100天搞定機器學習|Day1數據預處理

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數據預處理是機器學習中最基礎也最麻煩的一部份內容 在咱們把精力撲倒各類算法的推導以前，最應該作的就是把數據預處理先搞定 在以後的每一個算法實現和案例練手過程當中，這一步都必不可少 同窗們也不要嫌麻煩，動起手來吧 基礎比較好的同窗也能夠溫故知新，再練習一下哈

閒言少敘，下面咱們六步完成數據預處理 其實我感受這裏少了一步：觀察數據 ![此處輸入圖片的描述][1]

這是十組國籍、年齡、收入、是否已購買的數據

有分類數據，有數值型數據，還有一些缺失值

看起來是一個分類預測問題

根據國籍、年齡、收入來預測是夠會購買

OK，有了大致的認識，開始表演。

Step 1：導入庫

import numpy as np

import pandas as pd
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Step 2：導入數據集

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

X = dataset.iloc[ : , :-1].values
Y = dataset.iloc[ : , 3].values
print("X")
print(X)
print("Y")
print(Y)
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這一步的目的是將自變量和因變量拆成一個矩陣和一個向量。 結果以下

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']
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Step 3：處理缺失數據

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])
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Imputer類具體用法移步

scikit-learn.org/stable/modu…

本例中咱們用的是均值替代法填充缺失值

運行結果以下

Step 3: Handling the missing data
step2
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]]
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Step 4：把分類數據轉換爲數字

from sklearn.preprocessing import LabelEncoder, OneHotEncoder
labelencoder_X = LabelEncoder()
X[ : , 0] = labelencoder_X.fit_transform(X[ : , 0])

onehotencoder = OneHotEncoder(categorical_features = [0])
X = onehotencoder.fit_transform(X).toarray()
labelencoder_Y = LabelEncoder()
Y =  labelencoder_Y.fit_transform(Y)
print("X")
print(X)

print("Y")
print(Y)
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LabelEncoder用法請移步

scikit-learn.org/stable/modu…

X
[[1.00000000e+00 0.00000000e+00 0.00000000e+00 4.40000000e+01
  7.20000000e+04]
 [0.00000000e+00 0.00000000e+00 1.00000000e+00 2.70000000e+01
  4.80000000e+04]
 [0.00000000e+00 1.00000000e+00 0.00000000e+00 3.00000000e+01
  5.40000000e+04]
 [0.00000000e+00 0.00000000e+00 1.00000000e+00 3.80000000e+01
  6.10000000e+04]
 [0.00000000e+00 1.00000000e+00 0.00000000e+00 4.00000000e+01
  6.37777778e+04]
 [1.00000000e+00 0.00000000e+00 0.00000000e+00 3.50000000e+01
  5.80000000e+04]
 [0.00000000e+00 0.00000000e+00 1.00000000e+00 3.87777778e+01
  5.20000000e+04]
 [1.00000000e+00 0.00000000e+00 0.00000000e+00 4.80000000e+01
  7.90000000e+04]
 [0.00000000e+00 1.00000000e+00 0.00000000e+00 5.00000000e+01
  8.30000000e+04]
 [1.00000000e+00 0.00000000e+00 0.00000000e+00 3.70000000e+01
  6.70000000e+04]]
Y
[0 1 0 0 1 1 0 1 0 1]
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Step 5：將數據集分爲訓練集和測試集 from sklearn.cross_validation import train_test_split X_train, X_test, Y_train, Y_test = train_test_split( X , Y , test_size = 0.2, random_state = 0)

X_train
[[0.00000000e+00 1.00000000e+00 0.00000000e+00 4.00000000e+01
  6.37777778e+04]
 [1.00000000e+00 0.00000000e+00 0.00000000e+00 3.70000000e+01
  6.70000000e+04]
 [0.00000000e+00 0.00000000e+00 1.00000000e+00 2.70000000e+01
  4.80000000e+04]
 [0.00000000e+00 0.00000000e+00 1.00000000e+00 3.87777778e+01
  5.20000000e+04]
 [1.00000000e+00 0.00000000e+00 0.00000000e+00 4.80000000e+01
  7.90000000e+04]
 [0.00000000e+00 0.00000000e+00 1.00000000e+00 3.80000000e+01
  6.10000000e+04]
 [1.00000000e+00 0.00000000e+00 0.00000000e+00 4.40000000e+01
  7.20000000e+04]
 [1.00000000e+00 0.00000000e+00 0.00000000e+00 3.50000000e+01
  5.80000000e+04]]
X_test
[[0.0e+00 1.0e+00 0.0e+00 3.0e+01 5.4e+04]
 [0.0e+00 1.0e+00 0.0e+00 5.0e+01 8.3e+04]]
step2
Y_train
[1 1 1 0 1 0 0 1]
Y_test
[0 0]
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Step 6：特徵縮放

from sklearn.preprocessing import StandardScaler
sc_X = StandardScaler()
X_train = sc_X.fit_transform(X_train)
X_test = sc_X.transform(X_test)
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大多數機器學習算法在計算中使用兩個數據點之間的歐氏距離

特徵在幅度、單位和範圍上很大的變化,這引發了問題

高數值特徵在距離計算中的權重大於低數值特徵

經過特徵標準化或Z分數歸一化來完成

導入sklearn.preprocessing 庫中的StandardScala

用法：scikit-learn.org/stable/modu…

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]]
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