2 categorias: BsmtExposure
--------------------
Variable >2 categorias: BsmtFinType1
--------------------
Variable >2 categorias: BsmtFinType2
--------------------
Variable >2 categorias: Heating
--------------------
Variable >2 categorias: HeatingQC
Variable 2 categorias: CentralAir
--------------------
--------------------
Variable >2 categorias: Electrical
--------------------
Variable >2 categorias: KitchenQual
--------------------
Variable >2 categorias: Functional
--------------------
Variable >2 categorias: FireplaceQu
--------------------
Variable >2 categorias: GarageType
--------------------
Variable >2 categorias: GarageFinish
--------------------
Variable >2 categorias: GarageQual
--------------------
Variable >2 categorias: GarageCond
--------------------
Variable >2 categorias: PavedDrive
--------------------
Variable >2 categorias: SaleType
--------------------
Variable >2 categorias: SaleCondition

Out[40]:

	Pave	AllPub	Y	MSSubClass	LotFrontage	LotArea	OverallQual	OverallCond	YearBuilt	YearRemodAdd	MasVnrArea	BsmtFinSF1	BsmtUnfSF	TotalBsmtSF	1stFlrSF	2ndFlrSF	GrLivArea	BsmtFullBath	BsmtHalfBath	FullBath	HalfBath	BedroomAbvGr	KitchenAbvGr	TotRmsAbvGrd	Fireplaces	GarageYrBlt	GarageCars	GarageArea	WoodDeckSF	OpenPorchSF	EnclosedPorch	MoSold	YrSold	SalePrice
0	1	1	1	60	-0.220799	8450	7	5	2003	2003	0.513928	706	150	856	856	854	1710	1	0	2	1	3	1	8	0	1.017250	2	548	0	61	0	2	2008	208500
1	1	1	1	20	0.460162	9600	6	8	1976	1976	-0.570555	978	284	1262	1262	0	1262	0	1	2	0	3	1	6	1	-0.107890	2	460	298	0	0	5	2007	181500
2	1	1	1	60	-0.084607	11250	7	5	2001	2002	0.325803	486	434	920	920	866	1786	1	0	2	1	3	1	6	1	0.933906	2	608	0	42	0	9	2008	223500
3	1	1	1	70	-0.447787	9550	7	5	1915	1970	-0.570555	216	540	756	961	756	1717	1	0	1	0	3	1	7	1	0.808890	3	642	0	35	272	2	2006	140000
4	1	1	1	60	0.641752	14260	8	5	2000	2000	1.366021	655	490	1145	1145	1053	2198	1	0	2	1	4	1	9	1	0.892234	3	836	192	84	0	12	2008	250000

In [41]:

train_y.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1460 entries, 0 to 1459
Data columns (total 43 columns):
 #   Column         Non-Null Count  Dtype  
---  ------         --------------  -----  
 0   Grvl           1460 non-null   uint8  
 1   Pave           1460 non-null   uint8  
 2   AllPub         1460 non-null   uint8  
 3   NoSeWa         1460 non-null   uint8  
 4   N              1460 non-null   uint8  
 5   Y              1460 non-null   uint8  
 6   MSSubClass     1460 non-null   int64  
 7   LotFrontage    1460 non-null   float64
 8   LotArea        1460 non-null   int64  
 9   OverallQual    1460 non-null   int64  
 10  OverallCond    1460 non-null   int64  
 11  YearBuilt      1460 non-null   int64  
 12  YearRemodAdd   1460 non-null   int64  
 13  MasVnrArea     1460 non-null   float64
 14  BsmtFinSF1     1460 non-null   int64  
 15  BsmtFinSF2     1460 non-null   int64  
 16  BsmtUnfSF      1460 non-null   int64  
 17  TotalBsmtSF    1460 non-null   int64  
 18  1stFlrSF       1460 non-null   int64  
 19  2ndFlrSF       1460 non-null   int64  
 20  LowQualFinSF   1460 non-null   int64  
 21  GrLivArea      1460 non-null   int64  
 22  BsmtFullBath   1460 non-null   int64  
 23  BsmtHalfBath   1460 non-null   int64  
 24  FullBath       1460 non-null   int64  
 25  HalfBath       1460 non-null   int64  
 26  BedroomAbvGr   1460 non-null   int64  
 27  KitchenAbvGr   1460 non-null   int64  
 28  TotRmsAbvGrd   1460 non-null   int64  
 29  Fireplaces     1460 non-null   int64  
 30  GarageYrBlt    1460 non-null   float64
 31  GarageCars     1460 non-null   int64  
 32  GarageArea     1460 non-null   int64  
 33  WoodDeckSF     1460 non-null   int64  
 34  OpenPorchSF    1460 non-null   int64  
 35  EnclosedPorch  1460 non-null   int64  
 36  3SsnPorch      1460 non-null   int64  
 37  ScreenPorch    1460 non-null   int64  
 38  PoolArea       1460 non-null   int64  
 39  MiscVal        1460 non-null   int64  
 40  MoSold         1460 non-null   int64  
 41  YrSold         1460 non-null   int64  
 42  SalePrice      1460 non-null   int64  
dtypes: float64(3), int64(34), uint8(6)
memory usage: 430.7 KB

Definir entrenamiento y test¶

In [42]:

# traigo estos modelos 
from sklearn.ensemble import AdaBoostClassifier
from sklearn.model_selection import train_test_split
from sklearn import metrics
X = train_y.drop(columns=['SalePrice']) # matriz de diseño
y = train_y.SalePrice # vector respuesta

In [43]:

X.head()

Out[43]:

	Pave	AllPub	Y	MSSubClass	LotFrontage	LotArea	OverallQual	OverallCond	YearBuilt	YearRemodAdd	MasVnrArea	BsmtFinSF1	BsmtUnfSF	TotalBsmtSF	1stFlrSF	2ndFlrSF	GrLivArea	BsmtFullBath	BsmtHalfBath	FullBath	HalfBath	BedroomAbvGr	KitchenAbvGr	TotRmsAbvGrd	Fireplaces	GarageYrBlt	GarageCars	GarageArea	WoodDeckSF	OpenPorchSF	EnclosedPorch	MoSold	YrSold
0	1	1	1	60	-0.220799	8450	7	5	2003	2003	0.513928	706	150	856	856	854	1710	1	0	2	1	3	1	8	0	1.017250	2	548	0	61	0	2	2008
1	1	1	1	20	0.460162	9600	6	8	1976	1976	-0.570555	978	284	1262	1262	0	1262	0	1	2	0	3	1	6	1	-0.107890	2	460	298	0	0	5	2007
2	1	1	1	60	-0.084607	11250	7	5	2001	2002	0.325803	486	434	920	920	866	1786	1	0	2	1	3	1	6	1	0.933906	2	608	0	42	0	9	2008
3	1	1	1	70	-0.447787	9550	7	5	1915	1970	-0.570555	216	540	756	961	756	1717	1	0	1	0	3	1	7	1	0.808890	3	642	0	35	272	2	2006
4	1	1	1	60	0.641752	14260	8	5	2000	2000	1.366021	655	490	1145	1145	1053	2198	1	0	2	1	4	1	9	1	0.892234	3	836	192	84	0	12	2008

In [44]:

#Separación en Train y Test
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20) # 95% training and 5% test porque tengo un dataset de test aparte

Modelo 1: AdaBoost¶

In [45]:

from sklearn.ensemble import AdaBoostRegressor
ada=AdaBoostRegressor(n_estimators=12,learning_rate=0.1, loss='linear',random_state=42)

In [46]:

#Fiteamos el modelo
model = ada.fit(X_train, y_train)

In [48]:

X_train.head()

Out[48]:

	Pave	AllPub	Y	MSSubClass	LotFrontage	LotArea	OverallQual	OverallCond	YearBuilt	YearRemodAdd	MasVnrArea	BsmtFinSF1	BsmtUnfSF	TotalBsmtSF	1stFlrSF	2ndFlrSF	GrLivArea	BsmtFullBath	BsmtHalfBath	FullBath	HalfBath	BedroomAbvGr	KitchenAbvGr	TotRmsAbvGrd	Fireplaces	GarageYrBlt	GarageCars	GarageArea	WoodDeckSF	OpenPorchSF	EnclosedPorch	ScreenPorch	MiscVal	MoSold	YrSold
176	1	1	1	60	1.231918	10029	6	5	1988	1989	0.912309	831	320	1151	1164	896	2060	0	1	2	1	4	1	8	1	0.392172	2	521	0	228	0	192	0	9	2007
221	1	1	1	60	-0.039210	8068	6	5	2002	2002	-0.570555	0	1010	1010	1010	1257	2267	0	0	2	1	4	1	8	1	0.975578	2	390	120	46	0	0	0	12	2009
294	1	1	1	20	0.460162	9600	6	5	1953	1953	0.746317	1285	131	1416	1644	0	1644	1	0	1	0	3	1	7	2	-1.066342	2	418	110	0	0	0	0	10	2009
813	1	1	1	20	0.233175	9750	6	6	1958	1958	0.773982	608	834	1442	1442	0	1442	0	0	1	1	4	1	7	0	-0.857983	1	301	0	0	275	0	500	4	2007
85	1	1	1	60	2.321457	16059	8	5	1991	1992	1.000838	0	1288	1288	1301	1116	2417	0	0	2	1	4	1	9	1	0.517187	2	462	127	82	0	0	0	4	2006

In [49]:

X_test.head()

Out[49]:

	Pave	AllPub	Y	MSSubClass	LotFrontage	LotArea	OverallQual	OverallCond	YearBuilt	YearRemodAdd	MasVnrArea	BsmtFinSF1	BsmtUnfSF	TotalBsmtSF	1stFlrSF	2ndFlrSF	GrLivArea	BsmtFullBath	FullBath	HalfBath	BedroomAbvGr	KitchenAbvGr	TotRmsAbvGrd	Fireplaces	GarageYrBlt	GarageCars	GarageArea	WoodDeckSF	OpenPorchSF	EnclosedPorch	MoSold	YrSold
1092	1	1	1	50	-0.447787	8400	6	5	1925	1950	-0.570555	423	758	1181	1390	304	1694	0	2	0	4	1	7	1	-2.233154	2	576	342	0	128	6	2008
748	1	1	1	20	-0.493184	10593	7	5	1996	1996	1.299624	919	801	1720	1720	0	1720	1	2	0	3	1	7	1	0.725547	2	527	240	56	154	3	2010
430	1	1	1	160	-2.218286	1680	6	5	1971	1971	0.713118	387	96	483	483	504	987	0	1	1	2	1	4	0	-0.316249	1	264	0	0	0	7	2008
965	1	1	1	60	-0.220799	10237	6	5	2005	2007	-0.570555	0	783	783	783	701	1484	0	2	1	3	1	8	1	1.100593	2	393	0	72	0	7	2007
349	1	1	1	60	-0.629376	20431	9	5	2005	2006	4.243219	1410	438	1848	1848	880	2728	1	2	1	4	1	10	2	1.142265	3	706	0	0	0	4	2006

In [54]:

#Predicción!
y_pred = model.predict(X_test) # Sacar predicciones con test

In [56]:

from sklearn.metrics import r2_score
from sklearn.metrics import mean_squared_error
from sklearn.metrics import median_absolute_error
r2=r2_score(y_true=y_test, y_pred=y_pred)
print('r2: ', r2)
mse=mean_squared_error(y_true=y_test, y_pred=y_pred)
print('MSE: ',mse)
mae=median_absolute_error(y_true=y_test, y_pred=y_pred)
print('MAE: ',mae)

r2:  0.7367893518145634
MSE:  1648289558.8257012
MAE:  21582.873793242165

Modelo 2: GradientBoosting¶

In [57]:

#Ahora comparemos con GBM
from sklearn.ensemble import GradientBoostingRegressor
gbrt=GradientBoostingRegressor(loss='ls',learning_rate=0.1, n_estimators=50, subsample=0.8,max_depth=4,criterion='friedman_mse',
                               random_state=42)

In [58]:

#Fiteamos el modelo
model1 = gbrt.fit(X_train, y_train)

/usr/local/lib/python3.7/dist-packages/sklearn/ensemble/_gb.py:290: FutureWarning: The loss 'ls' was deprecated in v1.0 and will be removed in version 1.2. Use 'squared_error' which is equivalent.
  FutureWarning,

In [59]:

y_pred = model1.predict(X_test)

In [60]:

from sklearn.metrics import r2_score
from sklearn.metrics import mean_squared_error
from sklearn.metrics import median_absolute_error
r2=r2_score(y_true=y_test, y_pred=y_pred)
print('r2: ', r2)
mse=mean_squared_error(y_true=y_test, y_pred=y_pred)
print('MSE: ',mse)
mae=median_absolute_error(y_true=y_test, y_pred=y_pred)
print('MAE: ',mae)

r2:  0.911749740054561
MSE:  552644746.838815
MAE:  9685.009492501253

Modelo 3: LightGBM¶

In [61]:

import lightgbm as lgb #pip install lightgbm
clf = lgb.LGBMRegressor(boosting_type='gbdt',max_depth=4,num_leaves=20,learning_rate=0.01,n_estimators=100)
clf.fit(X_train, y_train)
y_pred=clf.predict(X_test)

In [62]:

from sklearn.metrics import r2_score
from sklearn.metrics import mean_squared_error
from sklearn.metrics import median_absolute_error
r2=r2_score(y_true=y_test, y_pred=y_pred)
print('r2: ', r2)
mse=mean_squared_error(y_true=y_test, y_pred=y_pred)
print('MSE: ',mse)
mae=median_absolute_error(y_true=y_test, y_pred=y_pred)
print('MAE: ',mae)

r2:  0.70613934811082
MSE:  1840227390.4868917
MAE:  20513.164854036193

Modelo 4: XGBoost¶

In [63]:

import pandas as pd
import xgboost as xgb
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
regressor = xgb.XGBRegressor(
    n_estimators=100,
    reg_lambda=1,
    gamma=0,
    max_depth=3
)

In [64]:

#Fiteamos
regressor.fit(X_train, y_train)

[00:24:17] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.

Out[64]:

XGBRegressor()

In [65]:

#Predecimos
y_pred = regressor.predict(X_test)

In [66]:

from sklearn.metrics import r2_score
from sklearn.metrics import mean_squared_error
from sklearn.metrics import median_absolute_error
r2=r2_score(y_true=y_test, y_pred=y_pred)
print('r2: ', r2)
mse=mean_squared_error(y_true=y_test, y_pred=y_pred)
print('MSE: ',mse)
mae=median_absolute_error(y_true=y_test, y_pred=y_pred)
print('MAE: ',mae)

r2:  0.891122802621978
MSE:  681815682.1146731
MAE:  9738.4609375

Ahora miremos que tan bien lo hace con el dataset que nos dieron¶

In [105]:

test.head()

Out[105]:

	Id	MSSubClass	MSZoning	LotFrontage	LotArea	Street	Alley	LotShape	LandContour	Utilities	LotConfig	LandSlope	Neighborhood	Condition1	Condition2	BldgType	HouseStyle	OverallQual	OverallCond	YearBuilt	YearRemodAdd	RoofStyle	RoofMatl	Exterior1st	Exterior2nd	MasVnrType	MasVnrArea	ExterQual	ExterCond	Foundation	BsmtQual	BsmtCond	BsmtExposure	BsmtFinType1	BsmtFinSF1	BsmtFinType2	BsmtFinSF2	BsmtUnfSF	TotalBsmtSF	Heating	HeatingQC	CentralAir	Electrical	1stFlrSF	2ndFlrSF	GrLivArea	FullBath	HalfBath	BedroomAbvGr	KitchenAbvGr	KitchenQual	TotRmsAbvGrd	Functional	Fireplaces	FireplaceQu	GarageType	GarageYrBlt	GarageFinish	GarageCars	GarageArea	GarageQual	GarageCond	PavedDrive	WoodDeckSF	OpenPorchSF	ScreenPorch	PoolQC	Fence	MiscFeature	MiscVal	MoSold	YrSold	SaleType	SaleCondition
0	1461	20	RH	80.0	11622	Pave	NaN	Reg	Lvl	AllPub	Inside	Gtl	NAmes	Feedr	Norm	1Fam	1Story	5	6	1961	1961	Gable	CompShg	VinylSd	VinylSd	None	0.0	TA	TA	CBlock	TA	TA	No	Rec	468.0	LwQ	144.0	270.0	882.0	GasA	TA	Y	SBrkr	896	0	896	1	0	2	1	TA	5	Typ	0	NaN	Attchd	1961.0	Unf	1.0	730.0	TA	TA	Y	140	0	120	NaN	MnPrv	NaN	0	6	2010	WD	Normal
1	1462	20	RL	81.0	14267	Pave	NaN	IR1	Lvl	AllPub	Corner	Gtl	NAmes	Norm	Norm	1Fam	1Story	6	6	1958	1958	Hip	CompShg	Wd Sdng	Wd Sdng	BrkFace	108.0	TA	TA	CBlock	TA	TA	No	ALQ	923.0	Unf	0.0	406.0	1329.0	GasA	TA	Y	SBrkr	1329	0	1329	1	1	3	1	Gd	6	Typ	0	NaN	Attchd	1958.0	Unf	1.0	312.0	TA	TA	Y	393	36	0	NaN	NaN	Gar2	12500	6	2010	WD	Normal
2	1463	60	RL	74.0	13830	Pave	NaN	IR1	Lvl	AllPub	Inside	Gtl	Gilbert	Norm	Norm	1Fam	2Story	5	5	1997	1998	Gable	CompShg	VinylSd	VinylSd	None	0.0	TA	TA	PConc	Gd	TA	No	GLQ	791.0	Unf	0.0	137.0	928.0	GasA	Gd	Y	SBrkr	928	701	1629	2	1	3	1	TA	6	Typ	1	TA	Attchd	1997.0	Fin	2.0	482.0	TA	TA	Y	212	34	0	NaN	MnPrv	NaN	0	3	2010	WD	Normal
3	1464	60	RL	78.0	9978	Pave	NaN	IR1	Lvl	AllPub	Inside	Gtl	Gilbert	Norm	Norm	1Fam	2Story	6	6	1998	1998	Gable	CompShg	VinylSd	VinylSd	BrkFace	20.0	TA	TA	PConc	TA	TA	No	GLQ	602.0	Unf	0.0	324.0	926.0	GasA	Ex	Y	SBrkr	926	678	1604	2	1	3	1	Gd	7	Typ	1	Gd	Attchd	1998.0	Fin	2.0	470.0	TA	TA	Y	360	36	0	NaN	NaN	NaN	0	6	2010	WD	Normal
4	1465	120	RL	43.0	5005	Pave	NaN	IR1	HLS	AllPub	Inside	Gtl	StoneBr	Norm	Norm	TwnhsE	1Story	8	5	1992	1992	Gable	CompShg	HdBoard	HdBoard	None	0.0	Gd	TA	PConc	Gd	TA	No	ALQ	263.0	Unf	0.0	1017.0	1280.0	GasA	Ex	Y	SBrkr	1280	0	1280	2	0	2	1	Gd	5	Typ	0	NaN	Attchd	1992.0	RFn	2.0	506.0	TA	TA	Y	0	82	144	NaN	NaN	NaN	0	1	2010	WD	Normal

Necesitamos aplicar la misma metodologia de limpieza que antes

In [106]:

col_del=['PoolQC','MiscFeature','Alley','Fence','Id']
col_inter= ['LotFrontage','GarageYrBlt','MasVnrArea','BsmtFinSF1','BsmtFinSF2','BsmtUnfSF','TotalBsmtSF','BsmtFullBath','BsmtHalfBath',\
            'GarageCars','GarageArea']
col_cat= ['FireplaceQu','GarageFinish','GarageCond','GarageType','BsmtExposure','BsmtFinType2','BsmtFinType1','BsmtCond',
          'BsmtQual','MasVnrType','Electrical']
test_x=test.drop(labels=col_del, axis=1)
for i in col_inter:
  test_x[i] = test_x[i].fillna(test_x[i].median())
for i in col_cat:
  test_x[i] = test_x[i].fillna('Desconocido')
alldata=[]
alldata1=[]
variables=[]
for i in test_x.columns:
  if test_x[i].dtype == 'O':
    if len(test_x[i].unique()) ==2:
      print('Variable 2 categorias:',i)
      y= pd.get_dummies(test_x[i])
      alldata.append(y)
      variables.append(i)
    else:
      pass
    print('--------------------')
    if len(test_x[i].unique()) >2:
      print('Variable >2 categorias:',i)
      z= pd.DataFrame()
      z[i]=test_x[i].astype('category').cat.codes
      alldata1.append(z)
      variables.append(i)
  if test_x[i].dtype == 'float64':
    test_x[i]=(test_x[i]-test_x[i].mean())/(test_x[i].std())
data_y=pd.concat(alldata, axis=1)
data_z=pd.concat(alldata1, axis=1)
test_y= pd.concat([data_y,data_z,test_x],axis=1) # Unir todos los datasets
test_y=test_y.drop(labels=variables, axis=1) # Borrar las columnas ya recodificadas
test_y.head()

--------------------
Variable >2 categorias: MSZoning
Variable 2 categorias: Street
--------------------
--------------------
Variable >2 categorias: LotShape
--------------------
Variable >2 categorias: LandContour
Variable 2 categorias: Utilities
--------------------
--------------------
Variable >2 categorias: LotConfig
--------------------
Variable >2 categorias: LandSlope
--------------------
Variable >2 categorias: Neighborhood
--------------------
Variable >2 categorias: Condition1
--------------------
Variable >2 categorias: Condition2
--------------------
Variable >2 categorias: BldgType
--------------------
Variable >2 categorias: HouseStyle
--------------------
Variable >2 categorias: RoofStyle
--------------------
Variable >2 categorias: RoofMatl
--------------------
Variable >2 categorias: Exterior1st
--------------------
Variable >2 categorias: Exterior2nd
--------------------
Variable >2 categorias: MasVnrType
--------------------
Variable >2 categorias: ExterQual
--------------------
Variable >2 categorias: ExterCond
--------------------
Variable >2 categorias: Foundation
--------------------
Variable >2 categorias: BsmtQual
--------------------
Variable >2 categorias: BsmtCond
--------------------
Variable >2 categorias: BsmtExposure
--------------------
Variable >2 categorias: BsmtFinType1
--------------------
Variable >2 categorias: BsmtFinType2
--------------------
Variable >2 categorias: Heating
--------------------
Variable >2 categorias: HeatingQC
Variable 2 categorias: CentralAir
--------------------
--------------------
Variable >2 categorias: Electrical
--------------------
Variable >2 categorias: KitchenQual
--------------------
Variable >2 categorias: Functional
--------------------
Variable >2 categorias: FireplaceQu
--------------------
Variable >2 categorias: GarageType
--------------------
Variable >2 categorias: GarageFinish
--------------------
Variable >2 categorias: GarageQual
--------------------
Variable >2 categorias: GarageCond
--------------------
Variable >2 categorias: PavedDrive
--------------------
Variable >2 categorias: SaleType
--------------------
Variable >2 categorias: SaleCondition

Out[106]:

	Pave	AllPub	Y	MSSubClass	LotFrontage	LotArea	OverallQual	OverallCond	YearBuilt	YearRemodAdd	MasVnrArea	BsmtFinSF1	BsmtFinSF2	BsmtUnfSF	TotalBsmtSF	1stFlrSF	2ndFlrSF	GrLivArea	BsmtFullBath	BsmtHalfBath	FullBath	HalfBath	BedroomAbvGr	KitchenAbvGr	TotRmsAbvGrd	Fireplaces	GarageYrBlt	GarageCars	GarageArea	WoodDeckSF	OpenPorchSF	ScreenPorch	MiscVal	MoSold	YrSold
0	1	1	1	20	0.567135	11622	5	6	1961	1961	-0.563123	0.063406	0.517359	-0.650238	-0.370589	896	0	896	-0.817788	-0.258071	1	0	2	1	5	0	-0.652883	-0.987850	1.185515	140	0	120	0	6	2010
1	1	1	1	20	0.615752	14267	6	6	1958	1958	0.047040	1.063147	-0.297587	-0.339109	0.639011	1329	0	1329	-0.817788	-0.258071	1	1	3	1	6	0	-0.769542	-0.987850	-0.740981	393	36	0	12500	6	2010
2	1	1	1	60	0.275437	13830	5	5	1997	1998	-0.563123	0.773112	-0.297587	-0.954504	-0.266693	928	701	1629	-0.817788	-0.258071	2	1	3	1	6	1	0.747020	0.301303	0.042522	212	34	0	0	3	2010
3	1	1	1	60	0.469903	9978	6	6	1998	1998	-0.450130	0.357835	-0.297587	-0.526701	-0.271210	926	678	1604	-0.817788	-0.258071	2	1	3	1	7	1	0.785906	0.301303	-0.012784	360	36	0	0	6	2010
4	1	1	1	120	-1.231670	5005	8	5	1992	1992	-0.563123	-0.387027	-0.297587	1.058685	0.528339	1280	0	1280	-0.817788	-0.258071	2	0	2	1	5	0	0.552589	0.301303	0.153134	0	82	144	0	1	2010

In [107]:

test_y.isna().sum()

Out[107]:

Grvl             0
Pave             0
AllPub           0
N                0
Y                0
MSSubClass       0
LotFrontage      0
LotArea          0
OverallQual      0
OverallCond      0
YearBuilt        0
YearRemodAdd     0
MasVnrArea       0
BsmtFinSF1       0
BsmtFinSF2       0
BsmtUnfSF        0
TotalBsmtSF      0
1stFlrSF         0
2ndFlrSF         0
LowQualFinSF     0
GrLivArea        0
BsmtFullBath     0
BsmtHalfBath     0
FullBath         0
HalfBath         0
BedroomAbvGr     0
KitchenAbvGr     0
TotRmsAbvGrd     0
Fireplaces       0
GarageYrBlt      0
GarageCars       0
GarageArea       0
WoodDeckSF       0
OpenPorchSF      0
EnclosedPorch    0
3SsnPorch        0
ScreenPorch      0
PoolArea         0
MiscVal          0
MoSold           0
YrSold           0
dtype: int64

In [108]:

len(test_y.columns)

Out[108]:

In [111]:

len(X_test.columns)

Out[111]:

In [110]:

test_y.columns

Out[110]:

Index(['Grvl', 'Pave', 'AllPub', 'N', 'Y', 'MSSubClass', 'LotFrontage',
       'LotArea', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd',
       'MasVnrArea', 'BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF',
       '1stFlrSF', '2ndFlrSF', 'LowQualFinSF', 'GrLivArea', 'BsmtFullBath',
       'BsmtHalfBath', 'FullBath', 'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr',
       'TotRmsAbvGrd', 'Fireplaces', 'GarageYrBlt', 'GarageCars', 'GarageArea',
       'WoodDeckSF', 'OpenPorchSF', 'EnclosedPorch', '3SsnPorch',
       'ScreenPorch', 'PoolArea', 'MiscVal', 'MoSold', 'YrSold'],
      dtype='object')

In [112]:

X_test.columns

Out[112]:

Index(['Grvl', 'Pave', 'AllPub', 'NoSeWa', 'N', 'Y', 'MSSubClass',
       'LotFrontage', 'LotArea', 'OverallQual', 'OverallCond', 'YearBuilt',
       'YearRemodAdd', 'MasVnrArea', 'BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF',
       'TotalBsmtSF', '1stFlrSF', '2ndFlrSF', 'LowQualFinSF', 'GrLivArea',
       'BsmtFullBath', 'BsmtHalfBath', 'FullBath', 'HalfBath', 'BedroomAbvGr',
       'KitchenAbvGr', 'TotRmsAbvGrd', 'Fireplaces', 'GarageYrBlt',
       'GarageCars', 'GarageArea', 'WoodDeckSF', 'OpenPorchSF',
       'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'MiscVal',
       'MoSold', 'YrSold'],
      dtype='object')

In [115]:

X_test.NoSeWa.value_counts() # Todo es cero entonces podemos crear una igual

Out[115]:

0    292
Name: NoSeWa, dtype: int64

In [117]:

test_y['NoSeWa']=0
test_y.head()

Out[117]:

	Pave	AllPub	Y	MSSubClass	LotFrontage	LotArea	OverallQual	OverallCond	YearBuilt	YearRemodAdd	MasVnrArea	BsmtFinSF1	BsmtFinSF2	BsmtUnfSF	TotalBsmtSF	1stFlrSF	2ndFlrSF	GrLivArea	BsmtFullBath	BsmtHalfBath	FullBath	HalfBath	BedroomAbvGr	KitchenAbvGr	TotRmsAbvGrd	Fireplaces	GarageYrBlt	GarageCars	GarageArea	WoodDeckSF	OpenPorchSF	ScreenPorch	MiscVal	MoSold	YrSold
0	1	1	1	20	0.567135	11622	5	6	1961	1961	-0.563123	0.063406	0.517359	-0.650238	-0.370589	896	0	896	-0.817788	-0.258071	1	0	2	1	5	0	-0.652883	-0.987850	1.185515	140	0	120	0	6	2010
1	1	1	1	20	0.615752	14267	6	6	1958	1958	0.047040	1.063147	-0.297587	-0.339109	0.639011	1329	0	1329	-0.817788	-0.258071	1	1	3	1	6	0	-0.769542	-0.987850	-0.740981	393	36	0	12500	6	2010
2	1	1	1	60	0.275437	13830	5	5	1997	1998	-0.563123	0.773112	-0.297587	-0.954504	-0.266693	928	701	1629	-0.817788	-0.258071	2	1	3	1	6	1	0.747020	0.301303	0.042522	212	34	0	0	3	2010
3	1	1	1	60	0.469903	9978	6	6	1998	1998	-0.450130	0.357835	-0.297587	-0.526701	-0.271210	926	678	1604	-0.817788	-0.258071	2	1	3	1	7	1	0.785906	0.301303	-0.012784	360	36	0	0	6	2010
4	1	1	1	120	-1.231670	5005	8	5	1992	1992	-0.563123	-0.387027	-0.297587	1.058685	0.528339	1280	0	1280	-0.817788	-0.258071	2	0	2	1	5	0	0.552589	0.301303	0.153134	0	82	144	0	1	2010

In [118]:

interseccion = list(set(test_y.columns) & set(X_test.columns))
len(interseccion)
union = list(set(test_y.columns) | set(X_test.columns))
set(test_y.columns) ^ set(X_test.columns) # Esta columna tiene un problema y nos cambia la dimension del dataset original

Out[118]:

set()

In [125]:

# Predecimos con GradientBoosting
y_pred = model.predict(test_y)
y_pred
df= pd.DataFrame()
df['Id']= test_y.index
df['Prediccion_Adaboost']= y_pred
y_pred1=gbrt.predict(test_y)
df['Prediccion_GradientBoosting']= y_pred1
y_pred2=clf.predict(test_y)
df['Prediccion_LightGBM']= y_pred2
df.head()

/usr/local/lib/python3.7/dist-packages/sklearn/base.py:488: FutureWarning: The feature names should match those that were passed during fit. Starting version 1.2, an error will be raised.
Feature names must be in the same order as they were in fit.

  warnings.warn(message, FutureWarning)
/usr/local/lib/python3.7/dist-packages/sklearn/base.py:488: FutureWarning: The feature names should match those that were passed during fit. Starting version 1.2, an error will be raised.
Feature names must be in the same order as they were in fit.

  warnings.warn(message, FutureWarning)

Out[125]:

	Id	Prediccion_Adaboost	Prediccion_GradientBoosting	Prediccion_LightGBM
0	0	124989.680288	102220.681189	139929.143521
1	1	124989.680288	127633.610369	153849.447869
2	2	124989.680288	117111.617785	141688.350363
3	3	124989.680288	120649.428563	145765.584356
4	4	124989.680288	131458.061514	151059.186036

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