100天搞定机器学习|Day1数据预处理

数据预处理是机器学习中最基础也最麻烦的一部分内容在我们把精力扑倒各种算法的推导之前，最应该做的就是把数据预处理先搞定在之后的每个算法实现和案例练手过程中，这一步都必不可少同学们也不要嫌麻烦，动起手来吧基础比较好的同学也可以温故知新，再练习一下哈

闲言少叙，下面我们六步完成数据预处理其实我感觉这里少了一步：观察数据 ![此处输入图片的描述][1]

这是十组国籍、年龄、收入、是否已购买的数据

有分类数据，有数值型数据，还有一些缺失值

看起来是一个分类预测问题

根据国籍、年龄、收入来预测是够会购买

OK，有了大体的认识，开始表演。

Step 1：导入库

import numpy as np

import pandas as pd

Step 2：导入数据集

dataset = pd.read_csv('Data.csv')

X = dataset.iloc[ : , :-1].values
Y = dataset.iloc[ : , 3].values
print("X")
print(X)
print("Y")
print(Y)

这一步的目的是将自变量和因变量拆成一个矩阵和一个向量。结果如下

X
[['France' 44.0 72000.0]
 ['Spain' 27.0 48000.0]
 ['Germany' 30.0 54000.0]
 ['Spain' 38.0 61000.0]
 ['Germany' 40.0 nan]
 ['France' 35.0 58000.0]
 ['Spain' nan 52000.0]
 ['France' 48.0 79000.0]
 ['Germany' 50.0 83000.0]
 ['France' 37.0 67000.0]]
Y
['No' 'Yes' 'No' 'No' 'Yes' 'Yes' 'No' 'Yes' 'No' 'Yes']

Step 3：处理缺失数据

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])

Imputer类具体用法移步

scikit-learn.org/stable/modu…

本例中我们用的是均值替代法填充缺失值

运行结果如下

Step 3: Handling the missing data
step2
X
[['France' 44.0 72000.0]
 ['Spain' 27.0 48000.0]
 ['Germany' 30.0 54000.0]
 ['Spain' 38.0 61000.0]
 ['Germany' 40.0 63777.77777777778]
 ['France' 35.0 58000.0]
 ['Spain' 38.77777777777778 52000.0]
 ['France' 48.0 79000.0]
 ['Germany' 50.0 83000.0]
 ['France' 37.0 67000.0]]

Step 4：把分类数据转换为数字

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)
print("X")
print(X)

print("Y")
print(Y)

LabelEncoder用法请移步

scikit-learn.org/stable/modu…

X
[[1.00000000e+00 0.00000000e+00 0.00000000e+00 4.40000000e+01
  7.20000000e+04]
 [0.00000000e+00 0.00000000e+00 1.00000000e+00 2.70000000e+01
  4.80000000e+04]
 [0.00000000e+00 1.00000000e+00 0.00000000e+00 3.00000000e+01
  5.40000000e+04]
 [0.00000000e+00 0.00000000e+00 1.00000000e+00 3.80000000e+01
  6.10000000e+04]
 [0.00000000e+00 1.00000000e+00 0.00000000e+00 4.00000000e+01
  6.37777778e+04]
 [1.00000000e+00 0.00000000e+00 0.00000000e+00 3.50000000e+01
  5.80000000e+04]
 [0.00000000e+00 0.00000000e+00 1.00000000e+00 3.87777778e+01
  5.20000000e+04]
 [1.00000000e+00 0.00000000e+00 0.00000000e+00 4.80000000e+01
  7.90000000e+04]
 [0.00000000e+00 1.00000000e+00 0.00000000e+00 5.00000000e+01
  8.30000000e+04]
 [1.00000000e+00 0.00000000e+00 0.00000000e+00 3.70000000e+01
  6.70000000e+04]]
Y
[0 1 0 0 1 1 0 1 0 1]

Step 5：将数据集分为训练集和测试集 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)

X_train
[[0.00000000e+00 1.00000000e+00 0.00000000e+00 4.00000000e+01
  6.37777778e+04]
 [1.00000000e+00 0.00000000e+00 0.00000000e+00 3.70000000e+01
  6.70000000e+04]
 [0.00000000e+00 0.00000000e+00 1.00000000e+00 2.70000000e+01
  4.80000000e+04]
 [0.00000000e+00 0.00000000e+00 1.00000000e+00 3.87777778e+01
  5.20000000e+04]
 [1.00000000e+00 0.00000000e+00 0.00000000e+00 4.80000000e+01
  7.90000000e+04]
 [0.00000000e+00 0.00000000e+00 1.00000000e+00 3.80000000e+01
  6.10000000e+04]
 [1.00000000e+00 0.00000000e+00 0.00000000e+00 4.40000000e+01
  7.20000000e+04]
 [1.00000000e+00 0.00000000e+00 0.00000000e+00 3.50000000e+01
  5.80000000e+04]]
X_test
[[0.0e+00 1.0e+00 0.0e+00 3.0e+01 5.4e+04]
 [0.0e+00 1.0e+00 0.0e+00 5.0e+01 8.3e+04]]
step2
Y_train
[1 1 1 0 1 0 0 1]
Y_test
[0 0]

Step 6：特征缩放

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

大多数机器学习算法在计算中使用两个数据点之间的欧氏距离

特征在幅度、单位和范围上很大的变化,这引起了问题

高数值特征在距离计算中的权重大于低数值特征

通过特征标准化或Z分数归一化来完成

导入sklearn.preprocessing 库中的StandardScala

用法：scikit-learn.org/stable/modu…

X_train
[[-1.          2.64575131 -0.77459667  0.26306757  0.12381479]
 [ 1.         -0.37796447 -0.77459667 -0.25350148  0.46175632]
 [-1.         -0.37796447  1.29099445 -1.97539832 -1.53093341]
 [-1.         -0.37796447  1.29099445  0.05261351 -1.11141978]
 [ 1.         -0.37796447 -0.77459667  1.64058505  1.7202972 ]
 [-1.         -0.37796447  1.29099445 -0.0813118  -0.16751412]
 [ 1.         -0.37796447 -0.77459667  0.95182631  0.98614835]
 [ 1.         -0.37796447 -0.77459667 -0.59788085 -0.48214934]]
X_test
[[-1.          2.64575131 -0.77459667 -1.45882927 -0.90166297]
 [-1.          2.64575131 -0.77459667  1.98496442  2.13981082]]