ModuleNotFoundError: No module named 'sklearn.cross_validation'
导入 sklearn.cross_validation 会报错，这是版本更新之后，命名改变的缘故。现在应该使用 sklearn.model_selection
from sklearn.model_selection import train_test_split
就可以成功
# 1. Importing the libraries
import numpy as np
import pandas as pd
# 2. Importing dataset
dataset = pd.read_csv('Data.csv') # read csv file
X = dataset.iloc[: , : -1].values #.iloc[row, coloum]
Y = dataset.iloc[: , 3].values # :
# 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])
# 4. Encoding categorical data
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
labelencoder_X = LabelEncoder()
X[ : , 0] = labelencoder_X.fit_transform(X[ : , 0])
# Encoding categorical data
onehotencoder = OneHotEncoder(categorical_features = [0])
X = onehotencoder.fit_transform(X).toarray()
labelencoder_Y = LabelEncoder()
Y = labelencoder_Y.fit_transform(Y)
# 5. Splitting the datasets into training sets and Test sets
from sklearn.model_selection import train_test_split
# we must use 'sklearn.model_selection' instead of 'sklearn.cross_validation'
X_train, X_test, Y_train, Y_test = train_test_split(X, Y,
test_size = 0.2, random_state = 0)
# 6. Feature Scaling
from sklearn.preprocessing import StandardScaler
sc_X = StandardScaler()
X_train = sc_X.fit_transform(X_train)
X_test = sc_X.fit_transform(X_test)
Day 1 from 100-Days-Of-ML-Code
作者：东皇太一在此
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