主要任务：
①完成常见的数据导入操作，包括数据导入，缺失值填充
②完成常见的机器学习数据准备，包括特征二值化和训练集测试集的划分等
# -*- coding: utf-8 -*-"""
Created on Wed Oct 17 00:26:22 2018
@author: Administrator
"""%reset -f
%clear# In[*]## 第1步：导入库#Day 1: Data Prepocessing#Step 1: Importing the librariesimport numpy as npimport pandas as pdimport os
os.chdir("E:\multi\ml\coad")# In[*]#Step 2: Importing datasetdataset = pd.read_csv('coad_messa.csv',header=0,index_col=0)
X = dataset.iloc[ : , :-1].values
Y = dataset.iloc[ : , 6].values# In[*]print("Step 2: Importing dataset")
print("X")
print(X)
print("Y")
print(Y)这一步主要是导入数据，我们的前6列为用来预测的输入数据，包括gender， stage等等，我们将其设置为X，而输出数据，预测目标为患者的特征，可以是肿瘤或者正常等等，我们将其设置为Y。
Step 2: Importing dataset X [[61. 0. 1. 1. 1. 1.] [67. 1. 3. 1. 2. 3.] [42. 0. 2. 2. 1. 1.] ... [44. 0. 2. 1. 2. 1.] [82. 1. 2. 1. 2. 1.] [52. 0. 2. 2. 1. 1.]] > Y [0. 0. 0. 1. 1. 1. 0. 1. 0. 1. 0. 0. 0. 0. 0. 0. 1. 1. 1. 1. 1. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 1. 1. 1. 1. 0. 0. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 1. 1. 1. 0. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 1. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 1. 0. 0. 1. 0. 0. 0. 0. 1. 1. 1. 0. 0. 1. 0. 0. 0. 0. 0. 0. 1. 1. 1. 0. 1. 1. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 1. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 1. 0. 1. 0. 0. 0. > 1. 1. 0. 0. 0. 1. 0. 1. 1. 1. 0. 1. 1. 0. 1. 0. 0. 0. 1. 1. 0. 0. 0. 1. 0. 1. 1. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 1. 1. 1. 1. 0. 1. 1. 1. 0. 0. 0. 1. 1. 0. 0. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 1. 1. 0. 0. 1. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 1. 0. 0. 1. 1. 1. 0. 0. 1. 0. 0. 0. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 1. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 1. 0. 0. 0. 1. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 0. 1. 0. 0. 1. 0. 0. 0. 0. 1. 0. 1. 1. 0. 0. 1. 1. 0. 0. 1. 0. 0. 1. 0. 1. 0. 0. 1. 0. 0. 0. 1. 0. 1. 0. 0. 1. 0. 0. 1. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 1. 1. 1. 0.]
# In[*]#Step 3: Handling the missing datafrom 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])# In[*]print("---------------------")
print("Step 3: Handling the missing data")
print("step2")
print("X")
print(X)# In[*]#Step 4: Encoding categorical datafrom sklearn.preprocessing import LabelEncoder, OneHotEncoder
labelencoder_X = LabelEncoder()
X[ : , 2] = labelencoder_X.fit_transform(X[ : , 2])# In[*]#Creating a dummy variableonehotencoder = OneHotEncoder(categorical_features = [2])
X = onehotencoder.fit_transform(X).toarray()
labelencoder_Y = LabelEncoder()
Y = labelencoder_Y.fit_transform(Y)# In[*]print("---------------------")
print("Step 4: Encoding categorical data")
print("X")
print(X)
print("Y")
print(Y)这一步主要是将其中的数据二值化，因为我们使用的数据包括性别，众所周知，性别是男性或者女性，虽然我们可以简单的将其设置为0和1或者将其设置为1,2.但是
对于一些特征工程方面，有时会用到LabelEncoder和OneHotEncoder。比如kaggle中对于性别，sex，一般的属性值是male和female。两个值。那么不靠谱的方法直接用0表示male，用1表示female 了。上面说了这是不靠谱的。所以要用one-hot编码。首先我们需要用LabelEncoder把sex这个属性列里面的离散属性用数字来表示，就是上面的过程，把male,female这种不同的字符的属性值，用数字表示。
# In[*]#Step 5: Splitting the datasets into training sets and Test setsfrom sklearn.model_selection import train_test_split
X_train, X_test, Y_train, Y_test = train_test_split( X , Y ,
test_size = 0.2,
random_state = 0)# In[*]print("---------------------")
print("Step 5: Splitting the datasets into training sets and Test sets")
print("X_train")
print(X_train)
print("X_test")
print(X_test)
print("Y_train")
print(Y_train)
print("Y_test")
print(Y_test)# In[*]#Step 6: Feature Scalingfrom sklearn.preprocessing import StandardScaler
sc_X = StandardScaler()
X_train = sc_X.fit_transform(X_train)
X_test = sc_X.transform(X_test)# In[*]print("---------------------")
print("Step 6: Feature Scaling")
print("X_train")
print(X_train)
print("X_test")
print(X_test)最终我们将数据划分成训练集（80%）和测试集（20%）
作者：夜神moon
链接：https://www.jianshu.com/p/267597a244a2