数据预处理

Day 1

如图所示，通过6步完成数据预处理

此例用到的数据，代码。

第1步：导入库

  1. import numpy as np
  2. import pandas as pd

第2步：导入数据集

  1. dataset = pd.read_csv('Day1.csv')
  2. X = dataset.iloc[ : , :-1].values
  3. Y = dataset.iloc[ : , 3].values

第3步：处理丢失数据

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

第4步：解析分类数据

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

创建虚拟变量

  1. onehotencoder = OneHotEncoder(categorical_features = [0])
  2. X = onehotencoder.fit_transform(X).toarray()
  3. labelencoder_Y = LabelEncoder()
  4. Y =  labelencoder_Y.fit_transform(Y)

第5步：拆分数据集为训练集合和测试集合

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

第6步：特征量化

  1. from sklearn.preprocessing import StandardScaler
  2. sc_X = StandardScaler()
  3. X_train = sc_X.fit_transform(X_train)
  4. X_test = sc_X.transform(X_test)