57 lines
1.9 KiB
Markdown
57 lines
1.9 KiB
Markdown
# 数据预处理
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<p align="center">
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<img src="https://github.com/MachineLearning100/100-Days-Of-ML-Code/blob/master/Info-graphs/Day%201.jpg">
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</p>
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如图所示，通过6步完成数据预处理。
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此例用到的[数据](https://github.com/Avik-Jain/100-Days-Of-ML-Code/blob/master/datasets/Data.csv)，[代码](https://github.com/MLEveryday/100-Days-Of-ML-Code/blob/master/Code/Day%201_Data_Preprocessing.py)。
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## 第1步：导入库
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```Python
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import numpy as np
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import pandas as pd
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```
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## 第2步：导入数据集
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```python
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//随后一列是label
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dataset = pd.read_csv('Data.csv')//读取csv文件
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X = dataset.iloc[ : , :-1].values//.iloc[行，列]
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Y = dataset.iloc[ : , 3].values // : 全部行 or 列；[a]第a行 or 列
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// [a,b,c]第 a,b,c 行 or 列
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```
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## 第3步：处理丢失数据
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```python
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from sklearn.preprocessing import Imputer
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imputer = Imputer(missing_values = "NaN", strategy = "mean", axis = 0)
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imputer = imputer.fit(X[ : , 1:3])
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X[ : , 1:3] = imputer.transform(X[ : , 1:3])
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```
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## 第4步：解析分类数据
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```python
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from sklearn.preprocessing import LabelEncoder, OneHotEncoder
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labelencoder_X = LabelEncoder()
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X[ : , 0] = labelencoder_X.fit_transform(X[ : , 0])
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```
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### 创建虚拟变量
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```python
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onehotencoder = OneHotEncoder(categorical_features = [0])
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X = onehotencoder.fit_transform(X).toarray()
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labelencoder_Y = LabelEncoder()
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Y = labelencoder_Y.fit_transform(Y)
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```
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## 第5步：拆分数据集为训练集合和测试集合
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```python
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#from sklearn.model_selection import train_test_split
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from sklearn.cross_validation 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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```
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## 第6步：特征量化
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```python
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from sklearn.preprocessing import StandardScaler
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sc_X = StandardScaler()
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X_train = sc_X.fit_transform(X_train)
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X_test = sc_X.transform(X_test)
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```
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