마케팅 애널리틱스: Python으로 고객 이탈 예측하기
Mark Peterson
Director of Data Science, Infoblox
telco.dtypes
Account_Length int64 Vmail_Message int64 Day_Mins float64 Eve_Mins float64 Night_Mins float64 Intl_Mins float64 CustServ_Calls int64 Churn object Intl_Plan object Vmail_Plan object Day_Calls int64 Day_Charge float64 Eve_Calls int64 Eve_Charge float64 Night_Calls int64 Night_Charge float64 Intl_Calls int64 Intl_Charge float64 State object Area_Code int64 Phone object dtype: object
telco['Intl_Plan'].head()
0 no 1 no 2 no 3 yes 4 yes Name: Intl_Plan, dtype: object
방법 1: .replace()
.replace()
telco['Intl_Plan'].replace({'no':0 , 'yes':1}) telco['Intl_Plan'].head()
0 0 1 0 2 0 3 1 4 1 Name: Intl_Plan, dtype: int64
방법 2: LabelEncoder()
LabelEncoder()
from sklearn.preprocessing import LabelEncoder LabelEncoder().fit_transform(telco["Intl_Plan"]) telco['Intl_Plan'].head()
telco['State'].head(4)
0 KS 1 OH 2 NJ 3 OH Name: State, dtype: object
0 0 1 1 2 2 3 1 Name: State, dtype: int64
telco['Intl_Calls'].describe()
count 3333.000000 mean 4.479448 std 2.461214 min 0.000000 25% 3.000000 50% 4.000000 75% 6.000000 max 20.000000 Name: Intl_Calls, dtype: float64
telco['Night_Mins'].describe()
count 3333.000000 mean 200.872037 std 50.573847 min 23.200000 25% 167.000000 50% 201.200000 75% 235.300000 max 395.000000 Name: Night_Mins, dtype: float64
from sklearn.preprocessing import StandardScaler df = StandardScaler().fit_transform(df)
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