机器学习一百天——第一天，数据预处理

文章转载自：Github.com

数据预处理

如图所示，按照以上六部完成图像预处理。

此例用到的数据、代码见文末。

第1步：导入库

importnumpyasnp

importpandasaspd

第2步：导入数据集

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

X=dataset.iloc[ : , :-1].values

Y=dataset.iloc[ : ,3].values

第3步：处理丢失数据

fromsklearn.preprocessingimportImputer

imputer=Imputer(missing_values ="NaN",strategy ="mean",axis =)

imputer=imputer.fit(X[ : , 1:3])

X[ : ,1:3]=imputer.transform(X[ : ,1:3])

第4步：解析分类数据

fromsklearn.preprocessingimportLabelEncoder, OneHotEncoder

labelencoder_X=LabelEncoder()

X[ : ,]=labelencoder_X.fit_transform(X[ : ,])

创建虚拟变量

onehotencoder=OneHotEncoder(categorical_features =[])

X=onehotencoder.fit_transform(X).toarray()

labelencoder_Y=LabelEncoder()

Y=labelencoder_Y.fit_transform(Y)

第5步：拆分数据集为训练集合和测试集合

fromsklearn.model_selectionimporttrain_test_split

X_train,X_test,Y_train,Y_test=train_test_split(X,Y,test_size=0.2,random_state=)

第6步：特征量化

fromsklearn.preprocessingimportStandardScaler

sc_X=StandardScaler()

X_train=sc_X.fit_transform(X_train)

X_test=sc_X.transform(X_test)

数据：

Country,Age,Salary,Purchased

France,44,72000,No

Spain,27,48000,Yes

Germany,30,54000,No

Spain,38,61000,No

Germany,40,,Yes

France,35,58000,Yes

Spain,,52000,No

France,48,79000,Yes

Germany,50,83000,No

France,37,67000,Yes

代码：

#Day 1: Data Prepocessing

#Step 1: Importing the libraries

importnumpyasnp

importpandasaspd

#Step 2: Importing dataset

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

X=dataset.iloc[ : , :-1].values

Y=dataset.iloc[ : ,3].values

print("Step 2: Importing dataset")

print("X")

print(X)

print("Y")

print(Y)

#Step 3: Handling the missing data

fromsklearn.preprocessingimportImputer

imputer=Imputer(missing_values ="NaN",strategy ="mean",axis =)

imputer=imputer.fit(X[ : , 1:3])

X[ : ,1:3]=imputer.transform(X[ : ,1:3])

print("---------------------")

print("Step 3: Handling the missing data")

print("step2")

print("X")

print(X)

#Step 4: Encoding categorical data

fromsklearn.preprocessingimportLabelEncoder, OneHotEncoder

labelencoder_X=LabelEncoder()

X[ : ,]=labelencoder_X.fit_transform(X[ : ,])

#Creating a dummy variable

onehotencoder=OneHotEncoder(categorical_features =[])

X=onehotencoder.fit_transform(X).toarray()

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

fromsklearn.model_selectionimporttrain_test_split

X_train,X_test,Y_train,Y_test=train_test_split(X,Y,test_size=0.2,random_state=)

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

fromsklearn.preprocessingimportStandardScaler

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)

◆◆◆◆◆