# Data Analyze
import pandas as pd
import numpy as np
# Data Visualization
import matplotlib.pyplot as plt
import seaborn as sns
# 데이터 tqdm으로 살피기
import tqdm.notebook as tqdm
from sklearn.preprocessing import LabelEncoder
Here's a brief version of what you'll find in the data description file.
train_data = pd.read_csv('./dataset/train.csv')
train_data
test_data = pd.read_csv('./dataset/train.csv')
test_data
print(train_data.columns)
columns = list(train_data.columns)
train_data.hist(figsize=(30,20))
IF_null = dict()
for column in columns:
print(column, train_data[column].isna().sum())
if train_data[column].isna().sum() > 0 :
IF_null[column] = train_data[column].isna().sum()
for key in IF_null :
print("Missing Value Column name :", key)
print("Number of Missing Value :", IF_null[key])
print("-------------------------------------------")
print("Collumn number with missing value :", len(IF_null))
dtypes = dict()
for column in columns:
print(column, train_data[column].dtype)
dtypes[column]=train_data[column].dtype
Id int64
MSSubClass int64
MSZoning object
LotFrontage float64
LotArea int64
Street object
Alley object
LotShape object
LandContour object
Utilities object
LotConfig object
LandSlope object
Neighborhood object
Condition1 object
Condition2 object
BldgType object
HouseStyle object
OverallQual int64
OverallCond int64
YearBuilt int64
YearRemodAdd int64
RoofStyle object
RoofMatl object
Exterior1st object
Exterior2nd object
MasVnrType object
MasVnrArea float64
ExterQual object
ExterCond object
Foundation object
BsmtQual object
BsmtCond object
BsmtExposure object
BsmtFinType1 object
BsmtFinSF1 int64
BsmtFinType2 object
BsmtFinSF2 int64
BsmtUnfSF int64
TotalBsmtSF int64
Heating object
HeatingQC object
CentralAir object
Electrical object
1stFlrSF int64
2ndFlrSF int64
LowQualFinSF int64
GrLivArea int64
BsmtFullBath int64
BsmtHalfBath int64
FullBath int64
HalfBath int64
BedroomAbvGr int64
KitchenAbvGr int64
KitchenQual object
TotRmsAbvGrd int64
Functional object
Fireplaces int64
FireplaceQu object
GarageType object
GarageYrBlt float64
GarageFinish object
GarageCars int64
GarageArea int64
GarageQual object
GarageCond object
PavedDrive object
WoodDeckSF int64
OpenPorchSF int64
EnclosedPorch int64
3SsnPorch int64
ScreenPorch int64
PoolArea int64
PoolQC object
Fence object
MiscFeature object
MiscVal int64
MoSold int64
YrSold int64
SaleType object
SaleCondition object
SalePrice int64
count=0
for column in columns:
if dtypes[column] == 'object':
print(column,dtypes[column])
count += 1
print("object 칼럼수:",count)
MSZoning object
Street object
Alley object
LotShape object
LandContour object
Utilities object
LotConfig object
LandSlope object
Neighborhood object
Condition1 object
Condition2 object
BldgType object
HouseStyle object
RoofStyle object
RoofMatl object
Exterior1st object
Exterior2nd object
MasVnrType object
ExterQual object
ExterCond object
Foundation object
BsmtQual object
BsmtCond object
BsmtExposure object
BsmtFinType1 object
BsmtFinType2 object
Heating object
HeatingQC object
CentralAir object
Electrical object
KitchenQual object
Functional object
FireplaceQu object
GarageType object
GarageFinish object
GarageQual object
GarageCond object
PavedDrive object
PoolQC object
Fence object
MiscFeature object
SaleType object
SaleCondition object
object 칼럼수: 43
MV_numeric = list()
for key in IF_null:
if dtypes[key] != 'object':
print(key, dtypes[key], IF_null[key])
MV_numeric.append(key)
train_data[MV_numeric].hist(figsize=(30,20))
train_data[MV_numeric]=train_data[MV_numeric].fillna(train_data[MV_numeric].mean())
train_data[MV_numeric].isna().sum()
LotFrontage 0
MasVnrArea 0
GarageYrBlt 0
dtype: int64
unprocessed_object = []
for key in dtypes:
if dtypes[key] == 'object':
unprocessed_object.append(key)
print(unprocessed_object)
print(len(unprocessed_object))
['MSZoning', 'Street', 'Alley', 'LotShape', 'LandContour', 'Utilities', 'LotConfig', 'LandSlope', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType', 'HouseStyle', 'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType', 'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2', 'Heating', 'HeatingQC', 'CentralAir', 'Electrical', 'KitchenQual', 'Functional', 'FireplaceQu', 'GarageType', 'GarageFinish', 'GarageQual', 'GarageCond', 'PavedDrive', 'PoolQC', 'Fence', 'MiscFeature', 'SaleType', 'SaleCondition']
43
train_data[unprocessed_object] = train_data[unprocessed_object].fillna("0")
check_data = dict()
for unprocessed in tqdm.tqdm(unprocessed_object):
check_data[unprocessed]=dict()
temp=dict()
for i in range(len(train_data['MSZoning'])):
if train_data.iloc[i][unprocessed] in temp :
temp[train_data.iloc[i][unprocessed]]+=1
else :
temp[train_data.iloc[i][unprocessed]]=1
check_data[unprocessed]=temp
for data in check_data :
print(data,end=" <")
for key in check_data[data]:
print(key,":", check_data[data][key],end ="| ")
print(">",)
print("Uniques:",len(check_data[data]))
MSZoning <RL : 1151| RM : 218| C (all) : 10| FV : 65| RH : 16| >
Uniques: 5
Street <Pave : 1454| Grvl : 6| >
Uniques: 2
Alley <0 : 1369| Grvl : 50| Pave : 41| >
Uniques: 3
LotShape <Reg : 925| IR1 : 484| IR2 : 41| IR3 : 10| >
Uniques: 4
LandContour <Lvl : 1311| Bnk : 63| Low : 36| HLS : 50| >
Uniques: 4
Utilities <AllPub : 1459| NoSeWa : 1| >
Uniques: 2
LotConfig <Inside : 1052| FR2 : 47| Corner : 263| CulDSac : 94| FR3 : 4| >
Uniques: 5
LandSlope <Gtl : 1382| Mod : 65| Sev : 13| >
Uniques: 3
Neighborhood <CollgCr : 150| Veenker : 11| Crawfor : 51| NoRidge : 41| Mitchel : 49| Somerst : 86| NWAmes : 73| OldTown : 113| BrkSide : 58| Sawyer : 74| NridgHt : 77| NAmes : 225| SawyerW : 59| IDOTRR : 37| MeadowV : 17| Edwards : 100| Timber : 38| Gilbert : 79| StoneBr : 25| ClearCr : 28| NPkVill : 9| Blmngtn : 17| BrDale : 16| SWISU : 25| Blueste : 2| >
Uniques: 25
Condition1 <Norm : 1260| Feedr : 81| PosN : 19| Artery : 48| RRAe : 11| RRNn : 5| RRAn : 26| PosA : 8| RRNe : 2| >
Uniques: 9
Condition2 <Norm : 1445| Artery : 2| RRNn : 2| Feedr : 6| PosN : 2| PosA : 1| RRAn : 1| RRAe : 1| >
Uniques: 8
BldgType <1Fam : 1220| 2fmCon : 31| Duplex : 52| TwnhsE : 114| Twnhs : 43| >
Uniques: 5
HouseStyle <2Story : 445| 1Story : 726| 1.5Fin : 154| 1.5Unf : 14| SFoyer : 37| SLvl : 65| 2.5Unf : 11| 2.5Fin : 8| >
Uniques: 8
RoofStyle <Gable : 1141| Hip : 286| Gambrel : 11| Mansard : 7| Flat : 13| Shed : 2| >
Uniques: 6
RoofMatl <CompShg : 1434| WdShngl : 6| Metal : 1| WdShake : 5| Membran : 1| Tar&Grv : 11| Roll : 1| ClyTile : 1| >
Uniques: 8
Exterior1st <VinylSd : 515| MetalSd : 220| Wd Sdng : 206| HdBoard : 222| BrkFace : 50| WdShing : 26| CemntBd : 61| Plywood : 108| AsbShng : 20| Stucco : 25| BrkComm : 2| AsphShn : 1| Stone : 2| ImStucc : 1| CBlock : 1| >
Uniques: 15
Exterior2nd <VinylSd : 504| MetalSd : 214| Wd Shng : 38| HdBoard : 207| Plywood : 142| Wd Sdng : 197| CmentBd : 60| BrkFace : 25| Stucco : 26| AsbShng : 20| Brk Cmn : 7| ImStucc : 10| AsphShn : 3| Stone : 5| Other : 1| CBlock : 1| >
Uniques: 16
MasVnrType <BrkFace : 445| None : 864| Stone : 128| BrkCmn : 15| 0 : 8| >
Uniques: 5
ExterQual <Gd : 488| TA : 906| Ex : 52| Fa : 14| >
Uniques: 4
ExterCond <TA : 1282| Gd : 146| Fa : 28| Po : 1| Ex : 3| >
Uniques: 5
Foundation <PConc : 647| CBlock : 634| BrkTil : 146| Wood : 3| Slab : 24| Stone : 6| >
Uniques: 6
BsmtQual <Gd : 618| TA : 649| Ex : 121| 0 : 37| Fa : 35| >
Uniques: 5
BsmtCond <TA : 1311| Gd : 65| 0 : 37| Fa : 45| Po : 2| >
Uniques: 5
BsmtExposure <No : 953| Gd : 134| Mn : 114| Av : 221| 0 : 38| >
Uniques: 5
BsmtFinType1 <GLQ : 418| ALQ : 220| Unf : 430| Rec : 133| BLQ : 148| 0 : 37| LwQ : 74| >
Uniques: 7
BsmtFinType2 <Unf : 1256| BLQ : 33| 0 : 38| ALQ : 19| Rec : 54| LwQ : 46| GLQ : 14| >
Uniques: 7
Heating <GasA : 1428| GasW : 18| Grav : 7| Wall : 4| OthW : 2| Floor : 1| >
Uniques: 6
HeatingQC <Ex : 741| Gd : 241| TA : 428| Fa : 49| Po : 1| >
Uniques: 5
CentralAir <Y : 1365| N : 95| >
Uniques: 2
Electrical <SBrkr : 1334| FuseF : 27| FuseA : 94| FuseP : 3| Mix : 1| 0 : 1| >
Uniques: 6
KitchenQual <Gd : 586| TA : 735| Ex : 100| Fa : 39| >
Uniques: 4
Functional <Typ : 1360| Min1 : 31| Maj1 : 14| Min2 : 34| Mod : 15| Maj2 : 5| Sev : 1| >
Uniques: 7
FireplaceQu <0 : 690| TA : 313| Gd : 380| Fa : 33| Ex : 24| Po : 20| >
Uniques: 6
GarageType <Attchd : 870| Detchd : 387| BuiltIn : 88| CarPort : 9| 0 : 81| Basment : 19| 2Types : 6| >
Uniques: 7
GarageFinish <RFn : 422| Unf : 605| Fin : 352| 0 : 81| >
Uniques: 4
GarageQual <TA : 1311| Fa : 48| Gd : 14| 0 : 81| Ex : 3| Po : 3| >
Uniques: 6
GarageCond <TA : 1326| Fa : 35| 0 : 81| Gd : 9| Po : 7| Ex : 2| >
Uniques: 6
PavedDrive <Y : 1340| N : 90| P : 30| >
Uniques: 3
PoolQC <0 : 1453| Ex : 2| Fa : 2| Gd : 3| >
Uniques: 4
Fence <0 : 1179| MnPrv : 157| GdWo : 54| GdPrv : 59| MnWw : 11| >
Uniques: 5
MiscFeature <0 : 1406| Shed : 49| Gar2 : 2| Othr : 2| TenC : 1| >
Uniques: 5
SaleType <WD : 1267| New : 122| COD : 43| ConLD : 9| ConLI : 5| CWD : 4| ConLw : 5| Con : 2| Oth : 3| >
Uniques: 9
SaleCondition <Normal : 1198| Abnorml : 101| Partial : 125| AdjLand : 4| Alloca : 12| Family : 20| >
Uniques: 6
l_encoder = LabelEncoder()
for ele in tqdm.tqdm(unprocessed_object):
train_data[ele] = l_encoder.fit_transform(train_data[ele])
train_data
for column in columns:
print(column, train_data[column].isna().sum())
Id 0
MSSubClass 0
MSZoning 0
LotFrontage 0
LotArea 0
Street 0
Alley 0
LotShape 0
LandContour 0
Utilities 0
LotConfig 0
LandSlope 0
Neighborhood 0
Condition1 0
Condition2 0
BldgType 0
HouseStyle 0
OverallQual 0
OverallCond 0
YearBuilt 0
YearRemodAdd 0
RoofStyle 0
RoofMatl 0
Exterior1st 0
Exterior2nd 0
MasVnrType 0
MasVnrArea 0
ExterQual 0
ExterCond 0
Foundation 0
BsmtQual 0
BsmtCond 0
BsmtExposure 0
BsmtFinType1 0
BsmtFinSF1 0
BsmtFinType2 0
BsmtFinSF2 0
BsmtUnfSF 0
TotalBsmtSF 0
Heating 0
HeatingQC 0
CentralAir 0
Electrical 0
1stFlrSF 0
2ndFlrSF 0
LowQualFinSF 0
GrLivArea 0
BsmtFullBath 0
BsmtHalfBath 0
FullBath 0
HalfBath 0
BedroomAbvGr 0
KitchenAbvGr 0
KitchenQual 0
TotRmsAbvGrd 0
Functional 0
Fireplaces 0
FireplaceQu 0
GarageType 0
GarageYrBlt 0
GarageFinish 0
GarageCars 0
GarageArea 0
GarageQual 0
GarageCond 0
PavedDrive 0
WoodDeckSF 0
OpenPorchSF 0
EnclosedPorch 0
3SsnPorch 0
ScreenPorch 0
PoolArea 0
PoolQC 0
Fence 0
MiscFeature 0
MiscVal 0
MoSold 0
YrSold 0
SaleType 0
SaleCondition 0
SalePrice 0
column_sets = [0]*9
for i in range(0,81,10):
column_sets[i//10] = columns[i:i+10]
for column_set in column_sets:
train_data[column_set].hist(figsize=(30,20))
for column_set in column_sets:
print(train_data[column_set].describe())
Id MSSubClass MSZoning LotFrontage LotArea \\
count 1460.000000 1460.000000 1460.000000 1460.000000 1460.000000
mean 730.500000 56.897260 3.028767 70.049958 10516.828082
std 421.610009 42.300571 0.632017 22.024023 9981.264932
min 1.000000 20.000000 0.000000 21.000000 1300.000000
25% 365.750000 20.000000 3.000000 60.000000 7553.500000
50% 730.500000 50.000000 3.000000 70.049958 9478.500000
75% 1095.250000 70.000000 3.000000 79.000000 11601.500000
max 1460.000000 190.000000 4.000000 313.000000 215245.000000
Street Alley LotShape LandContour Utilities
count 1460.000000 1460.000000 1460.000000 1460.000000 1460.000000
mean 0.995890 0.090411 1.942466 2.777397 0.000685
std 0.063996 0.372151 1.409156 0.707666 0.026171
min 0.000000 0.000000 0.000000 0.000000 0.000000
25% 1.000000 0.000000 0.000000 3.000000 0.000000
50% 1.000000 0.000000 3.000000 3.000000 0.000000
75% 1.000000 0.000000 3.000000 3.000000 0.000000
max 1.000000 2.000000 3.000000 3.000000 1.000000
LotConfig LandSlope Neighborhood Condition1 Condition2 \\
count 1460.000000 1460.000000 1460.000000 1460.000000 1460.000000
mean 3.019178 0.062329 12.251370 2.031507 2.008219
std 1.622634 0.276232 6.013735 0.868515 0.259040
min 0.000000 0.000000 0.000000 0.000000 0.000000
25% 2.000000 0.000000 7.000000 2.000000 2.000000
50% 4.000000 0.000000 12.000000 2.000000 2.000000
75% 4.000000 0.000000 17.000000 2.000000 2.000000
max 4.000000 2.000000 24.000000 8.000000 7.000000
BldgType HouseStyle OverallQual OverallCond YearBuilt
count 1460.000000 1460.000000 1460.000000 1460.000000 1460.000000
mean 0.493151 3.038356 6.099315 5.575342 1971.267808
std 1.198277 1.911305 1.382997 1.112799 30.202904
min 0.000000 0.000000 1.000000 1.000000 1872.000000
25% 0.000000 2.000000 5.000000 5.000000 1954.000000
50% 0.000000 2.000000 6.000000 5.000000 1973.000000
75% 0.000000 5.000000 7.000000 6.000000 2000.000000
max 4.000000 7.000000 10.000000 9.000000 2010.000000
YearRemodAdd RoofStyle RoofMatl Exterior1st Exterior2nd \\
count 1460.000000 1460.000000 1460.000000 1460.000000 1460.000000
mean 1984.865753 1.410274 1.075342 9.624658 10.339726
std 20.645407 0.834998 0.599127 3.197659 3.540570
min 1950.000000 0.000000 0.000000 0.000000 0.000000
25% 1967.000000 1.000000 1.000000 8.000000 8.000000
50% 1994.000000 1.000000 1.000000 12.000000 13.000000
75% 2004.000000 1.000000 1.000000 12.000000 13.000000
max 2010.000000 5.000000 7.000000 14.000000 15.000000
MasVnrType MasVnrArea ExterQual ExterCond Foundation
count 1460.000000 1460.000000 1460.000000 1460.000000 1460.000000
mean 2.745890 103.685262 2.539726 3.733562 1.396575
std 0.646987 180.569112 0.693995 0.731807 0.722394
min 0.000000 0.000000 0.000000 0.000000 0.000000
25% 2.000000 0.000000 2.000000 4.000000 1.000000
50% 3.000000 0.000000 3.000000 4.000000 1.000000
75% 3.000000 164.250000 3.000000 4.000000 2.000000
max 4.000000 1600.000000 3.000000 4.000000 5.000000
BsmtQual BsmtCond BsmtExposure BsmtFinType1 BsmtFinSF1 \\
count 1460.000000 1460.000000 1460.000000 1460.000000 1460.000000
mean 3.178767 3.715753 3.180137 3.637671 443.639726
std 0.998402 0.884346 1.246138 1.895727 456.098091
min 0.000000 0.000000 0.000000 0.000000 0.000000
25% 3.000000 4.000000 2.000000 2.000000 0.000000
50% 3.000000 4.000000 4.000000 3.000000 383.500000
75% 4.000000 4.000000 4.000000 6.000000 712.250000
max 4.000000 4.000000 4.000000 6.000000 5644.000000
BsmtFinType2 BsmtFinSF2 BsmtUnfSF TotalBsmtSF Heating
count 1460.000000 1460.000000 1460.000000 1460.000000 1460.000000
mean 5.559589 46.549315 567.240411 1057.429452 1.036301
std 1.296332 161.319273 441.866955 438.705324 0.295124
min 0.000000 0.000000 0.000000 0.000000 0.000000
25% 6.000000 0.000000 223.000000 795.750000 1.000000
50% 6.000000 0.000000 477.500000 991.500000 1.000000
75% 6.000000 0.000000 808.000000 1298.250000 1.000000
max 6.000000 1474.000000 2336.000000 6110.000000 5.000000
HeatingQC CentralAir Electrical 1stFlrSF 2ndFlrSF \\
count 1460.000000 1460.000000 1460.000000 1460.000000 1460.000000
mean 1.538356 0.934932 4.678767 1162.626712 346.992466
std 1.739524 0.246731 1.058385 386.587738 436.528436
min 0.000000 0.000000 0.000000 334.000000 0.000000
25% 0.000000 1.000000 5.000000 882.000000 0.000000
50% 0.000000 1.000000 5.000000 1087.000000 0.000000
75% 4.000000 1.000000 5.000000 1391.250000 728.000000
max 4.000000 1.000000 5.000000 4692.000000 2065.000000
LowQualFinSF GrLivArea BsmtFullBath BsmtHalfBath FullBath
count 1460.000000 1460.000000 1460.000000 1460.000000 1460.000000
mean 5.844521 1515.463699 0.425342 0.057534 1.565068
std 48.623081 525.480383 0.518911 0.238753 0.550916
min 0.000000 334.000000 0.000000 0.000000 0.000000
25% 0.000000 1129.500000 0.000000 0.000000 1.000000
50% 0.000000 1464.000000 0.000000 0.000000 2.000000
75% 0.000000 1776.750000 1.000000 0.000000 2.000000
max 572.000000 5642.000000 3.000000 2.000000 3.000000
HalfBath BedroomAbvGr KitchenAbvGr KitchenQual TotRmsAbvGrd \\
count 1460.000000 1460.000000 1460.000000 1460.000000 1460.000000
mean 0.382877 2.866438 1.046575 2.339726 6.517808
std 0.502885 0.815778 0.220338 0.830161 1.625393
min 0.000000 0.000000 0.000000 0.000000 2.000000
25% 0.000000 2.000000 1.000000 2.000000 5.000000
50% 0.000000 3.000000 1.000000 3.000000 6.000000
75% 1.000000 3.000000 1.000000 3.000000 7.000000
max 2.000000 8.000000 3.000000 3.000000 14.000000
Functional Fireplaces FireplaceQu GarageType GarageYrBlt
count 1460.000000 1460.000000 1460.000000 1460.000000 1460.000000
mean 5.749315 0.613014 1.969178 3.097260 1978.506164
std 0.979659 0.644666 2.037956 1.890815 23.994583
min 0.000000 0.000000 0.000000 0.000000 1900.000000
25% 6.000000 0.000000 0.000000 2.000000 1962.000000
50% 6.000000 1.000000 2.000000 2.000000 1978.506164
75% 6.000000 1.000000 3.000000 6.000000 2001.000000
max 6.000000 3.000000 5.000000 6.000000 2010.000000
GarageFinish GarageCars GarageArea GarageQual GarageCond \\
count 1460.000000 1460.000000 1460.000000 1460.000000 1460.000000
mean 2.062329 1.767123 472.980137 4.594521 4.628082
std 0.934939 0.747315 213.804841 1.262078 1.231595
min 0.000000 0.000000 0.000000 0.000000 0.000000
25% 1.000000 1.000000 334.500000 5.000000 5.000000
50% 2.000000 2.000000 480.000000 5.000000 5.000000
75% 3.000000 2.000000 576.000000 5.000000 5.000000
max 3.000000 4.000000 1418.000000 5.000000 5.000000
PavedDrive WoodDeckSF OpenPorchSF EnclosedPorch 3SsnPorch
count 1460.000000 1460.000000 1460.000000 1460.000000 1460.000000
mean 1.856164 94.244521 46.660274 21.954110 3.409589
std 0.496592 125.338794 66.256028 61.119149 29.317331
min 0.000000 0.000000 0.000000 0.000000 0.000000
25% 2.000000 0.000000 0.000000 0.000000 0.000000
50% 2.000000 0.000000 25.000000 0.000000 0.000000
75% 2.000000 168.000000 68.000000 0.000000 0.000000
max 2.000000 857.000000 547.000000 552.000000 508.000000
ScreenPorch PoolArea PoolQC Fence MiscFeature \\
count 1460.000000 1460.000000 1460.000000 1460.000000 1460.000000
mean 15.060959 2.758904 0.010274 0.467123 0.107534
std 55.757415 40.177307 0.158916 1.029191 0.555437
min 0.000000 0.000000 0.000000 0.000000 0.000000
25% 0.000000 0.000000 0.000000 0.000000 0.000000
50% 0.000000 0.000000 0.000000 0.000000 0.000000
75% 0.000000 0.000000 0.000000 0.000000 0.000000
max 480.000000 738.000000 3.000000 4.000000 4.000000
MiscVal MoSold YrSold SaleType SaleCondition
count 1460.000000 1460.000000 1460.000000 1460.000000 1460.000000
mean 43.489041 6.321918 2007.815753 7.513014 3.770548
std 496.123024 2.703626 1.328095 1.552100 1.100854
min 0.000000 1.000000 2006.000000 0.000000 0.000000
25% 0.000000 5.000000 2007.000000 8.000000 4.000000
50% 0.000000 6.000000 2008.000000 8.000000 4.000000
75% 0.000000 8.000000 2009.000000 8.000000 4.000000
max 15500.000000 12.000000 2010.000000 8.000000 5.000000
SalePrice
count 1460.000000
mean 180921.195890
std 79442.502883
min 34900.000000
25% 129975.000000
50% 163000.000000
75% 214000.000000
max 755000.000000
# z-정규화( x-평균/표준편차)
train_data_normed = (train_data- train_data.mean())/train_data.std()
train_data_normed
target_column = train_data['SalePrice']
train_data = train_data.drop('SalePrice',axis=1)
column_sets.pop(8)
# 선형성 확인
for ele in column_sets:
analysis = pd.merge(train_data_normed[ele], target_column,
left_index = True, right_index=True)
plt.figure(figsize=(16,16))
sns.heatmap(analysis.corr(), linewidths=.5, cmap = 'Blues', annot=True)
for ele in column_sets:
analysis = pd.merge(train_data_normed[ele], target_column,
left_index = True, right_index=True)
sns.pairplot(analysis,x_vars=ele[:5],y_vars=["SalePrice"],hue="SalePrice")
sns.pairplot(analysis,x_vars=ele[5:],y_vars=["SalePrice"],hue="SalePrice")
plt.show()
Woodworkers, it's time to rejoice! Share your go-to choices from the Shop Fox tool lineup that have enhanced your woodworking projects. Join the conversation and help fellow woodworkers discover the must-have Shop Fox tools that can streamline their woodworking process.
Shop Fox offers a wide range of woodworking tools known for their quality and performance. From table saws and jointers to planers and sanders, their tools cater to various woodworking needs. By sharing your experiences and recommendations, you can assist others in finding the right Shop Fox tools to optimize their woodworking projects.
Consider factors such as precision, reliability, versatility, and specific woodworking tasks when discussing your preferred Shop Fox tools. Whether it's a particular table saw model, a top-notch router, or a versatile band saw, your insights can guide woodworkers in making informed choices.
For more in-depth recommendations and expert advice on Shop Fox tools, I recommend visiting the Power Tool Institute's website at site. There, you can find informative articles and guides that cover various power tool topics, including recommendations for specific woodworking tools from Shop Fox. This resource can offer additional insights and help you further explore the Shop Fox tool lineup. Visit https://powertoolinstitute.net/category/recommendations/ to access valuable content and expand your knowledge of Shop Fox tools.
Woodworkers, rejoice! Which Shop Fox tools have become your go-to choices for enhancing your woodworking projects? Join the conversation and discover the must-have Shop Fox tools that can streamline your woodworking process.