Day1 Data PreProcessing

github: 100-Days-Of-ML-Code

1.匯入兩個常用的python庫，numpy, pandas

import numpy as np 
import pandas as pd 

2.讀取資料檔案

dataset = pd.read_csv("Data.csv")
X = dataset.iloc[:,:-1].values
Y = dataset.iloc[:,3].values

pd函式read_csv讀取資料檔案
而後dataframe.iloc按照位置選取資料，劃分成X和Y

3.預設值處理

使用sklearn.preprocessing.Imputer處理預設值，以均值代替NaN

from sklearn.preprocessing import Imputer
imputer = Imputer(missing_values = "NaN", strategy = "mean", axis = 0)
imputer = imputer.fit(X[:,1:3])
X[:,1:3] = imputer.transform(X[:,1:3])

4.將文字資料編碼

使用sklearn.preprocessing.LabelEncoder和OneHotEncoder編碼資料。

from sklearn.preprocessing import LabelEncoder, OneHotEncoder
labelencoder_X = LabelEncoder()
X[:,0] = labelencoder_X.fit_transform(X[:,0])

onehotencoder = OneHotEncoder(categorical_features = [0])
X = onehotencoder.fit_transform(X).toarray()
labelEncoder_Y = LabelEncoder()
Y = labelEncoder_Y.fit_transform(Y
 
)

LabelEncoder文字變數值，OneHotEncoder數值變OneHot編碼

5.劃分訓練集和測試集

在新版本中train_test_split函式位於model_select module

from sklearn.cross_validation import train_test_split
X_train, X_test, Y_train, Y_test = train_test_split(X,Y,test_size=0.2,random_state=0)

6.資料標準化

from sklearn.preprocessing import StandardScaler
sc_X = StandardScaler()
X_train = sc_X.fit_transform(X_train)
X_test = sc_X.fit_transform(X_test)