眾所周知，進(jìn)行機(jī)器學(xué)習(xí)建模的第一步就是數(shù)據(jù)預(yù)處理，在數(shù)據(jù)預(yù)處理的過(guò)程中，處理缺失值則是關(guān)鍵一步。在數(shù)據(jù)集規(guī)模較小的情況下，如果對(duì)缺失值進(jìn)行貿(mào)然的刪除，則會(huì)導(dǎo)致本就不多的數(shù)據(jù)更為稀少。所以我們需要對(duì)缺失值進(jìn)行一定的填補(bǔ)。

在填補(bǔ)的方法中，有直接用0填補(bǔ)的，有用均值的，有用中位數(shù)的，還有用眾數(shù)的

這些方法雖然簡(jiǎn)單，但是對(duì)數(shù)據(jù)集的還原程度不高，所以今天為大家介紹如何使用隨機(jī)森林的方法預(yù)測(cè)并且填補(bǔ)缺失值

我們先來(lái)看看這個(gè)數(shù)據(jù)集

它有許多缺失值，我們先對(duì)這個(gè)數(shù)據(jù)集進(jìn)行探索

import?numpy?as?np
import?pandas?as?pd
data?=?pd.read_csv("test-2.csv")

觀察數(shù)據(jù)

data

99 rows × 10 columns

data.info()
'pandas.core.frame.DataFrame'>
RangeIndex:?99?entries,?0?to?98
Data?columns?(total?10?columns):
?#???Column?????????Non-Null?Count??Dtype??
---??------?????????--------------??-----??
?0???year???????????99?non-null?????int64??
?1???selling_price??99?non-null?????int64??
?2???km_driven??????99?non-null?????int64??
?3???fuel???????????99?non-null?????object?
?4???seller_type????99?non-null?????object?
?5???transmission???99?non-null?????object?
?6???owner??????????99?non-null?????object?
?7???mileage????????94?non-null?????float64
?8???engine?????????96?non-null?????float64
?9???seats??????????94?non-null?????float64
dtypes:?float64(3),?int64(3),?object(4)
memory?usage:?7.9+?KB

只有mileage、 engine 、seats有缺失值，其余都完整

重點(diǎn)關(guān)注文本數(shù)據(jù)

data["fuel"].value_counts()
Petrol????48
Diesel????48
LPG????????2
CNG????????1
Name:?fuel,?dtype:?int64
data["seller_type"].value_counts()
Individual????65
Dealer????????34
Name:?seller_type,?dtype:?int64
data["transmission"].value_counts()
Manual???????87
Automatic????12
Name:?transmission,?dtype:?int64
data["owner"].value_counts()
First?Owner?????69
Second?Owner????26
Third?Owner??????4
Name:?owner,?dtype:?int64

對(duì)文本數(shù)據(jù)，一般采用onehot編碼或者label編碼

從語(yǔ)義上看owner這個(gè)屬性的值是有明顯的定序特征，不宜采用onehot編碼，而其余都是分類(lèi)屬性，并且屬性值的種類(lèi)不多

不會(huì)對(duì)隨機(jī)森林算法有過(guò)度的負(fù)面作用，所以可以采用onehot編碼

def?func(x:?str)?->?int:
????if?x?==?"First?Owner":
????????return?1
????elif?x?==?"Second?Owner":
????????return?2
????elif?x?==?"Third?Owner":
????????return?3

對(duì)owner進(jìn)行l(wèi)abel編碼

data["owner"].apply(func)
data["owner"].value_counts()
First?Owner?????69
Second?Owner????26
Third?Owner??????4
Name:?owner,?dtype:?int64

對(duì)其余文本屬性，統(tǒng)一使用get_dummies方法進(jìn)行onehot編碼

data?=?pd.get_dummies(data)
data

99 rows × 17 columns

查看編碼后的數(shù)據(jù)

data.info()
'pandas.core.frame.DataFrame'>
RangeIndex:?99?entries,?0?to?98
Data?columns?(total?17?columns):
?#???Column??????????????????Non-Null?Count??Dtype??
---??------??????????????????--------------??-----??
?0???year????????????????????99?non-null?????int64??
?1???selling_price???????????99?non-null?????int64??
?2???km_driven???????????????99?non-null?????int64??
?3???mileage?????????????????94?non-null?????float64
?4???engine??????????????????96?non-null?????float64
?5???seats???????????????????94?non-null?????float64
?6???fuel_CNG????????????????99?non-null?????uint8??
?7???fuel_Diesel?????????????99?non-null?????uint8??
?8???fuel_LPG????????????????99?non-null?????uint8??
?9???fuel_Petrol?????????????99?non-null?????uint8??
?10??seller_type_Dealer??????99?non-null?????uint8??
?11??seller_type_Individual??99?non-null?????uint8??
?12??transmission_Automatic??99?non-null?????uint8??
?13??transmission_Manual?????99?non-null?????uint8??
?14??owner_First?Owner???????99?non-null?????uint8??
?15??owner_Second?Owner??????99?non-null?????uint8??
?16??owner_Third?Owner???????99?non-null?????uint8??
dtypes:?float64(3),?int64(3),?uint8(11)
memory?usage:?5.8?KB

對(duì)每一列屬性的缺失值個(gè)數(shù)進(jìn)行求和統(tǒng)計(jì)

data.isnull().sum(axis=0)
year??????????????????????0
selling_price?????????????0
km_driven?????????????????0
mileage???????????????????5
engine????????????????????3
seats?????????????????????5
fuel_CNG??????????????????0
fuel_Diesel???????????????0
fuel_LPG??????????????????0
fuel_Petrol???????????????0
seller_type_Dealer????????0
seller_type_Individual????0
transmission_Automatic????0
transmission_Manual???????0
owner_First?Owner?????????0
owner_Second?Owner????????0
owner_Third?Owner?????????0
dtype:?int64

隨機(jī)森林算法填充缺失值

先填充缺失值較少的的列，之后再填多的

原理：將要填補(bǔ)的列作為目標(biāo)列，其余列作為屬性列，用隨機(jī)森林預(yù)測(cè)目標(biāo)列的值進(jìn)行填充

用非空的行作為訓(xùn)練集，空的行作為測(cè)試集，訓(xùn)練集中的數(shù)據(jù)有空值，則先用0填充

#?引入隨機(jī)森林模型和填補(bǔ)缺失值的模型
from?sklearn.impute?import?SimpleImputer
from?sklearn.ensemble?import?RandomForestRegressor

首先去除特定的列得到屬性列，記為X；選取特定的列作為目標(biāo)列，記為Y

在得到的屬性列中，用0填補(bǔ)缺失值

在目標(biāo)列中選取非空的行的index作為選取訓(xùn)練集的依據(jù)，空行的index作為測(cè)試集的依據(jù)

這樣就可以在X,Y中得到訓(xùn)練集和測(cè)試集了

有了訓(xùn)練集就把它們丟到隨機(jī)森林訓(xùn)練，然后用訓(xùn)練好的模型預(yù)測(cè)測(cè)試集中的數(shù)據(jù)得到預(yù)測(cè)值

最后將預(yù)測(cè)值填到相應(yīng)的位置中

for?name?in?["engine",?"mileage",?"seats"]:
????X?=?data.drop(columns=f"{name}")
????Y?=?data.loc[:,?f"{name}"]
????X_0?=?SimpleImputer(missing_values=np.nan,?strategy="constant").fit_transform(X)
????y_train?=?Y[Y.notnull()]
????y_test?=?Y[Y.isnull()]
????x_train?=?X_0[y_train.index,?:]
????x_test?=?X_0[y_test.index,?:]

????rfc?=?RandomForestRegressor(n_estimators=100)
????rfc?=?rfc.fit(x_train,?y_train)
????y_predict?=?rfc.predict(x_test)

????data.loc[Y.isnull(),?f"{name}"]?=?y_predict

查看填充后的數(shù)據(jù)

data.info()
'pandas.core.frame.DataFrame'>
RangeIndex:?99?entries,?0?to?98
Data?columns?(total?17?columns):
?#???Column??????????????????Non-Null?Count??Dtype??
---??------??????????????????--------------??-----??
?0???year????????????????????99?non-null?????int64??
?1???selling_price???????????99?non-null?????int64??
?2???km_driven???????????????99?non-null?????int64??
?3???mileage?????????????????99?non-null?????float64
?4???engine??????????????????99?non-null?????float64
?5???seats???????????????????99?non-null?????float64
?6???fuel_CNG????????????????99?non-null?????uint8??
?7???fuel_Diesel?????????????99?non-null?????uint8??
?8???fuel_LPG????????????????99?non-null?????uint8??
?9???fuel_Petrol?????????????99?non-null?????uint8??
?10??seller_type_Dealer??????99?non-null?????uint8??
?11??seller_type_Individual??99?non-null?????uint8??
?12??transmission_Automatic??99?non-null?????uint8??
?13??transmission_Manual?????99?non-null?????uint8??
?14??owner_First?Owner???????99?non-null?????uint8??
?15??owner_Second?Owner??????99?non-null?????uint8??
?16??owner_Third?Owner???????99?non-null?????uint8??
dtypes:?float64(3),?int64(3),?uint8(11)
memory?usage:?5.8?KB

可以發(fā)現(xiàn)原有的缺失值已經(jīng)被填好了

最后把結(jié)果導(dǎo)出為excel文件

data.to_excel("test-2(填補(bǔ)后).xlsx")