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

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

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:

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]]

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