建模前的準備

Python 的行銷機器學習

Karolis Urbonas

Head of Analytics & Science, Amazon

資料範例

telco_raw.head()

電信資料表頭

資料型別

telco_raw.dtypes

customerID           object
gender               object
SeniorCitizen        object
Partner              object
Dependents           object
tenure                int64
PhoneService         object
MultipleLines        object
InternetService      object

OnlineSecurity       object
OnlineBackup         object
DeviceProtection     object
TechSupport          object
StreamingTV          object
StreamingMovies      object
Contract             object
PaperlessBilling     object
PaymentMethod        object
MonthlyCharges      float64
TotalCharges        float64
Churn                object

分開類別與數值欄位

將識別欄與目標變數名稱分成清單

custid = ['customerID']
target = ['Churn']

將類別與數值欄位名稱分成清單

categorical = telco_raw.nunique()[telcom.nunique()<10].keys().tolist()

categorical.remove(target[0])

numerical = [col for col in telco_raw.columns 
                     if col not in custid+target+categorical]

One-hot encoding

這是一個典型的類別型欄位

顏色
紅
白
藍
紅

One-hot encoding 結果

這是使用 one-hot encoding 轉換後的樣子。

顏色		紅	白	藍
紅	---------->	1	0	0
白	---------->	0	1	0
藍	---------->	0	0	1
紅	---------->	1	0	0

對類別變數做 one-hot encoding

telco_raw = pd.get_dummies(data=telco_raw, columns=categorical, drop_first=True)

縮放數值特徵

# Import StandardScaler library
from sklearn.preprocessing import StandardScaler

# Initialize StandardScaler instance
scaler = StandardScaler()

# Fit the scaler to numerical columns
scaled_numerical = scaler.fit_transform(telco_raw[numerical])

# Build a DataFrame
scaled_numerical = pd.DataFrame(scaled_numerical, columns=numerical)

整合全部處理

# Drop non-scaled numerical columns 
telco_raw = telco_raw.drop(columns=numerical, axis=1)

# Merge the non-numerical with the scaled numerical data
telco = telco_raw.merge(right=scaled_numerical,
                        how='left',
                        left_index=True, 
                        right_index=True
                        )

一起來練習資料前處理！

Python 的行銷機器學習