机器学习100天系列学习笔记 机器学习100天(中文翻译版)机器学习100天(英文原版）
数据预处理学习machine learning第一步需要先会清洗数据（即数据的预处理），下面我们需要对表中的数据进行清洗。
经过观察可以看出表中两处缺少值(NAN)；
#Step 1: importing library
import numpy as np
import pandas as pd
#Step 2: Importing dataset
dataset = pd.read_csv('D:/daily/机器学习100天/100-Days-Of-ML-Code-中文版本/100-Days-Of-ML-Code-master/datasets/Data.csv') # 设置你的文件路径
X = dataset.iloc[:,:,-1].values
Y = dataset.iloc[:,3].values
print("Step 2: Importing dataset")
print("X") #打印字符X
print(X) #打印变量X的值
print("Y")
print(Y)
打印
Step 2: Importing dataset
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: Handing the missing data
from sklearn.impute import SimpleImputer
imputer = SimpleImputer(missing_values=np.nan, strategy="mean") #通过SimpleImputer，这里用均值补充缺省值
X = dataset.iloc[:,:-1].values
imputer = imputer.fit(X[:,1:3])
X[:,1:3] = imputer.transform(X[:,1:3])
print("------------")
print("Step 3: Handing the missing data")
print("X")
print(X)
打印
Step 3: Handing the missing data
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]]
38.8、63777.8是平均值计算得到的；
第四步：数据编码#Step 4: Encoding categorical data
from sklearn.preprocessing import LabelEncoder, OneHotEncoder # OneHotEncoder 独热编码
from sklearn.compose import ColumnTransformer
ct = ColumnTransformer([("", OneHotEncoder(), [0])], remainder = 'passthrough')
X = ct.fit_transform(X)
labelencoder_Y = LabelEncoder()
Y = labelencoder_Y.fit_transform(Y)
print("----------------------------")
print("Step 4: Encoding categorical data")
print("X")
print(X)
print("Y")
print(Y)
打印
Step 4: Encoding categorical data
X
[[1.0 0.0 0.0 44.0 72000.0]
[0.0 0.0 1.0 27.0 48000.0]
[0.0 1.0 0.0 30.0 54000.0]
[0.0 0.0 1.0 38.0 61000.0]
[0.0 1.0 0.0 40.0 63777.77777777778]
[1.0 0.0 0.0 35.0 58000.0]
[0.0 0.0 1.0 38.77777777777778 52000.0]
[1.0 0.0 0.0 48.0 79000.0]
[0.0 1.0 0.0 50.0 83000.0]
[1.0 0.0 0.0 37.0 67000.0]]
Y
[0 1 0 0 1 1 0 1 0 1]
#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)
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)
打印
Step 5: Splitting the datasets into training sets and Test sets
X_train
[[0.0 1.0 0.0 40.0 63777.77777777778]
[1.0 0.0 0.0 37.0 67000.0]
[0.0 0.0 1.0 27.0 48000.0]
[0.0 0.0 1.0 38.77777777777778 52000.0]
[1.0 0.0 0.0 48.0 79000.0]
[0.0 0.0 1.0 38.0 61000.0]
[1.0 0.0 0.0 44.0 72000.0]
[1.0 0.0 0.0 35.0 58000.0]]
X_test
[[0.0 1.0 0.0 30.0 54000.0]
[0.0 1.0 0.0 50.0 83000.0]]
Y_train
[1 1 1 0 1 0 0 1]
Y_test
[0 0]
#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)
print("----------------------------")
print("Step 6: Feature Scaling")
print("X_train")
print(X_train)
print("X_test")
print(X_test)
打印
Step 6: Feature Scaling
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]]
对每一列的数据进行缩放，即（x-mean(x)）/std(x)。
数据上传不了，大家手动设置设置吧（狗头）
完整代码：
#Day 1: Data Preprocessing 2022/4/4
#Step 1: importing library
import numpy as np
import pandas as pd
#Step 2: Importing dataset
dataset = pd.read_csv('D:/daily/机器学习100天/100-Days-Of-ML-Code-中文版本/100-Days-Of-ML-Code-master/datasets/Data.csv')
X = dataset.iloc[:,:-1].values
Y = dataset.iloc[:,3].values
print("Step 2: Importing dataset")
print("X") #打印字符X
print(X) #打印变量X的值
print("Y")
print(Y)
#Step 3: Handing the missing data
from sklearn.impute import SimpleImputer
imputer = SimpleImputer(missing_values=np.nan, strategy="mean") #通过SimpleImputer，这里用均值补充缺省值
X = dataset.iloc[:,:-1].values
imputer = imputer.fit(X[:,1:3])
X[:,1:3] = imputer.transform(X[:,1:3])
print("------------")
print("Step 3: Handing the missing data")
print("X")
print(X)
#Step 4: Encoding categorical data
from sklearn.preprocessing import LabelEncoder, OneHotEncoder # OneHotEncoder 独热编码
from sklearn.compose import ColumnTransformer
ct = ColumnTransformer([("", OneHotEncoder(), [0])], remainder = 'passthrough')
X = ct.fit_transform(X)
labelencoder_Y = LabelEncoder()
Y = labelencoder_Y.fit_transform(Y)
print("----------------------------")
print("Step 4: Encoding categorical data")
print("X")
print(X)
print("Y")
print(Y)
#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)
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)
#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)
print("----------------------------")
print("Step 6: Feature Scaling")
print("X_train")
print(X_train)
print("X_test")
print(X_test)
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