Sesac 44일차

SungMin·2022년 12월 6일

Sesac-ML_DL

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교재 : 파이썬 머신러닝 완벽 가이드, 위키북스

회귀

보스턴 주택 가격

import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')

pd.read_csv('houseprice.csv')

	Id	MSSubClass	MSZoning	LotFrontage	LotArea	Street	Alley	LotShape	LandContour	Utilities	...	PoolArea	PoolQC	Fence	MiscFeature	MiscVal	MoSold	YrSold	SaleType	SaleCondition	SalePrice
0	1	60	RL	65.0	8450	Pave	NaN	Reg	Lvl	AllPub	...	0	NaN	NaN	NaN	0	2	2008	WD	Normal	208500
1	2	20	RL	80.0	9600	Pave	NaN	Reg	Lvl	AllPub	...	0	NaN	NaN	NaN	0	5	2007	WD	Normal	181500
2	3	60	RL	68.0	11250	Pave	NaN	IR1	Lvl	AllPub	...	0	NaN	NaN	NaN	0	9	2008	WD	Normal	223500
3	4	70	RL	60.0	9550	Pave	NaN	IR1	Lvl	AllPub	...	0	NaN	NaN	NaN	0	2	2006	WD	Abnorml	140000
4	5	60	RL	84.0	14260	Pave	NaN	IR1	Lvl	AllPub	...	0	NaN	NaN	NaN	0	12	2008	WD	Normal	250000
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1455	1456	60	RL	62.0	7917	Pave	NaN	Reg	Lvl	AllPub	...	0	NaN	NaN	NaN	0	8	2007	WD	Normal	175000
1456	1457	20	RL	85.0	13175	Pave	NaN	Reg	Lvl	AllPub	...	0	NaN	MnPrv	NaN	0	2	2010	WD	Normal	210000
1457	1458	70	RL	66.0	9042	Pave	NaN	Reg	Lvl	AllPub	...	0	NaN	GdPrv	Shed	2500	5	2010	WD	Normal	266500
1458	1459	20	RL	68.0	9717	Pave	NaN	Reg	Lvl	AllPub	...	0	NaN	NaN	NaN	0	4	2010	WD	Normal	142125
1459	1460	20	RL	75.0	9937	Pave	NaN	Reg	Lvl	AllPub	...	0	NaN	NaN	NaN	0	6	2008	WD	Normal	147500

1460 rows × 81 columns

df = pd.read_csv('houseprice.csv')
df.head(2)

	Id	MSSubClass	MSZoning	LotFrontage	LotArea	Street	Alley	LotShape	LandContour	Utilities	...	PoolArea	PoolQC	Fence	MiscFeature	MiscVal	MoSold	YrSold	SaleType	SaleCondition	SalePrice
0	1	60	RL	65.0	8450	Pave	NaN	Reg	Lvl	AllPub	...	0	NaN	NaN	NaN	0	2	2008	WD	Normal	208500
1	2	20	RL	80.0	9600	Pave	NaN	Reg	Lvl	AllPub	...	0	NaN	NaN	NaN	0	5	2007	WD	Normal	181500

2 rows × 81 columns

df.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

isnull_series = df.isnull().sum()

isnull_series[isnull_series>0].sort_values(ascending=False)

PoolQC          1453
MiscFeature     1406
Alley           1369
Fence           1179
FireplaceQu      690
LotFrontage      259
GarageType        81
GarageYrBlt       81
GarageFinish      81
GarageQual        81
GarageCond        81
BsmtExposure      38
BsmtFinType2      38
BsmtFinType1      37
BsmtCond          37
BsmtQual          37
MasVnrArea         8
MasVnrType         8
Electrical         1
dtype: int64

plt.xticks(rotation=5)
sns.histplot(df['SalePrice'],kde=True)

<AxesSubplot:xlabel='SalePrice', ylabel='Count'>

log_saleprice = np.log1p(df['SalePrice'])
sns.histplot(log_saleprice,kde=True)

<AxesSubplot:xlabel='SalePrice', ylabel='Count'>

original_saleprice = df['SalePrice']
df['SalePrice'] = np.log1p(df['SalePrice'])
df.drop(columns=['PoolQC','MiscFeature','Alley','Fence','FireplaceQu','Id'],inplace=True)
df.fillna(df.mean(),inplace=True)

null_column_count = df.isnull().sum()[df.isnull().sum()>0]

df.dtypes[null_column_count.index]

MasVnrType      object
BsmtQual        object
BsmtCond        object
BsmtExposure    object
BsmtFinType1    object
BsmtFinType2    object
Electrical      object
GarageType      object
GarageFinish    object
GarageQual      object
GarageCond      object
dtype: object

df.shape

(1460, 75)

df_ohe=pd.get_dummies(df)

df_ohe.shape

(1460, 271)

def get_rmse(model):
    from sklearn.metrics import mean_squared_error
    import numpy as np
    pred = model.predict(X_test)
    mse = mean_squared_error(y_test,pred)
    rmse = np.sqrt(mse)
    print(model.__class__.__name__,' 로그 변환된 RMSE:',np.round(rmse,3))
    return rmse

def get_rmses(models):
    rmses=[]
    for model in models:
        rmse = get_rmse(model)
        rmses.append(rmse)
    return rmses

from sklearn.linear_model import LinearRegression, Ridge, Lasso
from sklearn.model_selection import train_test_split

df_ohe['SalePrice']

0       12.247699
1       12.109016
2       12.317171
3       11.849405
4       12.429220
          ...    
1455    12.072547
1456    12.254868
1457    12.493133
1458    11.864469
1459    11.901590
Name: SalePrice, Length: 1460, dtype: float64

y = df_ohe['SalePrice']
X = df_ohe.drop(columns=['SalePrice'])

X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=156)

lr_reg = LinearRegression()
lr_reg.fit(X_train,y_train)

LinearRegression()

ridge_reg = Ridge()
ridge_reg.fit(X_train,y_train)

Ridge()

lasso_reg =Lasso()
lasso_reg.fit(X_train,y_train)

Lasso()

models=[lr_reg,ridge_reg,lasso_reg]
get_rmses(models)

LinearRegression  로그 변환된 RMSE: 0.132
Ridge  로그 변환된 RMSE: 0.128
Lasso  로그 변환된 RMSE: 0.176





[0.13189576579154494, 0.12750846334052998, 0.17628250556471403]

np.expm1(0.13189576579154494)

0.14098938294353924

def get_top_bottom_coef(model,n=10):
    coef = pd.Series(model.coef_,index=X.columns)
    return coef

get_top_bottom_coef(lr_reg)

MSSubClass              -0.000488
LotFrontage              0.000390
LotArea                  0.000002
OverallQual              0.041866
OverallCond              0.032077
                           ...   
SaleCondition_AdjLand   -0.076029
SaleCondition_Alloca     0.028081
SaleCondition_Family    -0.063272
SaleCondition_Normal    -0.000464
SaleCondition_Partial    0.167346
Length: 270, dtype: float64

def get_top_bottom_coef(model,n=10):
    coef = pd.Series(model.coef_,index=X.columns)
    coef_high = coef.sort_values(ascending=False).head(n)
    coef_low = coef.sort_values(ascending=False).tail(n)
    return coef_high, coef_low

get_top_bottom_coef(lr_reg,5)

(RoofMatl_Membran    0.528057
 RoofMatl_Metal      0.414453
 RoofMatl_WdShngl    0.345254
 RoofMatl_Roll       0.311983
 RoofStyle_Shed      0.292647
 dtype: float64,
 Electrical_Mix     -0.300984
 MSZoning_C (all)   -0.323040
 Functional_Maj2    -0.426389
 Condition2_PosN    -0.663744
 RoofMatl_ClyTile   -2.372268
 dtype: float64)

def visualize_coefficient(models):
    fig,axs = plt.subplots(figsize=(24,10),nrows=1,ncols=3)
    fig.tight_layout()
    for i_num,model in enumerate(models):
        coef_high,coef_low = get_top_bottom_coef(model)
        coef_concat = pd.concat([coef_high,coef_low])
        axs[i_num].set_title(model.__class__.__name__+ ' Coefficient',size=25)
        axs[i_num].tick_params(axis='y',direction='in',pad=-120)
        for label in (axs[i_num].get_xticklabels()+axs[i_num].get_yticklabels()):
            label.set_fontsize(22)
        sns.barplot(x=coef_concat.values,y=coef_concat.index,ax=axs[i_num])

visualize_coefficient(models)

from sklearn.model_selection import cross_val_score

def get_avg_rmse_cv(models):
    for model in models:
        rmse_list = np.sqrt(-cross_val_score(model, X, y, scoring='neg_mean_squared_error',cv=5)) 
        rmse_avg = np.mean(rmse_list)
        print(f'{model.__class__.__name__} cv rmse 값 리스트 : {np.round(rmse_list, 3)}')
        print(f'{model.__class__.__name__} cv 평균 rmse 값 : {np.round(rmse_avg, 3)}')

get_avg_rmse_cv(models)

LinearRegression cv rmse 값 리스트 : [0.135 0.165 0.168 0.111 0.198]
LinearRegression cv 평균 rmse 값 : 0.155
Ridge cv rmse 값 리스트 : [0.117 0.154 0.142 0.117 0.189]
Ridge cv 평균 rmse 값 : 0.144
Lasso cv rmse 값 리스트 : [0.161 0.204 0.177 0.181 0.265]
Lasso cv 평균 rmse 값 : 0.198

from sklearn.model_selection import GridSearchCV

def print_best_params(model, params):
    grid_model = GridSearchCV(model,params,scoring='neg_mean_squared_error',cv=5)
    grid_model.fit(X,y)
    rmse = np.sqrt(-1*grid_model.best_score_)
    print(f'{model.__class__.__name__} 5 cv시 최적 평균 rmse 값:{np.round(rmse,4)}, 최적 alpha값:{grid_model.best_params_}')

ridge_params = {
    'alpha':[0.05,0.1,1,5,8,12,15,20]
}
print_best_params(ridge_reg,ridge_params)

Ridge 5 cv시 최적 평균 rmse 값:0.1418, 최적 alpha값:{'alpha': 12}

lasso_params = {
    'alpha':[0.001,0.005,0.008,0.05,0.03,0.1,0.5,1,5,10]
}
print_best_params(lasso_reg,lasso_params)

Lasso 5 cv시 최적 평균 rmse 값:0.142, 최적 alpha값:{'alpha': 0.001}

lr_reg = LinearRegression()
lr_reg.fit(X_train,y_train)

ridge_reg = Ridge(alpha=12)
ridge_reg.fit(X_train,y_train)

lasso_reg = Lasso(alpha=0.001)
lasso_reg.fit(X_train,y_train)

models=[lr_reg,ridge_reg,lasso_reg]
get_rmses(models)

visualize_coefficient(models)

LinearRegression  로그 변환된 RMSE: 0.132
Ridge  로그 변환된 RMSE: 0.124
Lasso  로그 변환된 RMSE: 0.12

from scipy.stats import skew

# object가 아닌 숫자형 피쳐의 칼럼 index 객체 추출.
features_index = df.dtypes[df.dtypes != 'object'].index

# df에 칼럼 index를 []로 입력하면 해당하는 칼럼 데이터 세트 반환. apply lambda로 skew() 호출
skew_features = df[features_index].apply(lambda x:skew(x))

# skew(왜곡)정도가 1이상인 칼럼만 추출.
skew_features_top = skew_features[skew_features>1]

skew_features_top.sort_values(ascending=False)

MiscVal          24.451640
PoolArea         14.813135
LotArea          12.195142
3SsnPorch        10.293752
LowQualFinSF      9.002080
KitchenAbvGr      4.483784
BsmtFinSF2        4.250888
ScreenPorch       4.117977
BsmtHalfBath      4.099186
EnclosedPorch     3.086696
MasVnrArea        2.673661
LotFrontage       2.382499
OpenPorchSF       2.361912
BsmtFinSF1        1.683771
WoodDeckSF        1.539792
TotalBsmtSF       1.522688
MSSubClass        1.406210
1stFlrSF          1.375342
GrLivArea         1.365156
dtype: float64

np.log1p(df[skew_features_top.index])

	MSSubClass	LotFrontage	LotArea	MasVnrArea	BsmtFinSF1	BsmtFinSF2	TotalBsmtSF	1stFlrSF	LowQualFinSF	GrLivArea	BsmtHalfBath	KitchenAbvGr	WoodDeckSF	OpenPorchSF	EnclosedPorch	3SsnPorch	ScreenPorch	PoolArea	MiscVal
0	4.110874	4.189655	9.042040	5.283204	6.561031	0.000000	6.753438	6.753438	0.0	7.444833	0.000000	0.693147	0.000000	4.127134	0.000000	0.0	0.0	0.0	0.000000
1	3.044522	4.394449	9.169623	0.000000	6.886532	0.000000	7.141245	7.141245	0.0	7.141245	0.693147	0.693147	5.700444	0.000000	0.000000	0.0	0.0	0.0	0.000000
2	4.110874	4.234107	9.328212	5.093750	6.188264	0.000000	6.825460	6.825460	0.0	7.488294	0.000000	0.693147	0.000000	3.761200	0.000000	0.0	0.0	0.0	0.000000
3	4.262680	4.110874	9.164401	0.000000	5.379897	0.000000	6.629363	6.869014	0.0	7.448916	0.000000	0.693147	0.000000	3.583519	5.609472	0.0	0.0	0.0	0.000000
4	4.110874	4.442651	9.565284	5.860786	6.486161	0.000000	7.044033	7.044033	0.0	7.695758	0.000000	0.693147	5.262690	4.442651	0.000000	0.0	0.0	0.0	0.000000
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1455	4.110874	4.143135	8.976894	0.000000	0.000000	0.000000	6.860664	6.860664	0.0	7.407318	0.000000	0.693147	0.000000	3.713572	0.000000	0.0	0.0	0.0	0.000000
1456	3.044522	4.454347	9.486152	4.787492	6.673298	5.099866	7.341484	7.637234	0.0	7.637234	0.000000	0.693147	5.857933	0.000000	0.000000	0.0	0.0	0.0	0.000000
1457	4.262680	4.204693	9.109746	0.000000	5.620401	0.000000	7.050123	7.080868	0.0	7.758333	0.000000	0.693147	0.000000	4.110874	0.000000	0.0	0.0	0.0	7.824446
1458	3.044522	4.234107	9.181735	0.000000	3.912023	6.937314	6.983790	6.983790	0.0	6.983790	0.000000	0.693147	5.905362	0.000000	4.727388	0.0	0.0	0.0	0.000000
1459	3.044522	4.330733	9.204121	0.000000	6.722630	5.673323	7.136483	7.136483	0.0	7.136483	0.000000	0.693147	6.602588	4.234107	0.000000	0.0	0.0	0.0	0.000000

1460 rows × 19 columns

# 추출된 왜곡 정도가 높은 피처를 로그 변환.
df[skew_features_top.index] = np.log1p(df[skew_features_top.index])

# 로그 변환했으므로 다시 원-핫 인코딩을 적용하고 피처/타깃 데이터 세트 생성
df_ohe=pd.get_dummies(df)
y = df_ohe['SalePrice']
X = df_ohe.drop(columns=['SalePrice'])
X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=156)

# 피처를 로그 변환한 후 다시 최적 하이퍼 파라미터와 RMSE 출력
ridge_params = {'alpha':[0.05,0.1,1,5,8,10,12,15,20]}
print_best_params(ridge_reg,ridge_params)

lasso_params = {'alpha':[0.001,0.005,0.008,0.05,0.03,0.1,0.5,1,5,10]}
print_best_params(lasso_reg,lasso_params)

Ridge 5 cv시 최적 평균 rmse 값:0.1275, 최적 alpha값:{'alpha': 10}
Lasso 5 cv시 최적 평균 rmse 값:0.1252, 최적 alpha값:{'alpha': 0.001}

lr_reg = LinearRegression()
lr_reg.fit(X_train,y_train)

ridge_reg = Ridge(alpha=10)
ridge_reg.fit(X_train,y_train)

lasso_reg = Lasso(alpha=0.001)
lasso_reg.fit(X_train,y_train)

models=[lr_reg,ridge_reg,lasso_reg]
get_rmses(models)

visualize_coefficient(models)

LinearRegression  로그 변환된 RMSE: 0.128
Ridge  로그 변환된 RMSE: 0.122
Lasso  로그 변환된 RMSE: 0.119

# 이상치 데이터 처리
df_org = pd.read_csv('houseprice.csv')

plt.scatter(x=df_org['GrLivArea'],y=df_org['SalePrice'])

<matplotlib.collections.PathCollection at 0x247de310730>

cond1 = df_ohe['GrLivArea'] > np.log1p(4000)
cond2 = df_ohe['SalePrice'] < np.log1p(500000)
df_ohe[cond1 & cond2]

	MSSubClass	LotFrontage	LotArea	OverallQual	OverallCond	YearBuilt	YearRemodAdd	MasVnrArea	BsmtFinSF1	BsmtFinSF2	...	SaleType_ConLw	SaleType_New	SaleType_Oth	SaleType_WD	SaleCondition_Abnorml	SaleCondition_AdjLand	SaleCondition_Alloca	SaleCondition_Family	SaleCondition_Normal	SaleCondition_Partial
523	4.110874	4.875197	10.599007	10	5	2007	2008	6.637258	7.723562	0.0	...	0	1	0	0	0	0	0	0	0	1
1298	4.110874	5.749393	11.064887	10	5	2008	2008	6.680855	8.638525	0.0	...	0	1	0	0	0	0	0	0	0	1

2 rows × 271 columns

outlier_index = df_ohe[cond1 & cond2].index
df_ohe.shape

(1460, 271)

df_ohe.drop(index=outlier_index,inplace=True)
df_ohe.shape

(1458, 271)

y = df_ohe['SalePrice']
X = df_ohe.drop(columns=['SalePrice'])
X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=156)

ridge_params = {'alpha':[0.05,0.1,1,5,8,10,12,15,20]}
print_best_params(ridge_reg,ridge_params)

lasso_params = {'alpha':[0.001,0.005,0.008,0.05,0.03,0.1,0.5,1,5,10]}
print_best_params(lasso_reg,lasso_params)

Ridge 5 cv시 최적 평균 rmse 값:0.1125, 최적 alpha값:{'alpha': 8}
Lasso 5 cv시 최적 평균 rmse 값:0.1122, 최적 alpha값:{'alpha': 0.001}

lr_reg = LinearRegression()
lr_reg.fit(X_train,y_train)

ridge_reg = Ridge(alpha=10)
ridge_reg.fit(X_train,y_train)

lasso_reg = Lasso(alpha=0.001)
lasso_reg.fit(X_train,y_train)

models=[lr_reg,ridge_reg,lasso_reg]
get_rmses(models)

visualize_coefficient(models)
# LinearRegression  로그 변환된 RMSE: 0.132
# Ridge  로그 변환된 RMSE: 0.124
# Lasso  로그 변환된 RMSE: 0.12

LinearRegression  로그 변환된 RMSE: 0.129
Ridge  로그 변환된 RMSE: 0.103
Lasso  로그 변환된 RMSE: 0.1

차원 축소

일반적으로 적은 차원에서 학습된 모델일수록 신뢰도가 높아짐.
피처 선택(feature election)과 피처 추출(feature extraction)을 일반적으로 사용

주 성분 분석(Principal Component Analysis)

PCA는 가장 대표적인 차원 축소 기법.
축 생성 -> 축으로 데이터 투영 -> 데이터를 축으로 만들어 표현

from sklearn.datasets import load_iris
import pandas as pd
import matplotlib.pyplot as plt

#넘파이 데이터 세트를 판다스 데이터프레임으로 변환
iris = load_iris(as_frame=True)
iris.data.columns

Index(['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)',
       'petal width (cm)'],
      dtype='object')

iris.data.columns = ['sepal length', 'sepal width', 'petal length', 'petal width']
iris.data

	sepal length	sepal width	petal length	petal width
0	5.1	3.5	1.4	0.2
1	4.9	3.0	1.4	0.2
2	4.7	3.2	1.3	0.2
3	4.6	3.1	1.5	0.2
4	5.0	3.6	1.4	0.2
...	...	...	...	...
145	6.7	3.0	5.2	2.3
146	6.3	2.5	5.0	1.9
147	6.5	3.0	5.2	2.0
148	6.2	3.4	5.4	2.3
149	5.9	3.0	5.1	1.8

150 rows × 4 columns

iris.target

0      0
1      0
2      0
3      0
4      0
      ..
145    2
146    2
147    2
148    2
149    2
Name: target, Length: 150, dtype: int32

iris.data['target'] = iris.target
iris.data.head(2)

	sepal length	sepal width	petal length	petal width	target
0	5.1	3.5	1.4	0.2	0
1	4.9	3.0	1.4	0.2	0

markers=['^','s','o']
for i, marker in enumerate(markers):
    df = iris.data
    x = df[df['target']==i]['sepal length']
    y = df[df['target']==i]['sepal width']
    plt.scatter(x,y,marker=marker,label=iris.target_names[i])
    
plt.legend()
plt.show()

from sklearn.preprocessing import StandardScaler

df_scaled = StandardScaler().fit_transform(df.iloc[:,:-1])
df_scaled

array([[-9.00681170e-01,  1.01900435e+00, -1.34022653e+00,
        -1.31544430e+00],
       [-1.14301691e+00, -1.31979479e-01, -1.34022653e+00,
        -1.31544430e+00],
       [-1.38535265e+00,  3.28414053e-01, -1.39706395e+00,
        -1.31544430e+00],
       [-1.50652052e+00,  9.82172869e-02, -1.28338910e+00,
        -1.31544430e+00],
       [-1.02184904e+00,  1.24920112e+00, -1.34022653e+00,
        -1.31544430e+00],
       [-5.37177559e-01,  1.93979142e+00, -1.16971425e+00,
        -1.05217993e+00],
       [-1.50652052e+00,  7.88807586e-01, -1.34022653e+00,
        -1.18381211e+00],
       [-1.02184904e+00,  7.88807586e-01, -1.28338910e+00,
        -1.31544430e+00],
       [-1.74885626e+00, -3.62176246e-01, -1.34022653e+00,
        -1.31544430e+00],
       [-1.14301691e+00,  9.82172869e-02, -1.28338910e+00,
        -1.44707648e+00],
       [-5.37177559e-01,  1.47939788e+00, -1.28338910e+00,
        -1.31544430e+00],
       [-1.26418478e+00,  7.88807586e-01, -1.22655167e+00,
        -1.31544430e+00],
       [-1.26418478e+00, -1.31979479e-01, -1.34022653e+00,
        -1.44707648e+00],
       [-1.87002413e+00, -1.31979479e-01, -1.51073881e+00,
        -1.44707648e+00],
       [-5.25060772e-02,  2.16998818e+00, -1.45390138e+00,
        -1.31544430e+00],
       [-1.73673948e-01,  3.09077525e+00, -1.28338910e+00,
        -1.05217993e+00],
       [-5.37177559e-01,  1.93979142e+00, -1.39706395e+00,
        -1.05217993e+00],
       [-9.00681170e-01,  1.01900435e+00, -1.34022653e+00,
        -1.18381211e+00],
       [-1.73673948e-01,  1.70959465e+00, -1.16971425e+00,
        -1.18381211e+00],
       [-9.00681170e-01,  1.70959465e+00, -1.28338910e+00,
        -1.18381211e+00],
       [-5.37177559e-01,  7.88807586e-01, -1.16971425e+00,
        -1.31544430e+00],
       [-9.00681170e-01,  1.47939788e+00, -1.28338910e+00,
        -1.05217993e+00],
       [-1.50652052e+00,  1.24920112e+00, -1.56757623e+00,
        -1.31544430e+00],
       [-9.00681170e-01,  5.58610819e-01, -1.16971425e+00,
        -9.20547742e-01],
       [-1.26418478e+00,  7.88807586e-01, -1.05603939e+00,
        -1.31544430e+00],
       [-1.02184904e+00, -1.31979479e-01, -1.22655167e+00,
        -1.31544430e+00],
       [-1.02184904e+00,  7.88807586e-01, -1.22655167e+00,
        -1.05217993e+00],
       [-7.79513300e-01,  1.01900435e+00, -1.28338910e+00,
        -1.31544430e+00],
       [-7.79513300e-01,  7.88807586e-01, -1.34022653e+00,
        -1.31544430e+00],
       [-1.38535265e+00,  3.28414053e-01, -1.22655167e+00,
        -1.31544430e+00],
       [-1.26418478e+00,  9.82172869e-02, -1.22655167e+00,
        -1.31544430e+00],
       [-5.37177559e-01,  7.88807586e-01, -1.28338910e+00,
        -1.05217993e+00],
       [-7.79513300e-01,  2.40018495e+00, -1.28338910e+00,
        -1.44707648e+00],
       [-4.16009689e-01,  2.63038172e+00, -1.34022653e+00,
        -1.31544430e+00],
       [-1.14301691e+00,  9.82172869e-02, -1.28338910e+00,
        -1.31544430e+00],
       [-1.02184904e+00,  3.28414053e-01, -1.45390138e+00,
        -1.31544430e+00],
       [-4.16009689e-01,  1.01900435e+00, -1.39706395e+00,
        -1.31544430e+00],
       [-1.14301691e+00,  1.24920112e+00, -1.34022653e+00,
        -1.44707648e+00],
       [-1.74885626e+00, -1.31979479e-01, -1.39706395e+00,
        -1.31544430e+00],
       [-9.00681170e-01,  7.88807586e-01, -1.28338910e+00,
        -1.31544430e+00],
       [-1.02184904e+00,  1.01900435e+00, -1.39706395e+00,
        -1.18381211e+00],
       [-1.62768839e+00, -1.74335684e+00, -1.39706395e+00,
        -1.18381211e+00],
       [-1.74885626e+00,  3.28414053e-01, -1.39706395e+00,
        -1.31544430e+00],
       [-1.02184904e+00,  1.01900435e+00, -1.22655167e+00,
        -7.88915558e-01],
       [-9.00681170e-01,  1.70959465e+00, -1.05603939e+00,
        -1.05217993e+00],
       [-1.26418478e+00, -1.31979479e-01, -1.34022653e+00,
        -1.18381211e+00],
       [-9.00681170e-01,  1.70959465e+00, -1.22655167e+00,
        -1.31544430e+00],
       [-1.50652052e+00,  3.28414053e-01, -1.34022653e+00,
        -1.31544430e+00],
       [-6.58345429e-01,  1.47939788e+00, -1.28338910e+00,
        -1.31544430e+00],
       [-1.02184904e+00,  5.58610819e-01, -1.34022653e+00,
        -1.31544430e+00],
       [ 1.40150837e+00,  3.28414053e-01,  5.35408562e-01,
         2.64141916e-01],
       [ 6.74501145e-01,  3.28414053e-01,  4.21733708e-01,
         3.95774101e-01],
       [ 1.28034050e+00,  9.82172869e-02,  6.49083415e-01,
         3.95774101e-01],
       [-4.16009689e-01, -1.74335684e+00,  1.37546573e-01,
         1.32509732e-01],
       [ 7.95669016e-01, -5.92373012e-01,  4.78571135e-01,
         3.95774101e-01],
       [-1.73673948e-01, -5.92373012e-01,  4.21733708e-01,
         1.32509732e-01],
       [ 5.53333275e-01,  5.58610819e-01,  5.35408562e-01,
         5.27406285e-01],
       [-1.14301691e+00, -1.51316008e+00, -2.60315415e-01,
        -2.62386821e-01],
       [ 9.16836886e-01, -3.62176246e-01,  4.78571135e-01,
         1.32509732e-01],
       [-7.79513300e-01, -8.22569778e-01,  8.07091462e-02,
         2.64141916e-01],
       [-1.02184904e+00, -2.43394714e+00, -1.46640561e-01,
        -2.62386821e-01],
       [ 6.86617933e-02, -1.31979479e-01,  2.51221427e-01,
         3.95774101e-01],
       [ 1.89829664e-01, -1.97355361e+00,  1.37546573e-01,
        -2.62386821e-01],
       [ 3.10997534e-01, -3.62176246e-01,  5.35408562e-01,
         2.64141916e-01],
       [-2.94841818e-01, -3.62176246e-01, -8.98031345e-02,
         1.32509732e-01],
       [ 1.03800476e+00,  9.82172869e-02,  3.64896281e-01,
         2.64141916e-01],
       [-2.94841818e-01, -1.31979479e-01,  4.21733708e-01,
         3.95774101e-01],
       [-5.25060772e-02, -8.22569778e-01,  1.94384000e-01,
        -2.62386821e-01],
       [ 4.32165405e-01, -1.97355361e+00,  4.21733708e-01,
         3.95774101e-01],
       [-2.94841818e-01, -1.28296331e+00,  8.07091462e-02,
        -1.30754636e-01],
       [ 6.86617933e-02,  3.28414053e-01,  5.92245988e-01,
         7.90670654e-01],
       [ 3.10997534e-01, -5.92373012e-01,  1.37546573e-01,
         1.32509732e-01],
       [ 5.53333275e-01, -1.28296331e+00,  6.49083415e-01,
         3.95774101e-01],
       [ 3.10997534e-01, -5.92373012e-01,  5.35408562e-01,
         8.77547895e-04],
       [ 6.74501145e-01, -3.62176246e-01,  3.08058854e-01,
         1.32509732e-01],
       [ 9.16836886e-01, -1.31979479e-01,  3.64896281e-01,
         2.64141916e-01],
       [ 1.15917263e+00, -5.92373012e-01,  5.92245988e-01,
         2.64141916e-01],
       [ 1.03800476e+00, -1.31979479e-01,  7.05920842e-01,
         6.59038469e-01],
       [ 1.89829664e-01, -3.62176246e-01,  4.21733708e-01,
         3.95774101e-01],
       [-1.73673948e-01, -1.05276654e+00, -1.46640561e-01,
        -2.62386821e-01],
       [-4.16009689e-01, -1.51316008e+00,  2.38717193e-02,
        -1.30754636e-01],
       [-4.16009689e-01, -1.51316008e+00, -3.29657076e-02,
        -2.62386821e-01],
       [-5.25060772e-02, -8.22569778e-01,  8.07091462e-02,
         8.77547895e-04],
       [ 1.89829664e-01, -8.22569778e-01,  7.62758269e-01,
         5.27406285e-01],
       [-5.37177559e-01, -1.31979479e-01,  4.21733708e-01,
         3.95774101e-01],
       [ 1.89829664e-01,  7.88807586e-01,  4.21733708e-01,
         5.27406285e-01],
       [ 1.03800476e+00,  9.82172869e-02,  5.35408562e-01,
         3.95774101e-01],
       [ 5.53333275e-01, -1.74335684e+00,  3.64896281e-01,
         1.32509732e-01],
       [-2.94841818e-01, -1.31979479e-01,  1.94384000e-01,
         1.32509732e-01],
       [-4.16009689e-01, -1.28296331e+00,  1.37546573e-01,
         1.32509732e-01],
       [-4.16009689e-01, -1.05276654e+00,  3.64896281e-01,
         8.77547895e-04],
       [ 3.10997534e-01, -1.31979479e-01,  4.78571135e-01,
         2.64141916e-01],
       [-5.25060772e-02, -1.05276654e+00,  1.37546573e-01,
         8.77547895e-04],
       [-1.02184904e+00, -1.74335684e+00, -2.60315415e-01,
        -2.62386821e-01],
       [-2.94841818e-01, -8.22569778e-01,  2.51221427e-01,
         1.32509732e-01],
       [-1.73673948e-01, -1.31979479e-01,  2.51221427e-01,
         8.77547895e-04],
       [-1.73673948e-01, -3.62176246e-01,  2.51221427e-01,
         1.32509732e-01],
       [ 4.32165405e-01, -3.62176246e-01,  3.08058854e-01,
         1.32509732e-01],
       [-9.00681170e-01, -1.28296331e+00, -4.30827696e-01,
        -1.30754636e-01],
       [-1.73673948e-01, -5.92373012e-01,  1.94384000e-01,
         1.32509732e-01],
       [ 5.53333275e-01,  5.58610819e-01,  1.27429511e+00,
         1.71209594e+00],
       [-5.25060772e-02, -8.22569778e-01,  7.62758269e-01,
         9.22302838e-01],
       [ 1.52267624e+00, -1.31979479e-01,  1.21745768e+00,
         1.18556721e+00],
       [ 5.53333275e-01, -3.62176246e-01,  1.04694540e+00,
         7.90670654e-01],
       [ 7.95669016e-01, -1.31979479e-01,  1.16062026e+00,
         1.31719939e+00],
       [ 2.12851559e+00, -1.31979479e-01,  1.61531967e+00,
         1.18556721e+00],
       [-1.14301691e+00, -1.28296331e+00,  4.21733708e-01,
         6.59038469e-01],
       [ 1.76501198e+00, -3.62176246e-01,  1.44480739e+00,
         7.90670654e-01],
       [ 1.03800476e+00, -1.28296331e+00,  1.16062026e+00,
         7.90670654e-01],
       [ 1.64384411e+00,  1.24920112e+00,  1.33113254e+00,
         1.71209594e+00],
       [ 7.95669016e-01,  3.28414053e-01,  7.62758269e-01,
         1.05393502e+00],
       [ 6.74501145e-01, -8.22569778e-01,  8.76433123e-01,
         9.22302838e-01],
       [ 1.15917263e+00, -1.31979479e-01,  9.90107977e-01,
         1.18556721e+00],
       [-1.73673948e-01, -1.28296331e+00,  7.05920842e-01,
         1.05393502e+00],
       [-5.25060772e-02, -5.92373012e-01,  7.62758269e-01,
         1.58046376e+00],
       [ 6.74501145e-01,  3.28414053e-01,  8.76433123e-01,
         1.44883158e+00],
       [ 7.95669016e-01, -1.31979479e-01,  9.90107977e-01,
         7.90670654e-01],
       [ 2.24968346e+00,  1.70959465e+00,  1.67215710e+00,
         1.31719939e+00],
       [ 2.24968346e+00, -1.05276654e+00,  1.78583195e+00,
         1.44883158e+00],
       [ 1.89829664e-01, -1.97355361e+00,  7.05920842e-01,
         3.95774101e-01],
       [ 1.28034050e+00,  3.28414053e-01,  1.10378283e+00,
         1.44883158e+00],
       [-2.94841818e-01, -5.92373012e-01,  6.49083415e-01,
         1.05393502e+00],
       [ 2.24968346e+00, -5.92373012e-01,  1.67215710e+00,
         1.05393502e+00],
       [ 5.53333275e-01, -8.22569778e-01,  6.49083415e-01,
         7.90670654e-01],
       [ 1.03800476e+00,  5.58610819e-01,  1.10378283e+00,
         1.18556721e+00],
       [ 1.64384411e+00,  3.28414053e-01,  1.27429511e+00,
         7.90670654e-01],
       [ 4.32165405e-01, -5.92373012e-01,  5.92245988e-01,
         7.90670654e-01],
       [ 3.10997534e-01, -1.31979479e-01,  6.49083415e-01,
         7.90670654e-01],
       [ 6.74501145e-01, -5.92373012e-01,  1.04694540e+00,
         1.18556721e+00],
       [ 1.64384411e+00, -1.31979479e-01,  1.16062026e+00,
         5.27406285e-01],
       [ 1.88617985e+00, -5.92373012e-01,  1.33113254e+00,
         9.22302838e-01],
       [ 2.49201920e+00,  1.70959465e+00,  1.50164482e+00,
         1.05393502e+00],
       [ 6.74501145e-01, -5.92373012e-01,  1.04694540e+00,
         1.31719939e+00],
       [ 5.53333275e-01, -5.92373012e-01,  7.62758269e-01,
         3.95774101e-01],
       [ 3.10997534e-01, -1.05276654e+00,  1.04694540e+00,
         2.64141916e-01],
       [ 2.24968346e+00, -1.31979479e-01,  1.33113254e+00,
         1.44883158e+00],
       [ 5.53333275e-01,  7.88807586e-01,  1.04694540e+00,
         1.58046376e+00],
       [ 6.74501145e-01,  9.82172869e-02,  9.90107977e-01,
         7.90670654e-01],
       [ 1.89829664e-01, -1.31979479e-01,  5.92245988e-01,
         7.90670654e-01],
       [ 1.28034050e+00,  9.82172869e-02,  9.33270550e-01,
         1.18556721e+00],
       [ 1.03800476e+00,  9.82172869e-02,  1.04694540e+00,
         1.58046376e+00],
       [ 1.28034050e+00,  9.82172869e-02,  7.62758269e-01,
         1.44883158e+00],
       [-5.25060772e-02, -8.22569778e-01,  7.62758269e-01,
         9.22302838e-01],
       [ 1.15917263e+00,  3.28414053e-01,  1.21745768e+00,
         1.44883158e+00],
       [ 1.03800476e+00,  5.58610819e-01,  1.10378283e+00,
         1.71209594e+00],
       [ 1.03800476e+00, -1.31979479e-01,  8.19595696e-01,
         1.44883158e+00],
       [ 5.53333275e-01, -1.28296331e+00,  7.05920842e-01,
         9.22302838e-01],
       [ 7.95669016e-01, -1.31979479e-01,  8.19595696e-01,
         1.05393502e+00],
       [ 4.32165405e-01,  7.88807586e-01,  9.33270550e-01,
         1.44883158e+00],
       [ 6.86617933e-02, -1.31979479e-01,  7.62758269e-01,
         7.90670654e-01]])

from sklearn.decomposition import PCA

pca = PCA(n_components=2)
iris_pca = pca.fit_transform(df_scaled)

iris_pca

array([[-2.26470281,  0.4800266 ],
       [-2.08096115, -0.67413356],
       [-2.36422905, -0.34190802],
       [-2.29938422, -0.59739451],
       [-2.38984217,  0.64683538],
       [-2.07563095,  1.48917752],
       [-2.44402884,  0.0476442 ],
       [-2.23284716,  0.22314807],
       [-2.33464048, -1.11532768],
       [-2.18432817, -0.46901356],
       [-2.1663101 ,  1.04369065],
       [-2.32613087,  0.13307834],
       [-2.2184509 , -0.72867617],
       [-2.6331007 , -0.96150673],
       [-2.1987406 ,  1.86005711],
       [-2.26221453,  2.68628449],
       [-2.2075877 ,  1.48360936],
       [-2.19034951,  0.48883832],
       [-1.898572  ,  1.40501879],
       [-2.34336905,  1.12784938],
       [-1.914323  ,  0.40885571],
       [-2.20701284,  0.92412143],
       [-2.7743447 ,  0.45834367],
       [-1.81866953,  0.08555853],
       [-2.22716331,  0.13725446],
       [-1.95184633, -0.62561859],
       [-2.05115137,  0.24216355],
       [-2.16857717,  0.52714953],
       [-2.13956345,  0.31321781],
       [-2.26526149, -0.3377319 ],
       [-2.14012214, -0.50454069],
       [-1.83159477,  0.42369507],
       [-2.61494794,  1.79357586],
       [-2.44617739,  2.15072788],
       [-2.10997488, -0.46020184],
       [-2.2078089 , -0.2061074 ],
       [-2.04514621,  0.66155811],
       [-2.52733191,  0.59229277],
       [-2.42963258, -0.90418004],
       [-2.16971071,  0.26887896],
       [-2.28647514,  0.44171539],
       [-1.85812246, -2.33741516],
       [-2.5536384 , -0.47910069],
       [-1.96444768,  0.47232667],
       [-2.13705901,  1.14222926],
       [-2.0697443 , -0.71105273],
       [-2.38473317,  1.1204297 ],
       [-2.39437631, -0.38624687],
       [-2.22944655,  0.99795976],
       [-2.20383344,  0.00921636],
       [ 1.10178118,  0.86297242],
       [ 0.73133743,  0.59461473],
       [ 1.24097932,  0.61629765],
       [ 0.40748306, -1.75440399],
       [ 1.0754747 , -0.20842105],
       [ 0.38868734, -0.59328364],
       [ 0.74652974,  0.77301931],
       [-0.48732274, -1.85242909],
       [ 0.92790164,  0.03222608],
       [ 0.01142619, -1.03401828],
       [-0.11019628, -2.65407282],
       [ 0.44069345, -0.06329519],
       [ 0.56210831, -1.76472438],
       [ 0.71956189, -0.18622461],
       [-0.0333547 , -0.43900321],
       [ 0.87540719,  0.50906396],
       [ 0.35025167, -0.19631173],
       [ 0.15881005, -0.79209574],
       [ 1.22509363, -1.6222438 ],
       [ 0.1649179 , -1.30260923],
       [ 0.73768265,  0.39657156],
       [ 0.47628719, -0.41732028],
       [ 1.2341781 , -0.93332573],
       [ 0.6328582 , -0.41638772],
       [ 0.70266118, -0.06341182],
       [ 0.87427365,  0.25079339],
       [ 1.25650912, -0.07725602],
       [ 1.35840512,  0.33131168],
       [ 0.66480037, -0.22592785],
       [-0.04025861, -1.05871855],
       [ 0.13079518, -1.56227183],
       [ 0.02345269, -1.57247559],
       [ 0.24153827, -0.77725638],
       [ 1.06109461, -0.63384324],
       [ 0.22397877, -0.28777351],
       [ 0.42913912,  0.84558224],
       [ 1.04872805,  0.5220518 ],
       [ 1.04453138, -1.38298872],
       [ 0.06958832, -0.21950333],
       [ 0.28347724, -1.32932464],
       [ 0.27907778, -1.12002852],
       [ 0.62456979,  0.02492303],
       [ 0.33653037, -0.98840402],
       [-0.36218338, -2.01923787],
       [ 0.28858624, -0.85573032],
       [ 0.09136066, -0.18119213],
       [ 0.22771687, -0.38492008],
       [ 0.57638829, -0.1548736 ],
       [-0.44766702, -1.54379203],
       [ 0.25673059, -0.5988518 ],
       [ 1.84456887,  0.87042131],
       [ 1.15788161, -0.69886986],
       [ 2.20526679,  0.56201048],
       [ 1.44015066, -0.04698759],
       [ 1.86781222,  0.29504482],
       [ 2.75187334,  0.8004092 ],
       [ 0.36701769, -1.56150289],
       [ 2.30243944,  0.42006558],
       [ 2.00668647, -0.71143865],
       [ 2.25977735,  1.92101038],
       [ 1.36417549,  0.69275645],
       [ 1.60267867, -0.42170045],
       [ 1.8839007 ,  0.41924965],
       [ 1.2601151 , -1.16226042],
       [ 1.4676452 , -0.44227159],
       [ 1.59007732,  0.67624481],
       [ 1.47143146,  0.25562182],
       [ 2.42632899,  2.55666125],
       [ 3.31069558,  0.01778095],
       [ 1.26376667, -1.70674538],
       [ 2.0377163 ,  0.91046741],
       [ 0.97798073, -0.57176432],
       [ 2.89765149,  0.41364106],
       [ 1.33323218, -0.48181122],
       [ 1.7007339 ,  1.01392187],
       [ 1.95432671,  1.0077776 ],
       [ 1.17510363, -0.31639447],
       [ 1.02095055,  0.06434603],
       [ 1.78834992, -0.18736121],
       [ 1.86364755,  0.56229073],
       [ 2.43595373,  0.25928443],
       [ 2.30492772,  2.62632347],
       [ 1.86270322, -0.17854949],
       [ 1.11414774, -0.29292262],
       [ 1.2024733 , -0.81131527],
       [ 2.79877045,  0.85680333],
       [ 1.57625591,  1.06858111],
       [ 1.3462921 ,  0.42243061],
       [ 0.92482492,  0.0172231 ],
       [ 1.85204505,  0.67612817],
       [ 2.01481043,  0.61388564],
       [ 1.90178409,  0.68957549],
       [ 1.15788161, -0.69886986],
       [ 2.04055823,  0.8675206 ],
       [ 1.9981471 ,  1.04916875],
       [ 1.87050329,  0.38696608],
       [ 1.56458048, -0.89668681],
       [ 1.5211705 ,  0.26906914],
       [ 1.37278779,  1.01125442],
       [ 0.96065603, -0.02433167]])

pca_columns = ['pca_com_1','pca_com_2']
df_pca = pd.DataFrame(iris_pca,columns=pca_columns)
df_pca['target']=iris.target
df_pca.head(2)

	pca_com_1	pca_com_2	target
0	-2.264703	0.480027	0
1	-2.080961	-0.674134	0

markers=['^','s','o']
for i, marker in enumerate(markers):
    df = iris.data
    x = df_pca[df_pca['target']==i]['pca_com_1']
    y = df_pca[df_pca['target']==i]['pca_com_2']
    plt.scatter(x,y,marker=marker,label=iris.target_names[i])
    
plt.legend()
plt.show()

pca.explained_variance_ratio_

array([0.72962445, 0.22850762])

from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import cross_val_score
import numpy as np

rcf = RandomForestClassifier(random_state=156)
scores = cross_val_score(rcf, iris.data.iloc[:,:-1],iris.target,scoring='accuracy',cv=3)
print(f'개별 정확도:{scores}, 평균 정확도 : {np.mean(scores)}')

개별 정확도:[0.98 0.94 0.96], 평균 정확도 : 0.96

rcf = RandomForestClassifier(random_state=156)
scores = cross_val_score(rcf, df_pca.iloc[:,:-1],iris.target,scoring='accuracy',cv=3)
print(f'개별 정확도:{scores}, 평균 정확도 : {np.mean(scores)}')

개별 정확도:[0.88 0.88 0.88], 평균 정확도 : 0.88

SungMin

초보 개발자의 학습 저장용 블로그

이전 포스트

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다음 포스트

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Sesac-ML_DL

회귀

보스턴 주택 가격

차원 축소

주 성분 분석(Principal Component Analysis)

Sesac 43일차

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0개의 댓글

	sepal length	sepal width	petal length	petal width
0	5.1	3.5	1.4	0.2
1	4.9	3.0	1.4	0.2
2	4.7	3.2	1.3	0.2
3	4.6	3.1	1.5	0.2
4	5.0	3.6	1.4	0.2
...	...	...	...	...
145	6.7	3.0	5.2	2.3
146	6.3	2.5	5.0	1.9
147	6.5	3.0	5.2	2.0
148	6.2	3.4	5.4	2.3
149	5.9	3.0	5.1	1.8

	sepal length	sepal width	petal length	petal width
0	5.1	3.5	1.4	0.2
1	4.9	3.0	1.4	0.2
2	4.7	3.2	1.3	0.2
3	4.6	3.1	1.5	0.2
4	5.0	3.6	1.4	0.2
...	...	...	...	...
145	6.7	3.0	5.2	2.3
146	6.3	2.5	5.0	1.9
147	6.5	3.0	5.2	2.0
148	6.2	3.4	5.4	2.3
149	5.9	3.0	5.1	1.8

	sepal length	sepal width	petal length	petal width
0	5.1	3.5	1.4	0.2
1	4.9	3.0	1.4	0.2
2	4.7	3.2	1.3	0.2
3	4.6	3.1	1.5	0.2
4	5.0	3.6	1.4	0.2
...	...	...	...	...
145	6.7	3.0	5.2	2.3
146	6.3	2.5	5.0	1.9
147	6.5	3.0	5.2	2.0
148	6.2	3.4	5.4	2.3
149	5.9	3.0	5.1	1.8