In [2]:
# Importing the libraries 導入庫
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
# 使圖像能夠調整
%matplotlib notebook
#中文字體顯示
plt.rc('font', family='SimHei', size=8)
In [3]:
dataset = pd.read_csv('Social_Network_Ads.csv')
dataset
Out[3]:
| User ID | Gender | Age | EstimatedSalary | Purchased | |
|---|---|---|---|---|---|
| 0 | 15624510 | Male | 19.0 | 19000.0 | 0 |
| 1 | 15810944 | Male | 35.0 | 20000.0 | 0 |
| 2 | 15668575 | Female | 26.0 | 43000.0 | 0 |
| 3 | 15603246 | Female | 27.0 | 57000.0 | 0 |
| 4 | 15804002 | Male | 19.0 | 76000.0 | 0 |
| 5 | 15728773 | Male | 27.0 | 58000.0 | 0 |
| 6 | 15598044 | Female | 27.0 | 84000.0 | 0 |
| 7 | 15694829 | Female | 32.0 | 150000.0 | 1 |
| 8 | 15600575 | Male | 25.0 | 33000.0 | 0 |
| 9 | 15727311 | Female | 35.0 | 65000.0 | 0 |
| 10 | 15570769 | Female | 26.0 | 80000.0 | 0 |
| 11 | 15606274 | Female | 26.0 | 52000.0 | 0 |
| 12 | 15746139 | Male | 20.0 | 86000.0 | 0 |
| 13 | 15704987 | Male | 32.0 | 18000.0 | 0 |
| 14 | 15628972 | Male | 18.0 | 82000.0 | 0 |
| 15 | 15697686 | Male | 29.0 | 80000.0 | 0 |
| 16 | 15733883 | Male | 47.0 | 25000.0 | 1 |
| 17 | 15617482 | Male | 45.0 | 26000.0 | 1 |
| 18 | 15704583 | Male | 46.0 | 28000.0 | 1 |
| 19 | 15621083 | Female | 48.0 | 29000.0 | 1 |
| 20 | 15649487 | Male | 45.0 | 22000.0 | 1 |
| 21 | 15736760 | Female | 47.0 | 49000.0 | 1 |
| 22 | 15714658 | Male | 48.0 | 41000.0 | 1 |
| 23 | 15599081 | Female | 45.0 | 22000.0 | 1 |
| 24 | 15705113 | Male | 46.0 | 23000.0 | 1 |
| 25 | 15631159 | Male | 47.0 | 20000.0 | 1 |
| 26 | 15792818 | Male | 49.0 | 28000.0 | 1 |
| 27 | 15633531 | Female | 47.0 | 30000.0 | 1 |
| 28 | 15744529 | Male | 29.0 | 43000.0 | 0 |
| 29 | 15669656 | Male | 31.0 | 18000.0 | 0 |
| ... | ... | ... | ... | ... | ... |
| 370 | 15611430 | Female | 60.0 | 46000.0 | 1 |
| 371 | 15774744 | Male | 60.0 | 83000.0 | 1 |
| 372 | 15629885 | Female | 39.0 | 73000.0 | 0 |
| 373 | 15708791 | Male | 59.0 | 130000.0 | 1 |
| 374 | 15793890 | Female | 37.0 | 80000.0 | 0 |
| 375 | 15646091 | Female | 46.0 | 32000.0 | 1 |
| 376 | 15596984 | Female | 46.0 | 74000.0 | 0 |
| 377 | 15800215 | Female | 42.0 | 53000.0 | 0 |
| 378 | 15577806 | Male | 41.0 | 87000.0 | 1 |
| 379 | 15749381 | Female | 58.0 | 23000.0 | 1 |
| 380 | 15683758 | Male | 42.0 | 64000.0 | 0 |
| 381 | 15670615 | Male | 48.0 | 33000.0 | 1 |
| 382 | 15715622 | Female | 44.0 | 139000.0 | 1 |
| 383 | 15707634 | Male | 49.0 | 28000.0 | 1 |
| 384 | 15806901 | Female | 57.0 | 33000.0 | 1 |
| 385 | 15775335 | Male | 56.0 | 60000.0 | 1 |
| 386 | 15724150 | Female | 49.0 | 39000.0 | 1 |
| 387 | 15627220 | Male | 39.0 | 71000.0 | 0 |
| 388 | 15672330 | Male | 47.0 | 34000.0 | 1 |
| 389 | 15668521 | Female | 48.0 | 35000.0 | 1 |
| 390 | 15807837 | Male | 48.0 | 33000.0 | 1 |
| 391 | 15592570 | Male | 47.0 | 23000.0 | 1 |
| 392 | 15748589 | Female | 45.0 | 45000.0 | 1 |
| 393 | 15635893 | Male | 60.0 | 42000.0 | 1 |
| 394 | 15757632 | Female | 39.0 | 59000.0 | 0 |
| 395 | 15691863 | Female | 46.0 | 41000.0 | 1 |
| 396 | 15706071 | Male | 51.0 | 23000.0 | 1 |
| 397 | 15654296 | Female | 50.0 | 20000.0 | 1 |
| 398 | 15755018 | Male | 36.0 | 33000.0 | 0 |
| 399 | 15594041 | Female | 49.0 | 36000.0 | 1 |
400 rows × 5 columns
In [4]:
X = dataset.iloc[:, [2,3]].values # 年齡和薪水
y = dataset.iloc[:, 4].values # 是否投放廣告
X
Out[4]:
array([[ 1.90000000e+01, 1.90000000e+04],
[ 3.50000000e+01, 2.00000000e+04],
[ 2.60000000e+01, 4.30000000e+04],
[ 2.70000000e+01, 5.70000000e+04],
[ 1.90000000e+01, 7.60000000e+04],
[ 2.70000000e+01, 5.80000000e+04],
[ 2.70000000e+01, 8.40000000e+04],
[ 3.20000000e+01, 1.50000000e+05],
[ 2.50000000e+01, 3.30000000e+04],
[ 3.50000000e+01, 6.50000000e+04],
[ 2.60000000e+01, 8.00000000e+04],
[ 2.60000000e+01, 5.20000000e+04],
[ 2.00000000e+01, 8.60000000e+04],
[ 3.20000000e+01, 1.80000000e+04],
[ 1.80000000e+01, 8.20000000e+04],
[ 2.90000000e+01, 8.00000000e+04],
[ 4.70000000e+01, 2.50000000e+04],
[ 4.50000000e+01, 2.60000000e+04],
[ 4.60000000e+01, 2.80000000e+04],
[ 4.80000000e+01, 2.90000000e+04],
[ 4.50000000e+01, 2.20000000e+04],
[ 4.70000000e+01, 4.90000000e+04],
[ 4.80000000e+01, 4.10000000e+04],
[ 4.50000000e+01, 2.20000000e+04],
[ 4.60000000e+01, 2.30000000e+04],
[ 4.70000000e+01, 2.00000000e+04],
[ 4.90000000e+01, 2.80000000e+04],
[ 4.70000000e+01, 3.00000000e+04],
[ 2.90000000e+01, 4.30000000e+04],
[ 3.10000000e+01, 1.80000000e+04],
[ 3.10000000e+01, 7.40000000e+04],
[ 2.70000000e+01, 1.37000000e+05],
[ 2.10000000e+01, 1.60000000e+04],
[ 2.80000000e+01, 4.40000000e+04],
[ 2.70000000e+01, 9.00000000e+04],
[ 3.50000000e+01, 2.70000000e+04],
[ 3.30000000e+01, 2.80000000e+04],
[ 3.00000000e+01, 4.90000000e+04],
[ 2.60000000e+01, 7.20000000e+04],
[ 2.70000000e+01, 3.10000000e+04],
[ 2.70000000e+01, 1.70000000e+04],
[ 3.30000000e+01, 5.10000000e+04],
[ 3.50000000e+01, 1.08000000e+05],
[ 3.00000000e+01, 1.50000000e+04],
[ 2.80000000e+01, 8.40000000e+04],
[ 2.30000000e+01, 2.00000000e+04],
[ 2.50000000e+01, 7.90000000e+04],
[ 2.70000000e+01, 5.40000000e+04],
[ 3.00000000e+01, 1.35000000e+05],
[ 3.10000000e+01, 8.90000000e+04],
[ 2.40000000e+01, 3.20000000e+04],
[ 1.80000000e+01, 4.40000000e+04],
[ 2.90000000e+01, 8.30000000e+04],
[ 3.50000000e+01, 2.30000000e+04],
[ 2.70000000e+01, 5.80000000e+04],
[ 2.40000000e+01, 5.50000000e+04],
[ 2.30000000e+01, 4.80000000e+04],
[ 2.80000000e+01, 7.90000000e+04],
[ 2.20000000e+01, 1.80000000e+04],
[ 3.20000000e+01, 1.17000000e+05],
[ 2.70000000e+01, 2.00000000e+04],
[ 2.50000000e+01, 8.70000000e+04],
[ 2.30000000e+01, 6.60000000e+04],
[ 3.20000000e+01, 1.20000000e+05],
[ 5.90000000e+01, 8.30000000e+04],
[ 2.40000000e+01, 5.80000000e+04],
[ 2.40000000e+01, 1.90000000e+04],
[ 2.30000000e+01, 8.20000000e+04],
[ 2.20000000e+01, 6.30000000e+04],
[ 3.10000000e+01, 6.80000000e+04],
[ 2.50000000e+01, 8.00000000e+04],
[ 2.40000000e+01, 2.70000000e+04],
[ 2.00000000e+01, 2.30000000e+04],
[ 3.30000000e+01, 1.13000000e+05],
[ 3.20000000e+01, 1.80000000e+04],
[ 3.40000000e+01, 1.12000000e+05],
[ 1.80000000e+01, 5.20000000e+04],
[ 2.20000000e+01, 2.70000000e+04],
[ 2.80000000e+01, 8.70000000e+04],
[ 2.60000000e+01, 1.70000000e+04],
[ 3.00000000e+01, 8.00000000e+04],
[ 3.90000000e+01, 4.20000000e+04],
[ 2.00000000e+01, 4.90000000e+04],
[ 3.50000000e+01, 8.80000000e+04],
[ 3.00000000e+01, 6.20000000e+04],
[ 3.10000000e+01, 1.18000000e+05],
[ 2.40000000e+01, 5.50000000e+04],
[ 2.80000000e+01, 8.50000000e+04],
[ 2.60000000e+01, 8.10000000e+04],
[ 3.50000000e+01, 5.00000000e+04],
[ 2.20000000e+01, 8.10000000e+04],
[ 3.00000000e+01, 1.16000000e+05],
[ 2.60000000e+01, 1.50000000e+04],
[ 2.90000000e+01, 2.80000000e+04],
[ 2.90000000e+01, 8.30000000e+04],
[ 3.50000000e+01, 4.40000000e+04],
[ 3.50000000e+01, 2.50000000e+04],
[ 2.80000000e+01, 1.23000000e+05],
[ 3.50000000e+01, 7.30000000e+04],
[ 2.80000000e+01, 3.70000000e+04],
[ 2.70000000e+01, 8.80000000e+04],
[ 2.80000000e+01, 5.90000000e+04],
[ 3.20000000e+01, 8.60000000e+04],
[ 3.30000000e+01, 1.49000000e+05],
[ 1.90000000e+01, 2.10000000e+04],
[ 2.10000000e+01, 7.20000000e+04],
[ 2.60000000e+01, 3.50000000e+04],
[ 2.70000000e+01, 8.90000000e+04],
[ 2.60000000e+01, 8.60000000e+04],
[ 3.80000000e+01, 8.00000000e+04],
[ 3.90000000e+01, 7.10000000e+04],
[ 3.70000000e+01, 7.10000000e+04],
[ 3.80000000e+01, 6.10000000e+04],
[ 3.70000000e+01, 5.50000000e+04],
[ 4.20000000e+01, 8.00000000e+04],
[ 4.00000000e+01, 5.70000000e+04],
[ 3.50000000e+01, 7.50000000e+04],
[ 3.60000000e+01, 5.20000000e+04],
[ 4.00000000e+01, 5.90000000e+04],
[ 4.10000000e+01, 5.90000000e+04],
[ 3.60000000e+01, 7.50000000e+04],
[ 3.70000000e+01, 7.20000000e+04],
[ 4.00000000e+01, 7.50000000e+04],
[ 3.50000000e+01, 5.30000000e+04],
[ 4.10000000e+01, 5.10000000e+04],
[ 3.90000000e+01, 6.10000000e+04],
[ 4.20000000e+01, 6.50000000e+04],
[ 2.60000000e+01, 3.20000000e+04],
[ 3.00000000e+01, 1.70000000e+04],
[ 2.60000000e+01, 8.40000000e+04],
[ 3.10000000e+01, 5.80000000e+04],
[ 3.30000000e+01, 3.10000000e+04],
[ 3.00000000e+01, 8.70000000e+04],
[ 2.10000000e+01, 6.80000000e+04],
[ 2.80000000e+01, 5.50000000e+04],
[ 2.30000000e+01, 6.30000000e+04],
[ 2.00000000e+01, 8.20000000e+04],
[ 3.00000000e+01, 1.07000000e+05],
[ 2.80000000e+01, 5.90000000e+04],
[ 1.90000000e+01, 2.50000000e+04],
[ 1.90000000e+01, 8.50000000e+04],
[ 1.80000000e+01, 6.80000000e+04],
[ 3.50000000e+01, 5.90000000e+04],
[ 3.00000000e+01, 8.90000000e+04],
[ 3.40000000e+01, 2.50000000e+04],
[ 2.40000000e+01, 8.90000000e+04],
[ 2.70000000e+01, 9.60000000e+04],
[ 4.10000000e+01, 3.00000000e+04],
[ 2.90000000e+01, 6.10000000e+04],
[ 2.00000000e+01, 7.40000000e+04],
[ 2.60000000e+01, 1.50000000e+04],
[ 4.10000000e+01, 4.50000000e+04],
[ 3.10000000e+01, 7.60000000e+04],
[ 3.60000000e+01, 5.00000000e+04],
[ 4.00000000e+01, 4.70000000e+04],
[ 3.10000000e+01, 1.50000000e+04],
[ 4.60000000e+01, 5.90000000e+04],
[ 2.90000000e+01, 7.50000000e+04],
[ 2.60000000e+01, 3.00000000e+04],
[ 3.20000000e+01, 1.35000000e+05],
[ 3.20000000e+01, 1.00000000e+05],
[ 2.50000000e+01, 9.00000000e+04],
[ 3.70000000e+01, 3.30000000e+04],
[ 3.50000000e+01, 3.80000000e+04],
[ 3.30000000e+01, 6.90000000e+04],
[ 1.80000000e+01, 8.60000000e+04],
[ 2.20000000e+01, 5.50000000e+04],
[ 3.50000000e+01, 7.10000000e+04],
[ 2.90000000e+01, 1.48000000e+05],
[ 2.90000000e+01, 4.70000000e+04],
[ 2.10000000e+01, 8.80000000e+04],
[ 3.40000000e+01, 1.15000000e+05],
[ 2.60000000e+01, 1.18000000e+05],
[ 3.40000000e+01, 4.30000000e+04],
[ 3.40000000e+01, 7.20000000e+04],
[ 2.30000000e+01, 2.80000000e+04],
[ 3.50000000e+01, 4.70000000e+04],
[ 2.50000000e+01, 2.20000000e+04],
[ 2.40000000e+01, 2.30000000e+04],
[ 3.10000000e+01, 3.40000000e+04],
[ 2.60000000e+01, 1.60000000e+04],
[ 3.10000000e+01, 7.10000000e+04],
[ 3.20000000e+01, 1.17000000e+05],
[ 3.30000000e+01, 4.30000000e+04],
[ 3.30000000e+01, 6.00000000e+04],
[ 3.10000000e+01, 6.60000000e+04],
[ 2.00000000e+01, 8.20000000e+04],
[ 3.30000000e+01, 4.10000000e+04],
[ 3.50000000e+01, 7.20000000e+04],
[ 2.80000000e+01, 3.20000000e+04],
[ 2.40000000e+01, 8.40000000e+04],
[ 1.90000000e+01, 2.60000000e+04],
[ 2.90000000e+01, 4.30000000e+04],
[ 1.90000000e+01, 7.00000000e+04],
[ 2.80000000e+01, 8.90000000e+04],
[ 3.40000000e+01, 4.30000000e+04],
[ 3.00000000e+01, 7.90000000e+04],
[ 2.00000000e+01, 3.60000000e+04],
[ 2.60000000e+01, 8.00000000e+04],
[ 3.50000000e+01, 2.20000000e+04],
[ 3.50000000e+01, 3.90000000e+04],
[ 4.90000000e+01, 7.40000000e+04],
[ 3.90000000e+01, 1.34000000e+05],
[ 4.10000000e+01, 7.10000000e+04],
[ 5.80000000e+01, 1.01000000e+05],
[ 4.70000000e+01, 4.70000000e+04],
[ 5.50000000e+01, 1.30000000e+05],
[ 5.20000000e+01, 1.14000000e+05],
[ 4.00000000e+01, 1.42000000e+05],
[ 4.60000000e+01, 2.20000000e+04],
[ 4.80000000e+01, 9.60000000e+04],
[ 5.20000000e+01, 1.50000000e+05],
[ 5.90000000e+01, 4.20000000e+04],
[ 3.50000000e+01, 5.80000000e+04],
[ 4.70000000e+01, 4.30000000e+04],
[ 6.00000000e+01, 1.08000000e+05],
[ 4.90000000e+01, 6.50000000e+04],
[ 4.00000000e+01, 7.80000000e+04],
[ 4.60000000e+01, 9.60000000e+04],
[ 5.90000000e+01, 1.43000000e+05],
[ 4.10000000e+01, 8.00000000e+04],
[ 3.50000000e+01, 9.10000000e+04],
[ 3.70000000e+01, 1.44000000e+05],
[ 6.00000000e+01, 1.02000000e+05],
[ 3.50000000e+01, 6.00000000e+04],
[ 3.70000000e+01, 5.30000000e+04],
[ 3.60000000e+01, 1.26000000e+05],
[ 5.60000000e+01, 1.33000000e+05],
[ 4.00000000e+01, 7.20000000e+04],
[ 4.20000000e+01, 8.00000000e+04],
[ 3.50000000e+01, 1.47000000e+05],
[ 3.90000000e+01, 4.20000000e+04],
[ 4.00000000e+01, 1.07000000e+05],
[ 4.90000000e+01, 8.60000000e+04],
[ 3.80000000e+01, 1.12000000e+05],
[ 4.60000000e+01, 7.90000000e+04],
[ 4.00000000e+01, 5.70000000e+04],
[ 3.70000000e+01, 8.00000000e+04],
[ 4.60000000e+01, 8.20000000e+04],
[ 5.30000000e+01, 1.43000000e+05],
[ 4.20000000e+01, 1.49000000e+05],
[ 3.80000000e+01, 5.90000000e+04],
[ 5.00000000e+01, 8.80000000e+04],
[ 5.60000000e+01, 1.04000000e+05],
[ 4.10000000e+01, 7.20000000e+04],
[ 5.10000000e+01, 1.46000000e+05],
[ 3.50000000e+01, 5.00000000e+04],
[ 5.70000000e+01, 1.22000000e+05],
[ 4.10000000e+01, 5.20000000e+04],
[ 3.50000000e+01, 9.70000000e+04],
[ 4.40000000e+01, 3.90000000e+04],
[ 3.70000000e+01, 5.20000000e+04],
[ 4.80000000e+01, 1.34000000e+05],
[ 3.70000000e+01, 1.46000000e+05],
[ 5.00000000e+01, 4.40000000e+04],
[ 5.20000000e+01, 9.00000000e+04],
[ 4.10000000e+01, 7.20000000e+04],
[ 4.00000000e+01, 5.70000000e+04],
[ 5.80000000e+01, 9.50000000e+04],
[ 4.50000000e+01, 1.31000000e+05],
[ 3.50000000e+01, 7.70000000e+04],
[ 3.60000000e+01, 1.44000000e+05],
[ 5.50000000e+01, 1.25000000e+05],
[ 3.50000000e+01, 7.20000000e+04],
[ 4.80000000e+01, 9.00000000e+04],
[ 4.20000000e+01, 1.08000000e+05],
[ 4.00000000e+01, 7.50000000e+04],
[ 3.70000000e+01, 7.40000000e+04],
[ 4.70000000e+01, 1.44000000e+05],
[ 4.00000000e+01, 6.10000000e+04],
[ 4.30000000e+01, 1.33000000e+05],
[ 5.90000000e+01, 7.60000000e+04],
[ 6.00000000e+01, 4.20000000e+04],
[ 3.90000000e+01, 1.06000000e+05],
[ 5.70000000e+01, 2.60000000e+04],
[ 5.70000000e+01, 7.40000000e+04],
[ 3.80000000e+01, 7.10000000e+04],
[ 4.90000000e+01, 8.80000000e+04],
[ 5.20000000e+01, 3.80000000e+04],
[ 5.00000000e+01, 3.60000000e+04],
[ 5.90000000e+01, 8.80000000e+04],
[ 3.50000000e+01, 6.10000000e+04],
[ 3.70000000e+01, 7.00000000e+04],
[ 5.20000000e+01, 2.10000000e+04],
[ 4.80000000e+01, 1.41000000e+05],
[ 3.70000000e+01, 9.30000000e+04],
[ 3.70000000e+01, 6.20000000e+04],
[ 4.80000000e+01, 1.38000000e+05],
[ 4.10000000e+01, 7.90000000e+04],
[ 3.70000000e+01, 7.80000000e+04],
[ 3.90000000e+01, 1.34000000e+05],
[ 4.90000000e+01, 8.90000000e+04],
[ 5.50000000e+01, 3.90000000e+04],
[ 3.70000000e+01, 7.70000000e+04],
[ 3.50000000e+01, 5.70000000e+04],
[ 3.60000000e+01, 6.30000000e+04],
[ 4.20000000e+01, 7.30000000e+04],
[ 4.30000000e+01, 1.12000000e+05],
[ 4.50000000e+01, 7.90000000e+04],
[ 4.60000000e+01, 1.17000000e+05],
[ 5.80000000e+01, 3.80000000e+04],
[ 4.80000000e+01, 7.40000000e+04],
[ 3.70000000e+01, 1.37000000e+05],
[ 3.70000000e+01, 7.90000000e+04],
[ 4.00000000e+01, 6.00000000e+04],
[ 4.20000000e+01, 5.40000000e+04],
[ 5.10000000e+01, 1.34000000e+05],
[ 4.70000000e+01, 1.13000000e+05],
[ 3.60000000e+01, 1.25000000e+05],
[ 3.80000000e+01, 5.00000000e+04],
[ 4.20000000e+01, 7.00000000e+04],
[ 3.90000000e+01, 9.60000000e+04],
[ 3.80000000e+01, 5.00000000e+04],
[ 4.90000000e+01, 1.41000000e+05],
[ 3.90000000e+01, 7.90000000e+04],
[ 3.90000000e+01, 7.50000000e+04],
[ 5.40000000e+01, 1.04000000e+05],
[ 3.50000000e+01, 5.50000000e+04],
[ 4.50000000e+01, 3.20000000e+04],
[ 3.60000000e+01, 6.00000000e+04],
[ 5.20000000e+01, 1.38000000e+05],
[ 5.30000000e+01, 8.20000000e+04],
[ 4.10000000e+01, 5.20000000e+04],
[ 4.80000000e+01, 3.00000000e+04],
[ 4.80000000e+01, 1.31000000e+05],
[ 4.10000000e+01, 6.00000000e+04],
[ 4.10000000e+01, 7.20000000e+04],
[ 4.20000000e+01, 7.50000000e+04],
[ 3.60000000e+01, 1.18000000e+05],
[ 4.70000000e+01, 1.07000000e+05],
[ 3.80000000e+01, 5.10000000e+04],
[ 4.80000000e+01, 1.19000000e+05],
[ 4.20000000e+01, 6.50000000e+04],
[ 4.00000000e+01, 6.50000000e+04],
[ 5.70000000e+01, 6.00000000e+04],
[ 3.60000000e+01, 5.40000000e+04],
[ 5.80000000e+01, 1.44000000e+05],
[ 3.50000000e+01, 7.90000000e+04],
[ 3.80000000e+01, 5.50000000e+04],
[ 3.90000000e+01, 1.22000000e+05],
[ 5.30000000e+01, 1.04000000e+05],
[ 3.50000000e+01, 7.50000000e+04],
[ 3.80000000e+01, 6.50000000e+04],
[ 4.70000000e+01, 5.10000000e+04],
[ 4.70000000e+01, 1.05000000e+05],
[ 4.10000000e+01, 6.30000000e+04],
[ 5.30000000e+01, 7.20000000e+04],
[ 5.40000000e+01, 1.08000000e+05],
[ 3.90000000e+01, 7.70000000e+04],
[ 3.80000000e+01, 6.10000000e+04],
[ 3.80000000e+01, 1.13000000e+05],
[ 3.70000000e+01, 7.50000000e+04],
[ 4.20000000e+01, 9.00000000e+04],
[ 3.70000000e+01, 5.70000000e+04],
[ 3.60000000e+01, 9.90000000e+04],
[ 6.00000000e+01, 3.40000000e+04],
[ 5.40000000e+01, 7.00000000e+04],
[ 4.10000000e+01, 7.20000000e+04],
[ 4.00000000e+01, 7.10000000e+04],
[ 4.20000000e+01, 5.40000000e+04],
[ 4.30000000e+01, 1.29000000e+05],
[ 5.30000000e+01, 3.40000000e+04],
[ 4.70000000e+01, 5.00000000e+04],
[ 4.20000000e+01, 7.90000000e+04],
[ 4.20000000e+01, 1.04000000e+05],
[ 5.90000000e+01, 2.90000000e+04],
[ 5.80000000e+01, 4.70000000e+04],
[ 4.60000000e+01, 8.80000000e+04],
[ 3.80000000e+01, 7.10000000e+04],
[ 5.40000000e+01, 2.60000000e+04],
[ 6.00000000e+01, 4.60000000e+04],
[ 6.00000000e+01, 8.30000000e+04],
[ 3.90000000e+01, 7.30000000e+04],
[ 5.90000000e+01, 1.30000000e+05],
[ 3.70000000e+01, 8.00000000e+04],
[ 4.60000000e+01, 3.20000000e+04],
[ 4.60000000e+01, 7.40000000e+04],
[ 4.20000000e+01, 5.30000000e+04],
[ 4.10000000e+01, 8.70000000e+04],
[ 5.80000000e+01, 2.30000000e+04],
[ 4.20000000e+01, 6.40000000e+04],
[ 4.80000000e+01, 3.30000000e+04],
[ 4.40000000e+01, 1.39000000e+05],
[ 4.90000000e+01, 2.80000000e+04],
[ 5.70000000e+01, 3.30000000e+04],
[ 5.60000000e+01, 6.00000000e+04],
[ 4.90000000e+01, 3.90000000e+04],
[ 3.90000000e+01, 7.10000000e+04],
[ 4.70000000e+01, 3.40000000e+04],
[ 4.80000000e+01, 3.50000000e+04],
[ 4.80000000e+01, 3.30000000e+04],
[ 4.70000000e+01, 2.30000000e+04],
[ 4.50000000e+01, 4.50000000e+04],
[ 6.00000000e+01, 4.20000000e+04],
[ 3.90000000e+01, 5.90000000e+04],
[ 4.60000000e+01, 4.10000000e+04],
[ 5.10000000e+01, 2.30000000e+04],
[ 5.00000000e+01, 2.00000000e+04],
[ 3.60000000e+01, 3.30000000e+04],
[ 4.90000000e+01, 3.60000000e+04]])In [5]:
y
Out[5]:
array([0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,
0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1,
0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1,
1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1,
1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1,
0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0,
1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1,
0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1,
0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1,
0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1,
1, 1, 1, 0, 1, 1, 1, 0, 1], dtype=int64)In [6]:
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)
In [7]:
from sklearn.preprocessing import StandardScaler # 導入標準化庫
sc_X = StandardScaler()
X_train = sc_X.fit_transform(X_train)
X_test = sc_X.transform(X_test)
X_test
Out[7]:
array([[-0.80480212, 0.50496393],
[-0.01254409, -0.5677824 ],
[-0.30964085, 0.1570462 ],
[-0.80480212, 0.27301877],
[-0.30964085, -0.5677824 ],
[-1.10189888, -1.43757673],
[-0.70576986, -1.58254245],
[-0.21060859, 2.15757314],
[-1.99318916, -0.04590581],
[ 0.8787462 , -0.77073441],
[-0.80480212, -0.59677555],
[-1.00286662, -0.42281668],
[-0.11157634, -0.42281668],
[ 0.08648817, 0.21503249],
[-1.79512465, 0.47597078],
[-0.60673761, 1.37475825],
[-0.11157634, 0.21503249],
[-1.89415691, 0.44697764],
[ 1.67100423, 1.75166912],
[-0.30964085, -1.37959044],
[-0.30964085, -0.65476184],
[ 0.8787462 , 2.15757314],
[ 0.28455268, -0.53878926],
[ 0.8787462 , 1.02684052],
[-1.49802789, -1.20563157],
[ 1.07681071, 2.07059371],
[-1.00286662, 0.50496393],
[-0.90383437, 0.30201192],
[-0.11157634, -0.21986468],
[-0.60673761, 0.47597078],
[-1.6960924 , 0.53395707],
[-0.11157634, 0.27301877],
[ 1.86906873, -0.27785096],
[-0.11157634, -0.48080297],
[-1.39899564, -0.33583725],
[-1.99318916, -0.50979612],
[-1.59706014, 0.33100506],
[-0.4086731 , -0.77073441],
[-0.70576986, -1.03167271],
[ 1.07681071, -0.97368642],
[-1.10189888, 0.53395707],
[ 0.28455268, -0.50979612],
[-1.10189888, 0.41798449],
[-0.30964085, -1.43757673],
[ 0.48261718, 1.22979253],
[-1.10189888, -0.33583725],
[-0.11157634, 0.30201192],
[ 1.37390747, 0.59194336],
[-1.20093113, -1.14764529],
[ 1.07681071, 0.47597078],
[ 1.86906873, 1.51972397],
[-0.4086731 , -1.29261101],
[-0.30964085, -0.3648304 ],
[-0.4086731 , 1.31677196],
[ 2.06713324, 0.53395707],
[ 0.68068169, -1.089659 ],
[-0.90383437, 0.38899135],
[-1.20093113, 0.30201192],
[ 1.07681071, -1.20563157],
[-1.49802789, -1.43757673],
[-0.60673761, -1.49556302],
[ 2.1661655 , -0.79972756],
[-1.89415691, 0.18603934],
[-0.21060859, 0.85288166],
[-1.89415691, -1.26361786],
[ 2.1661655 , 0.38899135],
[-1.39899564, 0.56295021],
[-1.10189888, -0.33583725],
[ 0.18552042, -0.65476184],
[ 0.38358493, 0.01208048],
[-0.60673761, 2.331532 ],
[-0.30964085, 0.21503249],
[-1.59706014, -0.19087153],
[ 0.68068169, -1.37959044],
[-1.10189888, 0.56295021],
[-1.99318916, 0.35999821],
[ 0.38358493, 0.27301877],
[ 0.18552042, -0.27785096],
[ 1.47293972, -1.03167271],
[ 0.8787462 , 1.08482681],
[ 1.96810099, 2.15757314],
[ 2.06713324, 0.38899135],
[-1.39899564, -0.42281668],
[-1.20093113, -1.00267957],
[ 1.96810099, -0.91570013],
[ 0.38358493, 0.30201192],
[ 0.18552042, 0.1570462 ],
[ 2.06713324, 1.75166912],
[ 0.77971394, -0.8287207 ],
[ 0.28455268, -0.27785096],
[ 0.38358493, -0.16187839],
[-0.11157634, 2.21555943],
[-1.49802789, -0.62576869],
[-1.29996338, -1.06066585],
[-1.39899564, 0.41798449],
[-1.10189888, 0.76590222],
[-1.49802789, -0.19087153],
[ 0.97777845, -1.06066585],
[ 0.97777845, 0.59194336],
[ 0.38358493, 0.99784738]])In [8]:
from sklearn.naive_bayes import GaussianNB
classifier = GaussianNB()
classifier.fit(X_train, y_train)
Out[8]:
GaussianNB(priors=None)
In [9]:
y_pred = classifier.predict(X_test)
In [10]:
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test,y_pred)
cm
Out[10]:
array([[65, 3],
[ 7, 25]])In [13]:
from matplotlib.colors import ListedColormap # 導入類
X_set, y_set = X_train, y_train # 賦值變量
X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),
np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))
plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),
alpha = 0.75, cmap = ListedColormap(('red', 'green'))) # 塗色紅色和綠色
plt.xlim(X1.min(), X1.max()) # 標註最大值，最小值
plt.ylim(X2.min(), X2.max()) # 標註最大值，最小值
for i, j in enumerate(np.unique(y_set)):
plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],
c = ListedColormap(('orange', 'blue'))(i), label = j)
plt.title(u'樸素貝葉斯（訓練集）')
plt.xlabel(u'年齡')
plt.ylabel(u'薪水')
plt.legend()
plt.show()
In [14]:
from matplotlib.colors import ListedColormap
X_set, y_set = X_test, y_test
X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),
np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))
plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),
alpha = 0.75, cmap = ListedColormap(('red', 'green')))
plt.xlim(X1.min(), X1.max())
plt.ylim(X2.min(), X2.max())
for i, j in enumerate(np.unique(y_set)):
plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],
c = ListedColormap(('orange', 'blue'))(i), label = j)
plt.title(u'樸素貝葉斯（測試集）')
plt.xlabel(u'年齡')
plt.ylabel(u'薪水')
plt.legend()
plt.show()
