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 libraries
import numpy as np
import pandas as pd
import os
os.chdir("E:\multi\ml\coad")
# In[*]
#Step 2: Importing dataset
dataset = 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 data
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])
# In[*]
print("---------------------")
print("Step 3: Handling the missing data")
print("step2")
print("X")
print(X)
# In[*]
#Step 4: Encoding categorical data
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
labelencoder_X = LabelEncoder()
X[ : , 2] = labelencoder_X.fit_transform(X[ : , 2])
# In[*]
#Creating a dummy variable
onehotencoder = 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 sets
from 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 Scaling
from 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%）