43 lines
1.2 KiB
Python
43 lines
1.2 KiB
Python
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import numpy as np
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import matplotlib.pyplot as plt
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import pandas as pd
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# 引入資料集
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dataset = pd.read_csv("Data.csv")
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X = dataset.iloc[:, :-1].values
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Y = dataset.iloc[:, 3].values
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# 缺損資料處理
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from sklearn.impute import SimpleImputer
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imputer = SimpleImputer(missing_values=np.nan, strategy='mean')
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X[:, 1: 3] = imputer.fit_transform(X[:, 1:3])
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# 分類資料的處理
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from sklearn.preprocessing import LabelEncoder
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labelencoder_X = LabelEncoder()
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X[:, 0] = labelencoder_X.fit_transform(X[:, 0])
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from sklearn.preprocessing import OneHotEncoder
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from sklearn.compose import ColumnTransformer
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ct = ColumnTransformer(transformers=[
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('col-0', OneHotEncoder(), [0])
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], remainder='passthrough')
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X = np.array(ct.fit_transform(X), dtype=float)
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labelencoder_Y = LabelEncoder()
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Y = labelencoder_Y.fit_transform(Y)
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# 將資料集分為訓練集和測試集
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from sklearn.model_selection import train_test_split
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X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=0)
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# 特徵縮放
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from sklearn.preprocessing import StandardScaler
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sc = StandardScaler()
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X_train = sc.fit_transform(X_train)
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X_test = sc.transform(X_test)
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print(X_train)
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