100-Days-Of-ML-Code Day3 多元线性回归

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多元线性回归|第3天

Steps

通俗点讲就是有好多好多个X决定一个Y。

Datasets

R&D Spend Administration Marketing Spend State Profit
0 165349.20 136897.80 471784.10 New York 192261.83
1 162597.70 151377.59 443898.53 California 191792.06
2 153441.51 101145.55 407934.54 Florida 191050.39
3 144372.41 118671.85 383199.62 New York 182901.99
4 142107.34 91391.77 366168.42 Florida 166187.94
5 131876.90 99814.71 362861.36 New York 156991.12
6 134615.46 147198.87 127716.82 California 156122.51
7 130298.13 145530.06 323876.68 Florida 155752.60
8 120542.52 148718.95 311613.29 New York 152211.77
9 123334.88 108679.17 304981.62 California 149759.96
10 101913.08 110594.11 229160.95 Florida 146121.95
11 100671.96 91790.61 249744.55 California 144259.40
12 93863.75 127320.38 249839.44 Florida 141585.52
13 91992.39 135495.07 252664.93 California 134307.35
14 119943.24 156547.42 256512.92 Florida 132602.65
15 114523.61 122616.84 261776.23 New York 129917.04
16 78013.11 121597.55 264346.06 California 126992.93
17 94657.16 145077.58 282574.31 New York 125370.37
18 91749.16 114175.79 294919.57 Florida 124266.90
19 86419.70 153514.11 0.00 New York 122776.86
20 76253.86 113867.30 298664.47 California 118474.03
21 78389.47 153773.43 299737.29 New York 111313.02
22 73994.56 122782.75 303319.26 Florida 110352.25
23 67532.53 105751.03 304768.73 Florida 108733.99
24 77044.01 99281.34 140574.81 New York 108552.04
25 64664.71 139553.16 137962.62 California 107404.34
26 75328.87 144135.98 134050.07 Florida 105733.54
27 72107.60 127864.55 353183.81 New York 105008.31
28 66051.52 182645.56 118148.20 Florida 103282.38
29 65605.48 153032.06 107138.38 New York 101004.64
30 61994.48 115641.28 91131.24 Florida 99937.59
31 61136.38 152701.92 88218.23 New York 97483.56
32 63408.86 129219.61 46085.25 California 97427.84
33 55493.95 103057.49 214634.81 Florida 96778.92
34 46426.07 157693.92 210797.67 California 96712.80
35 46014.02 85047.44 205517.64 New York 96479.51
36 28663.76 127056.21 201126.82 Florida 90708.19
37 44069.95 51283.14 197029.42 California 89949.14
38 20229.59 65947.93 185265.10 New York 81229.06
39 38558.51 82982.09 174999.30 California 81005.76
40 28754.33 118546.05 172795.67 California 78239.91
41 27892.92 84710.77 164470.71 Florida 77798.83
42 23640.93 96189.63 148001.11 California 71498.49
43 15505.73 127382.30 35534.17 New York 69758.98
44 22177.74 154806.14 28334.72 California 65200.33
45 1000.23 124153.04 1903.93 New York 64926.08
46 1315.46 115816.21 297114.46 Florida 49490.75
47 0.00 135426.92 0.00 California 42559.73
48 542.05 51743.15 0.00 New York 35673.41
49 0.00 116983.80 45173.06 California 14681.40

Code

第1步：数据预处理

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#导入库
import pandas as pd
import numpy as np
#导入数据集
dataset = pd.read_csv('50_Startups.csv')
X = dataset.iloc[:,:-1].values
Y = dataset.iloc[:,4].values
#将类别数据数字化
from sklearn.preprocessing import LabelEncoder,OneHotEncoder
labelencoder = LabelEncoder()
X[:,3] = labelencoder.fit_transform(X[:,3])
onehotencoder = OneHotEncoder(categorical_features = [3])
X = onehotencoder.fit_transform(X).toarray()
#躲避虚拟变量陷阱
X = X[:,1:]
#拆分数据集为训练集和测试集
from sklearn.model_selection import train_test_split
X_train,X_test,Y_train,Y_test = train_test_split(X,Y,test_size = 0.2,random_state = 0)

第2步：在训练集上训练多元线性回归模型

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from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train,Y_train)

第3步：在测试集上预测结果

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Y_pred = regressor.predict(X_test)

多元线性回归可视化几经探索后惨遭失败QAQ。