机器学习1/100天-数据预处理

Day1 Data PreProcessing

github: 100-Days-Of-ML-Code

1.导入两个常用的python库，numpy, pandas

import numpy as np 
import pandas as pd 

2.读取数据文件

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

pd函数read_csv读取数据文件
而后dataframe.iloc按照位置选取数据，划分成X和Y

3.缺省值处理

使用sklearn.preprocessing.Imputer处理缺省值，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.将文本数据编码

使用sklearn.preprocessing.LabelEncoder和OneHotEncoder编码数据。

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文本变数值，OneHotEncoder数值变OneHot编码

5.划分训练集和测试集

在新版本中train_test_split函数位于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.数据标准化

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