对应代码和数据集下载地址：https://download.csdn.net/download/weixin_38935192/12569615

一、导入包

import pandas as pd
import numpy as np
%matplotlib inline   #调用matplotlib.pyplot的绘图函数plot()进行绘图的时候，或者生成一个figure画布的时候，可以直接在你的python console里面生成图像

二、数据导入 数据审核

data = pd.read_csv('creditcard.csv')
data
Time V1 V2 V3 V4 V5 V6 V7 V8 V9 ... V21 V22 V23 V24 V25 V26 V27 V28 Amount Class
0 0.0 -1.359807 -0.072781 2.536347 1.378155 -0.338321 0.462388 0.239599 0.098698 0.363787 ... -0.018307 0.277838 -0.110474 0.066928 0.128539 -0.189115 0.133558 -0.021053 149.62 0
1 0.0 1.191857 0.266151 0.166480 0.448154 0.060018 -0.082361 -0.078803 0.085102 -0.255425 ... -0.225775 -0.638672 0.101288 -0.339846 0.167170 0.125895 -0.008983 0.014724 2.69 0
2 1.0 -1.358354 -1.340163 1.773209 0.379780 -0.503198 1.800499 0.791461 0.247676 -1.514654 ... 0.247998 0.771679 0.909412 -0.689281 -0.327642 -0.139097 -0.055353 -0.059752 378.66 0
3 1.0 -0.966272 -0.185226 1.792993 -0.863291 -0.010309 1.247203 0.237609 0.377436 -1.387024 ... -0.108300 0.005274 -0.190321 -1.175575 0.647376 -0.221929 0.062723 0.061458 123.50 0
4 2.0 -1.158233 0.877737 1.548718 0.403034 -0.407193 0.095921 0.592941 -0.270533 0.817739 ... -0.009431 0.798278 -0.137458 0.141267 -0.206010 0.502292 0.219422 0.215153 69.99 0
5 2.0 -0.425966 0.960523 1.141109 -0.168252 0.420987 -0.029728 0.476201 0.260314 -0.568671 ... -0.208254 -0.559825 -0.026398 -0.371427 -0.232794 0.105915 0.253844 0.081080 3.67 0
6 4.0 1.229658 0.141004 0.045371 1.202613 0.191881 0.272708 -0.005159 0.081213 0.464960 ... -0.167716 -0.270710 -0.154104 -0.780055 0.750137 -0.257237 0.034507 0.005168 4.99 0
7 7.0 -0.644269 1.417964 1.074380 -0.492199 0.948934 0.428118 1.120631 -3.807864 0.615375 ... 1.943465 -1.015455 0.057504 -0.649709 -0.415267 -0.051634 -1.206921 -1.085339 40.80 0
8 7.0 -0.894286 0.286157 -0.113192 -0.271526 2.669599 3.721818 0.370145 0.851084 -0.392048 ... -0.073425 -0.268092 -0.204233 1.011592 0.373205 -0.384157 0.011747 0.142404 93.20 0
9 9.0 -0.338262 1.119593 1.044367 -0.222187 0.499361 -0.246761 0.651583 0.069539 -0.736727 ... -0.246914 -0.633753 -0.120794 -0.385050 -0.069733 0.094199 0.246219 0.083076 3.68 0
10 10.0 1.449044 -1.176339 0.913860 -1.375667 -1.971383 -0.629152 -1.423236 0.048456 -1.720408 ... -0.009302 0.313894 0.027740 0.500512 0.251367 -0.129478 0.042850 0.016253 7.80 0
11 10.0 0.384978 0.616109 -0.874300 -0.094019 2.924584 3.317027 0.470455 0.538247 -0.558895 ... 0.049924 0.238422 0.009130 0.996710 -0.767315 -0.492208 0.042472 -0.054337 9.99 0
12 10.0 1.249999 -1.221637 0.383930 -1.234899 -1.485419 -0.753230 -0.689405 -0.227487 -2.094011 ... -0.231809 -0.483285 0.084668 0.392831 0.161135 -0.354990 0.026416 0.042422 121.50 0
13 11.0 1.069374 0.287722 0.828613 2.712520 -0.178398 0.337544 -0.096717 0.115982 -0.221083 ... -0.036876 0.074412 -0.071407 0.104744 0.548265 0.104094 0.021491 0.021293 27.50 0
14 12.0 -2.791855 -0.327771 1.641750 1.767473 -0.136588 0.807596 -0.422911 -1.907107 0.755713 ... 1.151663 0.222182 1.020586 0.028317 -0.232746 -0.235557 -0.164778 -0.030154 58.80 0
15 12.0 -0.752417 0.345485 2.057323 -1.468643 -1.158394 -0.077850 -0.608581 0.003603 -0.436167 ... 0.499625 1.353650 -0.256573 -0.065084 -0.039124 -0.087086 -0.180998 0.129394 15.99 0
16 12.0 1.103215 -0.040296 1.267332 1.289091 -0.735997 0.288069 -0.586057 0.189380 0.782333 ... -0.024612 0.196002 0.013802 0.103758 0.364298 -0.382261 0.092809 0.037051 12.99 0
17 13.0 -0.436905 0.918966 0.924591 -0.727219 0.915679 -0.127867 0.707642 0.087962 -0.665271 ... -0.194796 -0.672638 -0.156858 -0.888386 -0.342413 -0.049027 0.079692 0.131024 0.89 0
18 14.0 -5.401258 -5.450148 1.186305 1.736239 3.049106 -1.763406 -1.559738 0.160842 1.233090 ... -0.503600 0.984460 2.458589 0.042119 -0.481631 -0.621272 0.392053 0.949594 46.80 0
19 15.0 1.492936 -1.029346 0.454795 -1.438026 -1.555434 -0.720961 -1.080664 -0.053127 -1.978682 ... -0.177650 -0.175074 0.040002 0.295814 0.332931 -0.220385 0.022298 0.007602 5.00 0
20 16.0 0.694885 -1.361819 1.029221 0.834159 -1.191209 1.309109 -0.878586 0.445290 -0.446196 ... -0.295583 -0.571955 -0.050881 -0.304215 0.072001 -0.422234 0.086553 0.063499 231.71 0
21 17.0 0.962496 0.328461 -0.171479 2.109204 1.129566 1.696038 0.107712 0.521502 -1.191311 ... 0.143997 0.402492 -0.048508 -1.371866 0.390814 0.199964 0.016371 -0.014605 34.09 0
22 18.0 1.166616 0.502120 -0.067300 2.261569 0.428804 0.089474 0.241147 0.138082 -0.989162 ... 0.018702 -0.061972 -0.103855 -0.370415 0.603200 0.108556 -0.040521 -0.011418 2.28 0
23 18.0 0.247491 0.277666 1.185471 -0.092603 -1.314394 -0.150116 -0.946365 -1.617935 1.544071 ... 1.650180 0.200454 -0.185353 0.423073 0.820591 -0.227632 0.336634 0.250475 22.75 0
24 22.0 -1.946525 -0.044901 -0.405570 -1.013057 2.941968 2.955053 -0.063063 0.855546 0.049967 ... -0.579526 -0.799229 0.870300 0.983421 0.321201 0.149650 0.707519 0.014600 0.89 0
25 22.0 -2.074295 -0.121482 1.322021 0.410008 0.295198 -0.959537 0.543985 -0.104627 0.475664 ... -0.403639 -0.227404 0.742435 0.398535 0.249212 0.274404 0.359969 0.243232 26.43 0
26 23.0 1.173285 0.353498 0.283905 1.133563 -0.172577 -0.916054 0.369025 -0.327260 -0.246651 ... 0.067003 0.227812 -0.150487 0.435045 0.724825 -0.337082 0.016368 0.030041 41.88 0
27 23.0 1.322707 -0.174041 0.434555 0.576038 -0.836758 -0.831083 -0.264905 -0.220982 -1.071425 ... -0.284376 -0.323357 -0.037710 0.347151 0.559639 -0.280158 0.042335 0.028822 16.00 0
28 23.0 -0.414289 0.905437 1.727453 1.473471 0.007443 -0.200331 0.740228 -0.029247 -0.593392 ... 0.077237 0.457331 -0.038500 0.642522 -0.183891 -0.277464 0.182687 0.152665 33.00 0
29 23.0 1.059387 -0.175319 1.266130 1.186110 -0.786002 0.578435 -0.767084 0.401046 0.699500 ... 0.013676 0.213734 0.014462 0.002951 0.294638 -0.395070 0.081461 0.024220 12.99 0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
284777 172764.0 2.079137 -0.028723 -1.343392 0.358000 -0.045791 -1.345452 0.227476 -0.378355 0.665911 ... 0.235758 0.829758 -0.002063 0.001344 0.262183 -0.105327 -0.022363 -0.060283 1.00 0
284778 172764.0 -0.764523 0.588379 -0.907599 -0.418847 0.901528 -0.760802 0.758545 0.414698 -0.730854 ... 0.003530 -0.431876 0.141759 0.587119 -0.200998 0.267337 -0.152951 -0.065285 80.00 0
284779 172766.0 1.975178 -0.616244 -2.628295 -0.406246 2.327804 3.664740 -0.533297 0.842937 1.128798 ... 0.086043 0.543613 -0.032129 0.768379 0.477688 -0.031833 0.014151 -0.066542 25.00 0
284780 172766.0 -1.727503 1.108356 2.219561 1.148583 -0.884199 0.793083 -0.527298 0.866429 0.853819 ... -0.094708 0.236818 -0.204280 1.158185 0.627801 -0.399981 0.510818 0.233265 30.00 0
284781 172766.0 -1.139015 -0.155510 1.894478 -1.138957 1.451777 0.093598 0.191353 0.092211 -0.062621 ... -0.191027 -0.631658 -0.147249 0.212931 0.354257 -0.241068 -0.161717 -0.149188 13.00 0
284782 172767.0 -0.268061 2.540315 -1.400915 4.846661 0.639105 0.186479 -0.045911 0.936448 -2.419986 ... -0.263889 -0.857904 0.235172 -0.681794 -0.668894 0.044657 -0.066751 -0.072447 12.82 0
284783 172768.0 -1.796092 1.929178 -2.828417 -1.689844 2.199572 3.123732 -0.270714 1.657495 0.465804 ... 0.271170 1.145750 0.084783 0.721269 -0.529906 -0.240117 0.129126 -0.080620 11.46 0
284784 172768.0 -0.669662 0.923769 -1.543167 -1.560729 2.833960 3.240843 0.181576 1.282746 -0.893890 ... 0.183856 0.202670 -0.373023 0.651122 1.073823 0.844590 -0.286676 -0.187719 40.00 0
284785 172768.0 0.032887 0.545338 -1.185844 -1.729828 2.932315 3.401529 0.337434 0.925377 -0.165663 ... -0.266113 -0.716336 0.108519 0.688519 -0.460220 0.161939 0.265368 0.090245 1.79 0
284786 172768.0 -2.076175 2.142238 -2.522704 -1.888063 1.982785 3.732950 -1.217430 -0.536644 0.272867 ... 2.016666 -1.588269 0.588482 0.632444 -0.201064 0.199251 0.438657 0.172923 8.95 0
284787 172769.0 -1.029719 -1.110670 -0.636179 -0.840816 2.424360 -2.956733 0.283610 -0.332656 -0.247488 ... 0.353722 0.488487 0.293632 0.107812 -0.935586 1.138216 0.025271 0.255347 9.99 0
284788 172770.0 2.007418 -0.280235 -0.208113 0.335261 -0.715798 -0.751373 -0.458972 -0.140140 0.959971 ... -0.208260 -0.430347 0.416765 0.064819 -0.608337 0.268436 -0.028069 -0.041367 3.99 0
284789 172770.0 -0.446951 1.302212 -0.168583 0.981577 0.578957 -0.605641 1.253430 -1.042610 -0.417116 ... 0.851800 0.305268 -0.148093 -0.038712 0.010209 -0.362666 0.503092 0.229921 60.50 0
284790 172771.0 -0.515513 0.971950 -1.014580 -0.677037 0.912430 -0.316187 0.396137 0.532364 -0.224606 ... -0.280302 -0.849919 0.300245 0.000607 -0.376379 0.128660 -0.015205 -0.021486 9.81 0
284791 172774.0 -0.863506 0.874701 0.420358 -0.530365 0.356561 -1.046238 0.757051 0.230473 -0.506856 ... -0.108846 -0.480820 -0.074513 -0.003988 -0.113149 0.280378 -0.077310 0.023079 20.32 0
284792 172774.0 -0.724123 1.485216 -1.132218 -0.607190 0.709499 -0.482638 0.548393 0.343003 -0.226323 ... 0.414621 1.307511 -0.059545 0.242669 -0.665424 -0.269869 -0.170579 -0.030692 3.99 0
284793 172775.0 1.971002 -0.699067 -1.697541 -0.617643 1.718797 3.911336 -1.259306 1.056209 1.315006 ... 0.188758 0.694418 0.163002 0.726365 -0.058282 -0.191813 0.061858 -0.043716 4.99 0
284794 172777.0 -1.266580 -0.400461 0.956221 -0.723919 1.531993 -1.788600 0.314741 0.004704 0.013857 ... -0.157831 -0.883365 0.088485 -0.076790 -0.095833 0.132720 -0.028468 0.126494 0.89 0
284795 172778.0 -12.516732 10.187818 -8.476671 -2.510473 -4.586669 -1.394465 -3.632516 5.498583 4.893089 ... -0.944759 -1.565026 0.890675 -1.253276 1.786717 0.320763 2.090712 1.232864 9.87 0
284796 172780.0 1.884849 -0.143540 -0.999943 1.506772 -0.035300 -0.613638 0.190241 -0.249058 0.666458 ... 0.144008 0.634646 -0.042114 -0.053206 0.316403 -0.461441 0.018265 -0.041068 60.00 0
284797 172782.0 -0.241923 0.712247 0.399806 -0.463406 0.244531 -1.343668 0.929369 -0.206210 0.106234 ... -0.228876 -0.514376 0.279598 0.371441 -0.559238 0.113144 0.131507 0.081265 5.49 0
284798 172782.0 0.219529 0.881246 -0.635891 0.960928 -0.152971 -1.014307 0.427126 0.121340 -0.285670 ... 0.099936 0.337120 0.251791 0.057688 -1.508368 0.144023 0.181205 0.215243 24.05 0
284799 172783.0 -1.775135 -0.004235 1.189786 0.331096 1.196063 5.519980 -1.518185 2.080825 1.159498 ... 0.103302 0.654850 -0.348929 0.745323 0.704545 -0.127579 0.454379 0.130308 79.99 0
284800 172784.0 2.039560 -0.175233 -1.196825 0.234580 -0.008713 -0.726571 0.017050 -0.118228 0.435402 ... -0.268048 -0.717211 0.297930 -0.359769 -0.315610 0.201114 -0.080826 -0.075071 2.68 0
284801 172785.0 0.120316 0.931005 -0.546012 -0.745097 1.130314 -0.235973 0.812722 0.115093 -0.204064 ... -0.314205 -0.808520 0.050343 0.102800 -0.435870 0.124079 0.217940 0.068803 2.69 0
284802 172786.0 -11.881118 10.071785 -9.834783 -2.066656 -5.364473 -2.606837 -4.918215 7.305334 1.914428 ... 0.213454 0.111864 1.014480 -0.509348 1.436807 0.250034 0.943651 0.823731 0.77 0
284803 172787.0 -0.732789 -0.055080 2.035030 -0.738589 0.868229 1.058415 0.024330 0.294869 0.584800 ... 0.214205 0.924384 0.012463 -1.016226 -0.606624 -0.395255 0.068472 -0.053527 24.79 0
284804 172788.0 1.919565 -0.301254 -3.249640 -0.557828 2.630515 3.031260 -0.296827 0.708417 0.432454 ... 0.232045 0.578229 -0.037501 0.640134 0.265745 -0.087371 0.004455 -0.026561 67.88 0
284805 172788.0 -0.240440 0.530483 0.702510 0.689799 -0.377961 0.623708 -0.686180 0.679145 0.392087 ... 0.265245 0.800049 -0.163298 0.123205 -0.569159 0.546668 0.108821 0.104533 10.00 0
284806 172792.0 -0.533413 -0.189733 0.703337 -0.506271 -0.012546 -0.649617 1.577006 -0.414650 0.486180 ... 0.261057 0.643078 0.376777 0.008797 -0.473649 -0.818267 -0.002415 0.013649 217.00 0

284807 rows × 31 columns

data.head()  #发现无法展示完全所有列
Time V1 V2 V3 V4 V5 V6 V7 V8 V9 ... V21 V22 V23 V24 V25 V26 V27 V28 Amount Class
0 0.0 -1.359807 -0.072781 2.536347 1.378155 -0.338321 0.462388 0.239599 0.098698 0.363787 ... -0.018307 0.277838 -0.110474 0.066928 0.128539 -0.189115 0.133558 -0.021053 149.62 0
1 0.0 1.191857 0.266151 0.166480 0.448154 0.060018 -0.082361 -0.078803 0.085102 -0.255425 ... -0.225775 -0.638672 0.101288 -0.339846 0.167170 0.125895 -0.008983 0.014724 2.69 0
2 1.0 -1.358354 -1.340163 1.773209 0.379780 -0.503198 1.800499 0.791461 0.247676 -1.514654 ... 0.247998 0.771679 0.909412 -0.689281 -0.327642 -0.139097 -0.055353 -0.059752 378.66 0
3 1.0 -0.966272 -0.185226 1.792993 -0.863291 -0.010309 1.247203 0.237609 0.377436 -1.387024 ... -0.108300 0.005274 -0.190321 -1.175575 0.647376 -0.221929 0.062723 0.061458 123.50 0
4 2.0 -1.158233 0.877737 1.548718 0.403034 -0.407193 0.095921 0.592941 -0.270533 0.817739 ... -0.009431 0.798278 -0.137458 0.141267 -0.206010 0.502292 0.219422 0.215153 69.99 0

5 rows × 31 columns

pd.set_option('display.max_column',40) #设定最大展示列数，目的是对表的列进行完全展示
data.head()#再次进行展示
Time V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13 V14 V15 V16 V17 V18 V19 V20 V21 V22 V23 V24 V25 V26 V27 V28 Amount Class
0 0.0 -1.359807 -0.072781 2.536347 1.378155 -0.338321 0.462388 0.239599 0.098698 0.363787 0.090794 -0.551600 -0.617801 -0.991390 -0.311169 1.468177 -0.470401 0.207971 0.025791 0.403993 0.251412 -0.018307 0.277838 -0.110474 0.066928 0.128539 -0.189115 0.133558 -0.021053 149.62 0
1 0.0 1.191857 0.266151 0.166480 0.448154 0.060018 -0.082361 -0.078803 0.085102 -0.255425 -0.166974 1.612727 1.065235 0.489095 -0.143772 0.635558 0.463917 -0.114805 -0.183361 -0.145783 -0.069083 -0.225775 -0.638672 0.101288 -0.339846 0.167170 0.125895 -0.008983 0.014724 2.69 0
2 1.0 -1.358354 -1.340163 1.773209 0.379780 -0.503198 1.800499 0.791461 0.247676 -1.514654 0.207643 0.624501 0.066084 0.717293 -0.165946 2.345865 -2.890083 1.109969 -0.121359 -2.261857 0.524980 0.247998 0.771679 0.909412 -0.689281 -0.327642 -0.139097 -0.055353 -0.059752 378.66 0
3 1.0 -0.966272 -0.185226 1.792993 -0.863291 -0.010309 1.247203 0.237609 0.377436 -1.387024 -0.054952 -0.226487 0.178228 0.507757 -0.287924 -0.631418 -1.059647 -0.684093 1.965775 -1.232622 -0.208038 -0.108300 0.005274 -0.190321 -1.175575 0.647376 -0.221929 0.062723 0.061458 123.50 0
4 2.0 -1.158233 0.877737 1.548718 0.403034 -0.407193 0.095921 0.592941 -0.270533 0.817739 0.753074 -0.822843 0.538196 1.345852 -1.119670 0.175121 -0.451449 -0.237033 -0.038195 0.803487 0.408542 -0.009431 0.798278 -0.137458 0.141267 -0.206010 0.502292 0.219422 0.215153 69.99 0 
data.shape

(284807, 31)

data.info() #无缺失值

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 284807 entries, 0 to 284806
Data columns (total 31 columns):
Time      284807 non-null float64
V1        284807 non-null float64
V2        284807 non-null float64
V3        284807 non-null float64
V4        284807 non-null float64
V5        284807 non-null float64
V6        284807 non-null float64
V7        284807 non-null float64
V8        284807 non-null float64
V9        284807 non-null float64
V10       284807 non-null float64
V11       284807 non-null float64
V12       284807 non-null float64
V13       284807 non-null float64
V14       284807 non-null float64
V15       284807 non-null float64
V16       284807 non-null float64
V17       284807 non-null float64
V18       284807 non-null float64
V19       284807 non-null float64
V20       284807 non-null float64
V21       284807 non-null float64
V22       284807 non-null float64
V23       284807 non-null float64
V24       284807 non-null float64
V25       284807 non-null float64
V26       284807 non-null float64
V27       284807 non-null float64
V28       284807 non-null float64
Amount    284807 non-null float64
Class     284807 non-null int64
dtypes: float64(30), int64(1)
memory usage: 67.4 MB

data.describe()
Time V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13 V14 V15 V16 V17 V18 V19 V20 V21 V22 V23 V24 V25 V26 V27 V28 Amount Class
count 284807.000000 2.848070e+05 2.848070e+05 2.848070e+05 2.848070e+05 2.848070e+05 2.848070e+05 2.848070e+05 2.848070e+05 2.848070e+05 2.848070e+05 2.848070e+05 2.848070e+05 2.848070e+05 2.848070e+05 2.848070e+05 2.848070e+05 2.848070e+05 2.848070e+05 2.848070e+05 2.848070e+05 2.848070e+05 2.848070e+05 2.848070e+05 2.848070e+05 2.848070e+05 2.848070e+05 2.848070e+05 2.848070e+05 284807.000000 284807.000000
mean 94813.859575 3.919560e-15 5.688174e-16 -8.769071e-15 2.782312e-15 -1.552563e-15 2.010663e-15 -1.694249e-15 -1.927028e-16 -3.137024e-15 1.768627e-15 9.170318e-16 -1.810658e-15 1.693438e-15 1.479045e-15 3.482336e-15 1.392007e-15 -7.528491e-16 4.328772e-16 9.049732e-16 5.085503e-16 1.537294e-16 7.959909e-16 5.367590e-16 4.458112e-15 1.453003e-15 1.699104e-15 -3.660161e-16 -1.206049e-16 88.349619 0.001727
std 47488.145955 1.958696e+00 1.651309e+00 1.516255e+00 1.415869e+00 1.380247e+00 1.332271e+00 1.237094e+00 1.194353e+00 1.098632e+00 1.088850e+00 1.020713e+00 9.992014e-01 9.952742e-01 9.585956e-01 9.153160e-01 8.762529e-01 8.493371e-01 8.381762e-01 8.140405e-01 7.709250e-01 7.345240e-01 7.257016e-01 6.244603e-01 6.056471e-01 5.212781e-01 4.822270e-01 4.036325e-01 3.300833e-01 250.120109 0.041527
min 0.000000 -5.640751e+01 -7.271573e+01 -4.832559e+01 -5.683171e+00 -1.137433e+02 -2.616051e+01 -4.355724e+01 -7.321672e+01 -1.343407e+01 -2.458826e+01 -4.797473e+00 -1.868371e+01 -5.791881e+00 -1.921433e+01 -4.498945e+00 -1.412985e+01 -2.516280e+01 -9.498746e+00 -7.213527e+00 -5.449772e+01 -3.483038e+01 -1.093314e+01 -4.480774e+01 -2.836627e+00 -1.029540e+01 -2.604551e+00 -2.256568e+01 -1.543008e+01 0.000000 0.000000
25% 54201.500000 -9.203734e-01 -5.985499e-01 -8.903648e-01 -8.486401e-01 -6.915971e-01 -7.682956e-01 -5.540759e-01 -2.086297e-01 -6.430976e-01 -5.354257e-01 -7.624942e-01 -4.055715e-01 -6.485393e-01 -4.255740e-01 -5.828843e-01 -4.680368e-01 -4.837483e-01 -4.988498e-01 -4.562989e-01 -2.117214e-01 -2.283949e-01 -5.423504e-01 -1.618463e-01 -3.545861e-01 -3.171451e-01 -3.269839e-01 -7.083953e-02 -5.295979e-02 5.600000 0.000000
50% 84692.000000 1.810880e-02 6.548556e-02 1.798463e-01 -1.984653e-02 -5.433583e-02 -2.741871e-01 4.010308e-02 2.235804e-02 -5.142873e-02 -9.291738e-02 -3.275735e-02 1.400326e-01 -1.356806e-02 5.060132e-02 4.807155e-02 6.641332e-02 -6.567575e-02 -3.636312e-03 3.734823e-03 -6.248109e-02 -2.945017e-02 6.781943e-03 -1.119293e-02 4.097606e-02 1.659350e-02 -5.213911e-02 1.342146e-03 1.124383e-02 22.000000 0.000000
75% 139320.500000 1.315642e+00 8.037239e-01 1.027196e+00 7.433413e-01 6.119264e-01 3.985649e-01 5.704361e-01 3.273459e-01 5.971390e-01 4.539234e-01 7.395934e-01 6.182380e-01 6.625050e-01 4.931498e-01 6.488208e-01 5.232963e-01 3.996750e-01 5.008067e-01 4.589494e-01 1.330408e-01 1.863772e-01 5.285536e-01 1.476421e-01 4.395266e-01 3.507156e-01 2.409522e-01 9.104512e-02 7.827995e-02 77.165000 0.000000
max 172792.000000 2.454930e+00 2.205773e+01 9.382558e+00 1.687534e+01 3.480167e+01 7.330163e+01 1.205895e+02 2.000721e+01 1.559499e+01 2.374514e+01 1.201891e+01 7.848392e+00 7.126883e+00 1.052677e+01 8.877742e+00 1.731511e+01 9.253526e+00 5.041069e+00 5.591971e+00 3.942090e+01 2.720284e+01 1.050309e+01 2.252841e+01 4.584549e+00 7.519589e+00 3.517346e+00 3.161220e+01 3.384781e+01 25691.160000 1.000000

三、类分布

data['Class'].value_counts() #可以看到类的分布属于不平衡的类型

0    284315
1       492
Name: Class, dtype: int64

import matplotlib.pyplot as plt
data['Class'].value_counts().plot(kind='bar') #绘制柱状图
plt.title('Fraud class')
plt.xlabel('Class')
plt.ylabel('Frequency')

Text(0,0.5,'Frequency')

四、数据分区

data
Time V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13 V14 V15 V16 V17 V18 V19 V20 V21 V22 V23 V24 V25 V26 V27 V28 Amount Class
0 0.0 -1.359807 -0.072781 2.536347 1.378155 -0.338321 0.462388 0.239599 0.098698 0.363787 0.090794 -0.551600 -0.617801 -0.991390 -0.311169 1.468177 -0.470401 0.207971 0.025791 0.403993 0.251412 -0.018307 0.277838 -0.110474 0.066928 0.128539 -0.189115 0.133558 -0.021053 149.62 0
1 0.0 1.191857 0.266151 0.166480 0.448154 0.060018 -0.082361 -0.078803 0.085102 -0.255425 -0.166974 1.612727 1.065235 0.489095 -0.143772 0.635558 0.463917 -0.114805 -0.183361 -0.145783 -0.069083 -0.225775 -0.638672 0.101288 -0.339846 0.167170 0.125895 -0.008983 0.014724 2.69 0
2 1.0 -1.358354 -1.340163 1.773209 0.379780 -0.503198 1.800499 0.791461 0.247676 -1.514654 0.207643 0.624501 0.066084 0.717293 -0.165946 2.345865 -2.890083 1.109969 -0.121359 -2.261857 0.524980 0.247998 0.771679 0.909412 -0.689281 -0.327642 -0.139097 -0.055353 -0.059752 378.66 0
3 1.0 -0.966272 -0.185226 1.792993 -0.863291 -0.010309 1.247203 0.237609 0.377436 -1.387024 -0.054952 -0.226487 0.178228 0.507757 -0.287924 -0.631418 -1.059647 -0.684093 1.965775 -1.232622 -0.208038 -0.108300 0.005274 -0.190321 -1.175575 0.647376 -0.221929 0.062723 0.061458 123.50 0
4 2.0 -1.158233 0.877737 1.548718 0.403034 -0.407193 0.095921 0.592941 -0.270533 0.817739 0.753074 -0.822843 0.538196 1.345852 -1.119670 0.175121 -0.451449 -0.237033 -0.038195 0.803487 0.408542 -0.009431 0.798278 -0.137458 0.141267 -0.206010 0.502292 0.219422 0.215153 69.99 0
5 2.0 -0.425966 0.960523 1.141109 -0.168252 0.420987 -0.029728 0.476201 0.260314 -0.568671 -0.371407 1.341262 0.359894 -0.358091 -0.137134 0.517617 0.401726 -0.058133 0.068653 -0.033194 0.084968 -0.208254 -0.559825 -0.026398 -0.371427 -0.232794 0.105915 0.253844 0.081080 3.67 0
6 4.0 1.229658 0.141004 0.045371 1.202613 0.191881 0.272708 -0.005159 0.081213 0.464960 -0.099254 -1.416907 -0.153826 -0.751063 0.167372 0.050144 -0.443587 0.002821 -0.611987 -0.045575 -0.219633 -0.167716 -0.270710 -0.154104 -0.780055 0.750137 -0.257237 0.034507 0.005168 4.99 0
7 7.0 -0.644269 1.417964 1.074380 -0.492199 0.948934 0.428118 1.120631 -3.807864 0.615375 1.249376 -0.619468 0.291474 1.757964 -1.323865 0.686133 -0.076127 -1.222127 -0.358222 0.324505 -0.156742 1.943465 -1.015455 0.057504 -0.649709 -0.415267 -0.051634 -1.206921 -1.085339 40.80 0
8 7.0 -0.894286 0.286157 -0.113192 -0.271526 2.669599 3.721818 0.370145 0.851084 -0.392048 -0.410430 -0.705117 -0.110452 -0.286254 0.074355 -0.328783 -0.210077 -0.499768 0.118765 0.570328 0.052736 -0.073425 -0.268092 -0.204233 1.011592 0.373205 -0.384157 0.011747 0.142404 93.20 0
9 9.0 -0.338262 1.119593 1.044367 -0.222187 0.499361 -0.246761 0.651583 0.069539 -0.736727 -0.366846 1.017614 0.836390 1.006844 -0.443523 0.150219 0.739453 -0.540980 0.476677 0.451773 0.203711 -0.246914 -0.633753 -0.120794 -0.385050 -0.069733 0.094199 0.246219 0.083076 3.68 0
10 10.0 1.449044 -1.176339 0.913860 -1.375667 -1.971383 -0.629152 -1.423236 0.048456 -1.720408 1.626659 1.199644 -0.671440 -0.513947 -0.095045 0.230930 0.031967 0.253415 0.854344 -0.221365 -0.387226 -0.009302 0.313894 0.027740 0.500512 0.251367 -0.129478 0.042850 0.016253 7.80 0
11 10.0 0.384978 0.616109 -0.874300 -0.094019 2.924584 3.317027 0.470455 0.538247 -0.558895 0.309755 -0.259116 -0.326143 -0.090047 0.362832 0.928904 -0.129487 -0.809979 0.359985 0.707664 0.125992 0.049924 0.238422 0.009130 0.996710 -0.767315 -0.492208 0.042472 -0.054337 9.99 0
12 10.0 1.249999 -1.221637 0.383930 -1.234899 -1.485419 -0.753230 -0.689405 -0.227487 -2.094011 1.323729 0.227666 -0.242682 1.205417 -0.317631 0.725675 -0.815612 0.873936 -0.847789 -0.683193 -0.102756 -0.231809 -0.483285 0.084668 0.392831 0.161135 -0.354990 0.026416 0.042422 121.50 0
13 11.0 1.069374 0.287722 0.828613 2.712520 -0.178398 0.337544 -0.096717 0.115982 -0.221083 0.460230 -0.773657 0.323387 -0.011076 -0.178485 -0.655564 -0.199925 0.124005 -0.980496 -0.982916 -0.153197 -0.036876 0.074412 -0.071407 0.104744 0.548265 0.104094 0.021491 0.021293 27.50 0
14 12.0 -2.791855 -0.327771 1.641750 1.767473 -0.136588 0.807596 -0.422911 -1.907107 0.755713 1.151087 0.844555 0.792944 0.370448 -0.734975 0.406796 -0.303058 -0.155869 0.778265 2.221868 -1.582122 1.151663 0.222182 1.020586 0.028317 -0.232746 -0.235557 -0.164778 -0.030154 58.80 0
15 12.0 -0.752417 0.345485 2.057323 -1.468643 -1.158394 -0.077850 -0.608581 0.003603 -0.436167 0.747731 -0.793981 -0.770407 1.047627 -1.066604 1.106953 1.660114 -0.279265 -0.419994 0.432535 0.263451 0.499625 1.353650 -0.256573 -0.065084 -0.039124 -0.087086 -0.180998 0.129394 15.99 0
16 12.0 1.103215 -0.040296 1.267332 1.289091 -0.735997 0.288069 -0.586057 0.189380 0.782333 -0.267975 -0.450311 0.936708 0.708380 -0.468647 0.354574 -0.246635 -0.009212 -0.595912 -0.575682 -0.113910 -0.024612 0.196002 0.013802 0.103758 0.364298 -0.382261 0.092809 0.037051 12.99 0
17 13.0 -0.436905 0.918966 0.924591 -0.727219 0.915679 -0.127867 0.707642 0.087962 -0.665271 -0.737980 0.324098 0.277192 0.252624 -0.291896 -0.184520 1.143174 -0.928709 0.680470 0.025436 -0.047021 -0.194796 -0.672638 -0.156858 -0.888386 -0.342413 -0.049027 0.079692 0.131024 0.89 0
18 14.0 -5.401258 -5.450148 1.186305 1.736239 3.049106 -1.763406 -1.559738 0.160842 1.233090 0.345173 0.917230 0.970117 -0.266568 -0.479130 -0.526609 0.472004 -0.725481 0.075081 -0.406867 -2.196848 -0.503600 0.984460 2.458589 0.042119 -0.481631 -0.621272 0.392053 0.949594 46.80 0
19 15.0 1.492936 -1.029346 0.454795 -1.438026 -1.555434 -0.720961 -1.080664 -0.053127 -1.978682 1.638076 1.077542 -0.632047 -0.416957 0.052011 -0.042979 -0.166432 0.304241 0.554432 0.054230 -0.387910 -0.177650 -0.175074 0.040002 0.295814 0.332931 -0.220385 0.022298 0.007602 5.00 0
20 16.0 0.694885 -1.361819 1.029221 0.834159 -1.191209 1.309109 -0.878586 0.445290 -0.446196 0.568521 1.019151 1.298329 0.420480 -0.372651 -0.807980 -2.044557 0.515663 0.625847 -1.300408 -0.138334 -0.295583 -0.571955 -0.050881 -0.304215 0.072001 -0.422234 0.086553 0.063499 231.71 0
21 17.0 0.962496 0.328461 -0.171479 2.109204 1.129566 1.696038 0.107712 0.521502 -1.191311 0.724396 1.690330 0.406774 -0.936421 0.983739 0.710911 -0.602232 0.402484 -1.737162 -2.027612 -0.269321 0.143997 0.402492 -0.048508 -1.371866 0.390814 0.199964 0.016371 -0.014605 34.09 0
22 18.0 1.166616 0.502120 -0.067300 2.261569 0.428804 0.089474 0.241147 0.138082 -0.989162 0.922175 0.744786 -0.531377 -2.105346 1.126870 0.003075 0.424425 -0.454475 -0.098871 -0.816597 -0.307169 0.018702 -0.061972 -0.103855 -0.370415 0.603200 0.108556 -0.040521 -0.011418 2.28 0
23 18.0 0.247491 0.277666 1.185471 -0.092603 -1.314394 -0.150116 -0.946365 -1.617935 1.544071 -0.829881 -0.583200 0.524933 -0.453375 0.081393 1.555204 -1.396895 0.783131 0.436621 2.177807 -0.230983 1.650180 0.200454 -0.185353 0.423073 0.820591 -0.227632 0.336634 0.250475 22.75 0
24 22.0 -1.946525 -0.044901 -0.405570 -1.013057 2.941968 2.955053 -0.063063 0.855546 0.049967 0.573743 -0.081257 -0.215745 0.044161 0.033898 1.190718 0.578843 -0.975667 0.044063 0.488603 -0.216715 -0.579526 -0.799229 0.870300 0.983421 0.321201 0.149650 0.707519 0.014600 0.89 0
25 22.0 -2.074295 -0.121482 1.322021 0.410008 0.295198 -0.959537 0.543985 -0.104627 0.475664 0.149451 -0.856566 -0.180523 -0.655233 -0.279797 -0.211668 -0.333321 0.010751 -0.488473 0.505751 -0.386694 -0.403639 -0.227404 0.742435 0.398535 0.249212 0.274404 0.359969 0.243232 26.43 0
26 23.0 1.173285 0.353498 0.283905 1.133563 -0.172577 -0.916054 0.369025 -0.327260 -0.246651 -0.046139 -0.143419 0.979350 1.492285 0.101418 0.761478 -0.014584 -0.511640 -0.325056 -0.390934 0.027878 0.067003 0.227812 -0.150487 0.435045 0.724825 -0.337082 0.016368 0.030041 41.88 0
27 23.0 1.322707 -0.174041 0.434555 0.576038 -0.836758 -0.831083 -0.264905 -0.220982 -1.071425 0.868559 -0.641506 -0.111316 0.361485 0.171945 0.782167 -1.355871 -0.216935 1.271765 -1.240622 -0.522951 -0.284376 -0.323357 -0.037710 0.347151 0.559639 -0.280158 0.042335 0.028822 16.00 0
28 23.0 -0.414289 0.905437 1.727453 1.473471 0.007443 -0.200331 0.740228 -0.029247 -0.593392 -0.346188 -0.012142 0.786796 0.635954 -0.086324 0.076804 -1.405919 0.775592 -0.942889 0.543969 0.097308 0.077237 0.457331 -0.038500 0.642522 -0.183891 -0.277464 0.182687 0.152665 33.00 0
29 23.0 1.059387 -0.175319 1.266130 1.186110 -0.786002 0.578435 -0.767084 0.401046 0.699500 -0.064738 1.048292 1.005618 -0.542002 -0.039915 -0.218683 0.004476 -0.193554 0.042388 -0.277834 -0.178023 0.013676 0.213734 0.014462 0.002951 0.294638 -0.395070 0.081461 0.024220 12.99 0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
284777 172764.0 2.079137 -0.028723 -1.343392 0.358000 -0.045791 -1.345452 0.227476 -0.378355 0.665911 0.028351 -0.822911 0.049716 -0.352007 0.569087 0.791917 -0.178713 -0.500821 0.007475 -0.205470 -0.272447 0.235758 0.829758 -0.002063 0.001344 0.262183 -0.105327 -0.022363 -0.060283 1.00 0
284778 172764.0 -0.764523 0.588379 -0.907599 -0.418847 0.901528 -0.760802 0.758545 0.414698 -0.730854 -1.245088 0.874312 0.162691 -1.011819 -0.317789 -0.887823 0.482847 0.681412 0.486991 -0.022064 0.024870 0.003530 -0.431876 0.141759 0.587119 -0.200998 0.267337 -0.152951 -0.065285 80.00 0
284779 172766.0 1.975178 -0.616244 -2.628295 -0.406246 2.327804 3.664740 -0.533297 0.842937 1.128798 -0.220744 -0.590756 0.654596 -0.479779 0.120626 -0.907042 -1.186261 0.146953 -0.681104 0.295402 -0.168378 0.086043 0.543613 -0.032129 0.768379 0.477688 -0.031833 0.014151 -0.066542 25.00 0
284780 172766.0 -1.727503 1.108356 2.219561 1.148583 -0.884199 0.793083 -0.527298 0.866429 0.853819 -0.195152 -1.296770 0.725295 0.454177 -0.821823 -0.713585 -0.611659 0.457890 0.092557 0.757367 0.331940 -0.094708 0.236818 -0.204280 1.158185 0.627801 -0.399981 0.510818 0.233265 30.00 0
284781 172766.0 -1.139015 -0.155510 1.894478 -1.138957 1.451777 0.093598 0.191353 0.092211 -0.062621 -0.792066 0.659941 0.995402 0.861816 -0.300746 -0.188434 0.267385 -0.991270 0.290285 0.546517 0.341409 -0.191027 -0.631658 -0.147249 0.212931 0.354257 -0.241068 -0.161717 -0.149188 13.00 0
284782 172767.0 -0.268061 2.540315 -1.400915 4.846661 0.639105 0.186479 -0.045911 0.936448 -2.419986 0.525012 1.045386 0.124585 0.572344 -1.820716 -0.138285 1.100173 2.322135 1.349583 1.091523 0.111808 -0.263889 -0.857904 0.235172 -0.681794 -0.668894 0.044657 -0.066751 -0.072447 12.82 0
284783 172768.0 -1.796092 1.929178 -2.828417 -1.689844 2.199572 3.123732 -0.270714 1.657495 0.465804 0.832931 -0.344690 0.247153 -0.119068 0.849765 0.903389 -0.398816 -0.302906 0.036545 -0.202990 0.319366 0.271170 1.145750 0.084783 0.721269 -0.529906 -0.240117 0.129126 -0.080620 11.46 0
284784 172768.0 -0.669662 0.923769 -1.543167 -1.560729 2.833960 3.240843 0.181576 1.282746 -0.893890 -1.453432 0.187488 -0.390794 -0.289171 -0.510320 0.955637 0.553781 0.567862 0.409517 -0.671301 0.000965 0.183856 0.202670 -0.373023 0.651122 1.073823 0.844590 -0.286676 -0.187719 40.00 0
284785 172768.0 0.032887 0.545338 -1.185844 -1.729828 2.932315 3.401529 0.337434 0.925377 -0.165663 -0.386953 -0.199626 0.032017 -0.374731 0.354051 0.041228 -0.154750 -0.482455 -0.842462 -0.206385 0.022677 -0.266113 -0.716336 0.108519 0.688519 -0.460220 0.161939 0.265368 0.090245 1.79 0
284786 172768.0 -2.076175 2.142238 -2.522704 -1.888063 1.982785 3.732950 -1.217430 -0.536644 0.272867 0.300342 -0.451656 0.566368 -0.317804 0.855742 -0.041047 0.046620 0.017822 -0.772916 -0.354163 -0.308523 2.016666 -1.588269 0.588482 0.632444 -0.201064 0.199251 0.438657 0.172923 8.95 0
284787 172769.0 -1.029719 -1.110670 -0.636179 -0.840816 2.424360 -2.956733 0.283610 -0.332656 -0.247488 -0.328271 -1.089397 -0.694904 -1.315146 0.946281 -0.073008 -0.307347 -0.274712 -0.487247 -0.014378 0.218776 0.353722 0.488487 0.293632 0.107812 -0.935586 1.138216 0.025271 0.255347 9.99 0
284788 172770.0 2.007418 -0.280235 -0.208113 0.335261 -0.715798 -0.751373 -0.458972 -0.140140 0.959971 -0.028284 -0.635200 0.869261 0.996596 -0.280317 0.436079 0.397439 -0.557940 -0.536840 -0.082349 -0.143294 -0.208260 -0.430347 0.416765 0.064819 -0.608337 0.268436 -0.028069 -0.041367 3.99 0
284789 172770.0 -0.446951 1.302212 -0.168583 0.981577 0.578957 -0.605641 1.253430 -1.042610 -0.417116 0.076605 -1.291228 -0.690868 -1.481724 0.753473 -0.191141 -1.129904 0.319074 -0.201862 0.744416 -0.203306 0.851800 0.305268 -0.148093 -0.038712 0.010209 -0.362666 0.503092 0.229921 60.50 0
284790 172771.0 -0.515513 0.971950 -1.014580 -0.677037 0.912430 -0.316187 0.396137 0.532364 -0.224606 -0.753365 0.362990 0.110499 -0.853503 -0.383112 -0.774650 0.764540 0.216671 0.488508 0.180495 -0.177211 -0.280302 -0.849919 0.300245 0.000607 -0.376379 0.128660 -0.015205 -0.021486 9.81 0
284791 172774.0 -0.863506 0.874701 0.420358 -0.530365 0.356561 -1.046238 0.757051 0.230473 -0.506856 -1.032990 -1.187546 0.055871 -0.352509 0.550972 -0.551838 -0.229314 0.004179 -0.730361 0.077652 -0.162132 -0.108846 -0.480820 -0.074513 -0.003988 -0.113149 0.280378 -0.077310 0.023079 20.32 0
284792 172774.0 -0.724123 1.485216 -1.132218 -0.607190 0.709499 -0.482638 0.548393 0.343003 -0.226323 -0.348134 -1.381624 0.617933 1.428297 0.494490 0.426827 -0.229599 -0.494065 0.176169 0.182152 -0.077202 0.414621 1.307511 -0.059545 0.242669 -0.665424 -0.269869 -0.170579 -0.030692 3.99 0
284793 172775.0 1.971002 -0.699067 -1.697541 -0.617643 1.718797 3.911336 -1.259306 1.056209 1.315006 -0.146827 -0.222959 0.496509 -0.051785 -0.000744 0.893684 0.000363 -0.583710 0.074163 -0.356789 -0.153581 0.188758 0.694418 0.163002 0.726365 -0.058282 -0.191813 0.061858 -0.043716 4.99 0
284794 172777.0 -1.266580 -0.400461 0.956221 -0.723919 1.531993 -1.788600 0.314741 0.004704 0.013857 -0.815911 -1.311976 -0.946753 -2.119626 0.609469 -0.348707 0.077871 -0.357628 -0.578977 -0.518738 -0.029539 -0.157831 -0.883365 0.088485 -0.076790 -0.095833 0.132720 -0.028468 0.126494 0.89 0
284795 172778.0 -12.516732 10.187818 -8.476671 -2.510473 -4.586669 -1.394465 -3.632516 5.498583 4.893089 8.655320 -1.052365 2.834865 1.088141 1.288401 -0.931503 1.067864 0.586179 -0.085524 -0.571605 3.490065 -0.944759 -1.565026 0.890675 -1.253276 1.786717 0.320763 2.090712 1.232864 9.87 0
284796 172780.0 1.884849 -0.143540 -0.999943 1.506772 -0.035300 -0.613638 0.190241 -0.249058 0.666458 0.120908 -1.134176 0.677729 0.345928 0.002019 -0.679626 -0.549982 -0.199950 -0.420551 -0.283278 -0.153997 0.144008 0.634646 -0.042114 -0.053206 0.316403 -0.461441 0.018265 -0.041068 60.00 0
284797 172782.0 -0.241923 0.712247 0.399806 -0.463406 0.244531 -1.343668 0.929369 -0.206210 0.106234 -0.284708 -0.612982 -0.066655 -0.732987 0.237948 -0.293959 -0.245496 -0.174846 -0.891169 -0.290491 -0.139512 -0.228876 -0.514376 0.279598 0.371441 -0.559238 0.113144 0.131507 0.081265 5.49 0
284798 172782.0 0.219529 0.881246 -0.635891 0.960928 -0.152971 -1.014307 0.427126 0.121340 -0.285670 -0.111640 -1.109232 -0.453235 -1.046946 1.122674 1.243518 -1.431897 0.939328 -0.002373 2.894952 0.006666 0.099936 0.337120 0.251791 0.057688 -1.508368 0.144023 0.181205 0.215243 24.05 0
284799 172783.0 -1.775135 -0.004235 1.189786 0.331096 1.196063 5.519980 -1.518185 2.080825 1.159498 -0.594242 -1.264072 0.453596 -0.243142 -0.858719 -0.766554 -0.644646 0.447184 0.388721 0.792135 0.348176 0.103302 0.654850 -0.348929 0.745323 0.704545 -0.127579 0.454379 0.130308 79.99 0
284800 172784.0 2.039560 -0.175233 -1.196825 0.234580 -0.008713 -0.726571 0.017050 -0.118228 0.435402 0.267772 0.523316 0.559047 -0.834660 0.626211 -0.541494 0.225361 -0.605252 -0.163049 0.561695 -0.256922 -0.268048 -0.717211 0.297930 -0.359769 -0.315610 0.201114 -0.080826 -0.075071 2.68 0
284801 172785.0 0.120316 0.931005 -0.546012 -0.745097 1.130314 -0.235973 0.812722 0.115093 -0.204064 -0.657422 0.644837 0.190916 -0.546329 -0.731707 -0.808036 0.599628 0.070441 0.373110 0.128904 0.000676 -0.314205 -0.808520 0.050343 0.102800 -0.435870 0.124079 0.217940 0.068803 2.69 0
284802 172786.0 -11.881118 10.071785 -9.834783 -2.066656 -5.364473 -2.606837 -4.918215 7.305334 1.914428 4.356170 -1.593105 2.711941 -0.689256 4.626942 -0.924459 1.107641 1.991691 0.510632 -0.682920 1.475829 0.213454 0.111864 1.014480 -0.509348 1.436807 0.250034 0.943651 0.823731 0.77 0
284803 172787.0 -0.732789 -0.055080 2.035030 -0.738589 0.868229 1.058415 0.024330 0.294869 0.584800 -0.975926 -0.150189 0.915802 1.214756 -0.675143 1.164931 -0.711757 -0.025693 -1.221179 -1.545556 0.059616 0.214205 0.924384 0.012463 -1.016226 -0.606624 -0.395255 0.068472 -0.053527 24.79 0
284804 172788.0 1.919565 -0.301254 -3.249640 -0.557828 2.630515 3.031260 -0.296827 0.708417 0.432454 -0.484782 0.411614 0.063119 -0.183699 -0.510602 1.329284 0.140716 0.313502 0.395652 -0.577252 0.001396 0.232045 0.578229 -0.037501 0.640134 0.265745 -0.087371 0.004455 -0.026561 67.88 0
284805 172788.0 -0.240440 0.530483 0.702510 0.689799 -0.377961 0.623708 -0.686180 0.679145 0.392087 -0.399126 -1.933849 -0.962886 -1.042082 0.449624 1.962563 -0.608577 0.509928 1.113981 2.897849 0.127434 0.265245 0.800049 -0.163298 0.123205 -0.569159 0.546668 0.108821 0.104533 10.00 0
284806 172792.0 -0.533413 -0.189733 0.703337 -0.506271 -0.012546 -0.649617 1.577006 -0.414650 0.486180 -0.915427 -1.040458 -0.031513 -0.188093 -0.084316 0.041333 -0.302620 -0.660377 0.167430 -0.256117 0.382948 0.261057 0.643078 0.376777 0.008797 -0.473649 -0.818267 -0.002415 0.013649 217.00 0

284807 rows × 31 columns

target=data['Class'] #将class属性提取且转换为numpy数组
target

0         0
1         0
2         0
3         0
4         0
5         0
6         0
7         0
8         0
9         0
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11        0
12        0
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17        0
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21        0
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23        0
24        0
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26        0
27        0
28        0
29        0..
284777    0
284778    0
284779    0
284780    0
284781    0
284782    0
284783    0
284784    0
284785    0
284786    0
284787    0
284788    0
284789    0
284790    0
284791    0
284792    0
284793    0
284794    0
284795    0
284796    0
284797    0
284798    0
284799    0
284800    0
284801    0
284802    0
284803    0
284804    0
284805    0
284806    0
Name: Class, Length: 284807, dtype: int64

y=np.array(target)

(284807,)

data.drop('Class',axis=1) #axis=1定义按列操作
Time V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13 V14 V15 V16 V17 V18 V19 V20 V21 V22 V23 V24 V25 V26 V27 V28 Amount
0 0.0 -1.359807 -0.072781 2.536347 1.378155 -0.338321 0.462388 0.239599 0.098698 0.363787 0.090794 -0.551600 -0.617801 -0.991390 -0.311169 1.468177 -0.470401 0.207971 0.025791 0.403993 0.251412 -0.018307 0.277838 -0.110474 0.066928 0.128539 -0.189115 0.133558 -0.021053 149.62
1 0.0 1.191857 0.266151 0.166480 0.448154 0.060018 -0.082361 -0.078803 0.085102 -0.255425 -0.166974 1.612727 1.065235 0.489095 -0.143772 0.635558 0.463917 -0.114805 -0.183361 -0.145783 -0.069083 -0.225775 -0.638672 0.101288 -0.339846 0.167170 0.125895 -0.008983 0.014724 2.69
2 1.0 -1.358354 -1.340163 1.773209 0.379780 -0.503198 1.800499 0.791461 0.247676 -1.514654 0.207643 0.624501 0.066084 0.717293 -0.165946 2.345865 -2.890083 1.109969 -0.121359 -2.261857 0.524980 0.247998 0.771679 0.909412 -0.689281 -0.327642 -0.139097 -0.055353 -0.059752 378.66
3 1.0 -0.966272 -0.185226 1.792993 -0.863291 -0.010309 1.247203 0.237609 0.377436 -1.387024 -0.054952 -0.226487 0.178228 0.507757 -0.287924 -0.631418 -1.059647 -0.684093 1.965775 -1.232622 -0.208038 -0.108300 0.005274 -0.190321 -1.175575 0.647376 -0.221929 0.062723 0.061458 123.50
4 2.0 -1.158233 0.877737 1.548718 0.403034 -0.407193 0.095921 0.592941 -0.270533 0.817739 0.753074 -0.822843 0.538196 1.345852 -1.119670 0.175121 -0.451449 -0.237033 -0.038195 0.803487 0.408542 -0.009431 0.798278 -0.137458 0.141267 -0.206010 0.502292 0.219422 0.215153 69.99
5 2.0 -0.425966 0.960523 1.141109 -0.168252 0.420987 -0.029728 0.476201 0.260314 -0.568671 -0.371407 1.341262 0.359894 -0.358091 -0.137134 0.517617 0.401726 -0.058133 0.068653 -0.033194 0.084968 -0.208254 -0.559825 -0.026398 -0.371427 -0.232794 0.105915 0.253844 0.081080 3.67
6 4.0 1.229658 0.141004 0.045371 1.202613 0.191881 0.272708 -0.005159 0.081213 0.464960 -0.099254 -1.416907 -0.153826 -0.751063 0.167372 0.050144 -0.443587 0.002821 -0.611987 -0.045575 -0.219633 -0.167716 -0.270710 -0.154104 -0.780055 0.750137 -0.257237 0.034507 0.005168 4.99
7 7.0 -0.644269 1.417964 1.074380 -0.492199 0.948934 0.428118 1.120631 -3.807864 0.615375 1.249376 -0.619468 0.291474 1.757964 -1.323865 0.686133 -0.076127 -1.222127 -0.358222 0.324505 -0.156742 1.943465 -1.015455 0.057504 -0.649709 -0.415267 -0.051634 -1.206921 -1.085339 40.80
8 7.0 -0.894286 0.286157 -0.113192 -0.271526 2.669599 3.721818 0.370145 0.851084 -0.392048 -0.410430 -0.705117 -0.110452 -0.286254 0.074355 -0.328783 -0.210077 -0.499768 0.118765 0.570328 0.052736 -0.073425 -0.268092 -0.204233 1.011592 0.373205 -0.384157 0.011747 0.142404 93.20
9 9.0 -0.338262 1.119593 1.044367 -0.222187 0.499361 -0.246761 0.651583 0.069539 -0.736727 -0.366846 1.017614 0.836390 1.006844 -0.443523 0.150219 0.739453 -0.540980 0.476677 0.451773 0.203711 -0.246914 -0.633753 -0.120794 -0.385050 -0.069733 0.094199 0.246219 0.083076 3.68
10 10.0 1.449044 -1.176339 0.913860 -1.375667 -1.971383 -0.629152 -1.423236 0.048456 -1.720408 1.626659 1.199644 -0.671440 -0.513947 -0.095045 0.230930 0.031967 0.253415 0.854344 -0.221365 -0.387226 -0.009302 0.313894 0.027740 0.500512 0.251367 -0.129478 0.042850 0.016253 7.80
11 10.0 0.384978 0.616109 -0.874300 -0.094019 2.924584 3.317027 0.470455 0.538247 -0.558895 0.309755 -0.259116 -0.326143 -0.090047 0.362832 0.928904 -0.129487 -0.809979 0.359985 0.707664 0.125992 0.049924 0.238422 0.009130 0.996710 -0.767315 -0.492208 0.042472 -0.054337 9.99
12 10.0 1.249999 -1.221637 0.383930 -1.234899 -1.485419 -0.753230 -0.689405 -0.227487 -2.094011 1.323729 0.227666 -0.242682 1.205417 -0.317631 0.725675 -0.815612 0.873936 -0.847789 -0.683193 -0.102756 -0.231809 -0.483285 0.084668 0.392831 0.161135 -0.354990 0.026416 0.042422 121.50
13 11.0 1.069374 0.287722 0.828613 2.712520 -0.178398 0.337544 -0.096717 0.115982 -0.221083 0.460230 -0.773657 0.323387 -0.011076 -0.178485 -0.655564 -0.199925 0.124005 -0.980496 -0.982916 -0.153197 -0.036876 0.074412 -0.071407 0.104744 0.548265 0.104094 0.021491 0.021293 27.50
14 12.0 -2.791855 -0.327771 1.641750 1.767473 -0.136588 0.807596 -0.422911 -1.907107 0.755713 1.151087 0.844555 0.792944 0.370448 -0.734975 0.406796 -0.303058 -0.155869 0.778265 2.221868 -1.582122 1.151663 0.222182 1.020586 0.028317 -0.232746 -0.235557 -0.164778 -0.030154 58.80
15 12.0 -0.752417 0.345485 2.057323 -1.468643 -1.158394 -0.077850 -0.608581 0.003603 -0.436167 0.747731 -0.793981 -0.770407 1.047627 -1.066604 1.106953 1.660114 -0.279265 -0.419994 0.432535 0.263451 0.499625 1.353650 -0.256573 -0.065084 -0.039124 -0.087086 -0.180998 0.129394 15.99
16 12.0 1.103215 -0.040296 1.267332 1.289091 -0.735997 0.288069 -0.586057 0.189380 0.782333 -0.267975 -0.450311 0.936708 0.708380 -0.468647 0.354574 -0.246635 -0.009212 -0.595912 -0.575682 -0.113910 -0.024612 0.196002 0.013802 0.103758 0.364298 -0.382261 0.092809 0.037051 12.99
17 13.0 -0.436905 0.918966 0.924591 -0.727219 0.915679 -0.127867 0.707642 0.087962 -0.665271 -0.737980 0.324098 0.277192 0.252624 -0.291896 -0.184520 1.143174 -0.928709 0.680470 0.025436 -0.047021 -0.194796 -0.672638 -0.156858 -0.888386 -0.342413 -0.049027 0.079692 0.131024 0.89
18 14.0 -5.401258 -5.450148 1.186305 1.736239 3.049106 -1.763406 -1.559738 0.160842 1.233090 0.345173 0.917230 0.970117 -0.266568 -0.479130 -0.526609 0.472004 -0.725481 0.075081 -0.406867 -2.196848 -0.503600 0.984460 2.458589 0.042119 -0.481631 -0.621272 0.392053 0.949594 46.80
19 15.0 1.492936 -1.029346 0.454795 -1.438026 -1.555434 -0.720961 -1.080664 -0.053127 -1.978682 1.638076 1.077542 -0.632047 -0.416957 0.052011 -0.042979 -0.166432 0.304241 0.554432 0.054230 -0.387910 -0.177650 -0.175074 0.040002 0.295814 0.332931 -0.220385 0.022298 0.007602 5.00
20 16.0 0.694885 -1.361819 1.029221 0.834159 -1.191209 1.309109 -0.878586 0.445290 -0.446196 0.568521 1.019151 1.298329 0.420480 -0.372651 -0.807980 -2.044557 0.515663 0.625847 -1.300408 -0.138334 -0.295583 -0.571955 -0.050881 -0.304215 0.072001 -0.422234 0.086553 0.063499 231.71
21 17.0 0.962496 0.328461 -0.171479 2.109204 1.129566 1.696038 0.107712 0.521502 -1.191311 0.724396 1.690330 0.406774 -0.936421 0.983739 0.710911 -0.602232 0.402484 -1.737162 -2.027612 -0.269321 0.143997 0.402492 -0.048508 -1.371866 0.390814 0.199964 0.016371 -0.014605 34.09
22 18.0 1.166616 0.502120 -0.067300 2.261569 0.428804 0.089474 0.241147 0.138082 -0.989162 0.922175 0.744786 -0.531377 -2.105346 1.126870 0.003075 0.424425 -0.454475 -0.098871 -0.816597 -0.307169 0.018702 -0.061972 -0.103855 -0.370415 0.603200 0.108556 -0.040521 -0.011418 2.28
23 18.0 0.247491 0.277666 1.185471 -0.092603 -1.314394 -0.150116 -0.946365 -1.617935 1.544071 -0.829881 -0.583200 0.524933 -0.453375 0.081393 1.555204 -1.396895 0.783131 0.436621 2.177807 -0.230983 1.650180 0.200454 -0.185353 0.423073 0.820591 -0.227632 0.336634 0.250475 22.75
24 22.0 -1.946525 -0.044901 -0.405570 -1.013057 2.941968 2.955053 -0.063063 0.855546 0.049967 0.573743 -0.081257 -0.215745 0.044161 0.033898 1.190718 0.578843 -0.975667 0.044063 0.488603 -0.216715 -0.579526 -0.799229 0.870300 0.983421 0.321201 0.149650 0.707519 0.014600 0.89
25 22.0 -2.074295 -0.121482 1.322021 0.410008 0.295198 -0.959537 0.543985 -0.104627 0.475664 0.149451 -0.856566 -0.180523 -0.655233 -0.279797 -0.211668 -0.333321 0.010751 -0.488473 0.505751 -0.386694 -0.403639 -0.227404 0.742435 0.398535 0.249212 0.274404 0.359969 0.243232 26.43
26 23.0 1.173285 0.353498 0.283905 1.133563 -0.172577 -0.916054 0.369025 -0.327260 -0.246651 -0.046139 -0.143419 0.979350 1.492285 0.101418 0.761478 -0.014584 -0.511640 -0.325056 -0.390934 0.027878 0.067003 0.227812 -0.150487 0.435045 0.724825 -0.337082 0.016368 0.030041 41.88
27 23.0 1.322707 -0.174041 0.434555 0.576038 -0.836758 -0.831083 -0.264905 -0.220982 -1.071425 0.868559 -0.641506 -0.111316 0.361485 0.171945 0.782167 -1.355871 -0.216935 1.271765 -1.240622 -0.522951 -0.284376 -0.323357 -0.037710 0.347151 0.559639 -0.280158 0.042335 0.028822 16.00
28 23.0 -0.414289 0.905437 1.727453 1.473471 0.007443 -0.200331 0.740228 -0.029247 -0.593392 -0.346188 -0.012142 0.786796 0.635954 -0.086324 0.076804 -1.405919 0.775592 -0.942889 0.543969 0.097308 0.077237 0.457331 -0.038500 0.642522 -0.183891 -0.277464 0.182687 0.152665 33.00
29 23.0 1.059387 -0.175319 1.266130 1.186110 -0.786002 0.578435 -0.767084 0.401046 0.699500 -0.064738 1.048292 1.005618 -0.542002 -0.039915 -0.218683 0.004476 -0.193554 0.042388 -0.277834 -0.178023 0.013676 0.213734 0.014462 0.002951 0.294638 -0.395070 0.081461 0.024220 12.99
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
284777 172764.0 2.079137 -0.028723 -1.343392 0.358000 -0.045791 -1.345452 0.227476 -0.378355 0.665911 0.028351 -0.822911 0.049716 -0.352007 0.569087 0.791917 -0.178713 -0.500821 0.007475 -0.205470 -0.272447 0.235758 0.829758 -0.002063 0.001344 0.262183 -0.105327 -0.022363 -0.060283 1.00
284778 172764.0 -0.764523 0.588379 -0.907599 -0.418847 0.901528 -0.760802 0.758545 0.414698 -0.730854 -1.245088 0.874312 0.162691 -1.011819 -0.317789 -0.887823 0.482847 0.681412 0.486991 -0.022064 0.024870 0.003530 -0.431876 0.141759 0.587119 -0.200998 0.267337 -0.152951 -0.065285 80.00
284779 172766.0 1.975178 -0.616244 -2.628295 -0.406246 2.327804 3.664740 -0.533297 0.842937 1.128798 -0.220744 -0.590756 0.654596 -0.479779 0.120626 -0.907042 -1.186261 0.146953 -0.681104 0.295402 -0.168378 0.086043 0.543613 -0.032129 0.768379 0.477688 -0.031833 0.014151 -0.066542 25.00
284780 172766.0 -1.727503 1.108356 2.219561 1.148583 -0.884199 0.793083 -0.527298 0.866429 0.853819 -0.195152 -1.296770 0.725295 0.454177 -0.821823 -0.713585 -0.611659 0.457890 0.092557 0.757367 0.331940 -0.094708 0.236818 -0.204280 1.158185 0.627801 -0.399981 0.510818 0.233265 30.00
284781 172766.0 -1.139015 -0.155510 1.894478 -1.138957 1.451777 0.093598 0.191353 0.092211 -0.062621 -0.792066 0.659941 0.995402 0.861816 -0.300746 -0.188434 0.267385 -0.991270 0.290285 0.546517 0.341409 -0.191027 -0.631658 -0.147249 0.212931 0.354257 -0.241068 -0.161717 -0.149188 13.00
284782 172767.0 -0.268061 2.540315 -1.400915 4.846661 0.639105 0.186479 -0.045911 0.936448 -2.419986 0.525012 1.045386 0.124585 0.572344 -1.820716 -0.138285 1.100173 2.322135 1.349583 1.091523 0.111808 -0.263889 -0.857904 0.235172 -0.681794 -0.668894 0.044657 -0.066751 -0.072447 12.82
284783 172768.0 -1.796092 1.929178 -2.828417 -1.689844 2.199572 3.123732 -0.270714 1.657495 0.465804 0.832931 -0.344690 0.247153 -0.119068 0.849765 0.903389 -0.398816 -0.302906 0.036545 -0.202990 0.319366 0.271170 1.145750 0.084783 0.721269 -0.529906 -0.240117 0.129126 -0.080620 11.46
284784 172768.0 -0.669662 0.923769 -1.543167 -1.560729 2.833960 3.240843 0.181576 1.282746 -0.893890 -1.453432 0.187488 -0.390794 -0.289171 -0.510320 0.955637 0.553781 0.567862 0.409517 -0.671301 0.000965 0.183856 0.202670 -0.373023 0.651122 1.073823 0.844590 -0.286676 -0.187719 40.00
284785 172768.0 0.032887 0.545338 -1.185844 -1.729828 2.932315 3.401529 0.337434 0.925377 -0.165663 -0.386953 -0.199626 0.032017 -0.374731 0.354051 0.041228 -0.154750 -0.482455 -0.842462 -0.206385 0.022677 -0.266113 -0.716336 0.108519 0.688519 -0.460220 0.161939 0.265368 0.090245 1.79
284786 172768.0 -2.076175 2.142238 -2.522704 -1.888063 1.982785 3.732950 -1.217430 -0.536644 0.272867 0.300342 -0.451656 0.566368 -0.317804 0.855742 -0.041047 0.046620 0.017822 -0.772916 -0.354163 -0.308523 2.016666 -1.588269 0.588482 0.632444 -0.201064 0.199251 0.438657 0.172923 8.95
284787 172769.0 -1.029719 -1.110670 -0.636179 -0.840816 2.424360 -2.956733 0.283610 -0.332656 -0.247488 -0.328271 -1.089397 -0.694904 -1.315146 0.946281 -0.073008 -0.307347 -0.274712 -0.487247 -0.014378 0.218776 0.353722 0.488487 0.293632 0.107812 -0.935586 1.138216 0.025271 0.255347 9.99
284788 172770.0 2.007418 -0.280235 -0.208113 0.335261 -0.715798 -0.751373 -0.458972 -0.140140 0.959971 -0.028284 -0.635200 0.869261 0.996596 -0.280317 0.436079 0.397439 -0.557940 -0.536840 -0.082349 -0.143294 -0.208260 -0.430347 0.416765 0.064819 -0.608337 0.268436 -0.028069 -0.041367 3.99
284789 172770.0 -0.446951 1.302212 -0.168583 0.981577 0.578957 -0.605641 1.253430 -1.042610 -0.417116 0.076605 -1.291228 -0.690868 -1.481724 0.753473 -0.191141 -1.129904 0.319074 -0.201862 0.744416 -0.203306 0.851800 0.305268 -0.148093 -0.038712 0.010209 -0.362666 0.503092 0.229921 60.50
284790 172771.0 -0.515513 0.971950 -1.014580 -0.677037 0.912430 -0.316187 0.396137 0.532364 -0.224606 -0.753365 0.362990 0.110499 -0.853503 -0.383112 -0.774650 0.764540 0.216671 0.488508 0.180495 -0.177211 -0.280302 -0.849919 0.300245 0.000607 -0.376379 0.128660 -0.015205 -0.021486 9.81
284791 172774.0 -0.863506 0.874701 0.420358 -0.530365 0.356561 -1.046238 0.757051 0.230473 -0.506856 -1.032990 -1.187546 0.055871 -0.352509 0.550972 -0.551838 -0.229314 0.004179 -0.730361 0.077652 -0.162132 -0.108846 -0.480820 -0.074513 -0.003988 -0.113149 0.280378 -0.077310 0.023079 20.32
284792 172774.0 -0.724123 1.485216 -1.132218 -0.607190 0.709499 -0.482638 0.548393 0.343003 -0.226323 -0.348134 -1.381624 0.617933 1.428297 0.494490 0.426827 -0.229599 -0.494065 0.176169 0.182152 -0.077202 0.414621 1.307511 -0.059545 0.242669 -0.665424 -0.269869 -0.170579 -0.030692 3.99
284793 172775.0 1.971002 -0.699067 -1.697541 -0.617643 1.718797 3.911336 -1.259306 1.056209 1.315006 -0.146827 -0.222959 0.496509 -0.051785 -0.000744 0.893684 0.000363 -0.583710 0.074163 -0.356789 -0.153581 0.188758 0.694418 0.163002 0.726365 -0.058282 -0.191813 0.061858 -0.043716 4.99
284794 172777.0 -1.266580 -0.400461 0.956221 -0.723919 1.531993 -1.788600 0.314741 0.004704 0.013857 -0.815911 -1.311976 -0.946753 -2.119626 0.609469 -0.348707 0.077871 -0.357628 -0.578977 -0.518738 -0.029539 -0.157831 -0.883365 0.088485 -0.076790 -0.095833 0.132720 -0.028468 0.126494 0.89
284795 172778.0 -12.516732 10.187818 -8.476671 -2.510473 -4.586669 -1.394465 -3.632516 5.498583 4.893089 8.655320 -1.052365 2.834865 1.088141 1.288401 -0.931503 1.067864 0.586179 -0.085524 -0.571605 3.490065 -0.944759 -1.565026 0.890675 -1.253276 1.786717 0.320763 2.090712 1.232864 9.87
284796 172780.0 1.884849 -0.143540 -0.999943 1.506772 -0.035300 -0.613638 0.190241 -0.249058 0.666458 0.120908 -1.134176 0.677729 0.345928 0.002019 -0.679626 -0.549982 -0.199950 -0.420551 -0.283278 -0.153997 0.144008 0.634646 -0.042114 -0.053206 0.316403 -0.461441 0.018265 -0.041068 60.00
284797 172782.0 -0.241923 0.712247 0.399806 -0.463406 0.244531 -1.343668 0.929369 -0.206210 0.106234 -0.284708 -0.612982 -0.066655 -0.732987 0.237948 -0.293959 -0.245496 -0.174846 -0.891169 -0.290491 -0.139512 -0.228876 -0.514376 0.279598 0.371441 -0.559238 0.113144 0.131507 0.081265 5.49
284798 172782.0 0.219529 0.881246 -0.635891 0.960928 -0.152971 -1.014307 0.427126 0.121340 -0.285670 -0.111640 -1.109232 -0.453235 -1.046946 1.122674 1.243518 -1.431897 0.939328 -0.002373 2.894952 0.006666 0.099936 0.337120 0.251791 0.057688 -1.508368 0.144023 0.181205 0.215243 24.05
284799 172783.0 -1.775135 -0.004235 1.189786 0.331096 1.196063 5.519980 -1.518185 2.080825 1.159498 -0.594242 -1.264072 0.453596 -0.243142 -0.858719 -0.766554 -0.644646 0.447184 0.388721 0.792135 0.348176 0.103302 0.654850 -0.348929 0.745323 0.704545 -0.127579 0.454379 0.130308 79.99
284800 172784.0 2.039560 -0.175233 -1.196825 0.234580 -0.008713 -0.726571 0.017050 -0.118228 0.435402 0.267772 0.523316 0.559047 -0.834660 0.626211 -0.541494 0.225361 -0.605252 -0.163049 0.561695 -0.256922 -0.268048 -0.717211 0.297930 -0.359769 -0.315610 0.201114 -0.080826 -0.075071 2.68
284801 172785.0 0.120316 0.931005 -0.546012 -0.745097 1.130314 -0.235973 0.812722 0.115093 -0.204064 -0.657422 0.644837 0.190916 -0.546329 -0.731707 -0.808036 0.599628 0.070441 0.373110 0.128904 0.000676 -0.314205 -0.808520 0.050343 0.102800 -0.435870 0.124079 0.217940 0.068803 2.69
284802 172786.0 -11.881118 10.071785 -9.834783 -2.066656 -5.364473 -2.606837 -4.918215 7.305334 1.914428 4.356170 -1.593105 2.711941 -0.689256 4.626942 -0.924459 1.107641 1.991691 0.510632 -0.682920 1.475829 0.213454 0.111864 1.014480 -0.509348 1.436807 0.250034 0.943651 0.823731 0.77
284803 172787.0 -0.732789 -0.055080 2.035030 -0.738589 0.868229 1.058415 0.024330 0.294869 0.584800 -0.975926 -0.150189 0.915802 1.214756 -0.675143 1.164931 -0.711757 -0.025693 -1.221179 -1.545556 0.059616 0.214205 0.924384 0.012463 -1.016226 -0.606624 -0.395255 0.068472 -0.053527 24.79
284804 172788.0 1.919565 -0.301254 -3.249640 -0.557828 2.630515 3.031260 -0.296827 0.708417 0.432454 -0.484782 0.411614 0.063119 -0.183699 -0.510602 1.329284 0.140716 0.313502 0.395652 -0.577252 0.001396 0.232045 0.578229 -0.037501 0.640134 0.265745 -0.087371 0.004455 -0.026561 67.88
284805 172788.0 -0.240440 0.530483 0.702510 0.689799 -0.377961 0.623708 -0.686180 0.679145 0.392087 -0.399126 -1.933849 -0.962886 -1.042082 0.449624 1.962563 -0.608577 0.509928 1.113981 2.897849 0.127434 0.265245 0.800049 -0.163298 0.123205 -0.569159 0.546668 0.108821 0.104533 10.00
284806 172792.0 -0.533413 -0.189733 0.703337 -0.506271 -0.012546 -0.649617 1.577006 -0.414650 0.486180 -0.915427 -1.040458 -0.031513 -0.188093 -0.084316 0.041333 -0.302620 -0.660377 0.167430 -0.256117 0.382948 0.261057 0.643078 0.376777 0.008797 -0.473649 -0.818267 -0.002415 0.013649 217.00

284807 rows × 30 columns

X=np.array(data)
X.shape

(284807, 31)

from sklearn.cross_validation import train_test_split
X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.25,random_state=1) #0.25分区
print(X_train.shape,X_test.shape,y_train.shape,y_test.shape)

(213605, 31) (71202, 31) (213605,) (71202,)

五、SMOTE-Regular调节样本失衡

如果数据存在严重的不平衡，预测得出的结论往往也是有偏的，即分类结果会偏向于较多观测的类。对于这种问题该如何处理呢？
最简单粗暴的办法就是构造1:1的数据，要么将多的那一类砍掉一部分（即欠采样），要么将少的那一类进行Bootstrap抽样（即过采样）。但这样做会存在问题，对于第一种方法，砍掉的数据会导致某些隐含信息的丢失;而第二种方法中，有放回的抽样形成的简单复制，又会使模型产生过拟合。
　　SMOTE算法的基本思想就是对少数类别样本进行分析和模拟，并将人工模拟的新样本添加到数据集中，进而使原始数据中的类别不再严重失衡。该算法的模拟过程采用了KNN技术，模拟生成新样本的步骤如下：
1、采样最邻近算法，计算出每个少数类样本的K个近邻;
2、根据样本不平衡比例设置一个采样比例以确定采样倍率N，对于每一个少数类样本a，从其k近邻中随机选择若干个样本，假设选择的近邻为b。
3、3、对于每一个随机选出的近邻b，分别与原样本a按照如下的公式构建新的样本:c=a+rand(0,1)∗|a−b|
SMOTE缺陷：
该算法主要存在两方面的问题:
一是在近邻选择时,存在一定的盲目性。从上面的算法流程可以看出,在算法执行过程中,需要确定K值,即选择多少个近邻样本,这需要用户自行解决。从K值的定义可以看出,K值的下限是M值(M值为从K个近邻中随机挑选出的近邻样本的个数,且有M< K),M的大小可以根据负类样本数量、正类样本数量和数据集最后需要达到的平衡率决定。但K值的上限没有办法确定,只能根据具体的数据集去反复测试。因此如何确定K值,才能使算法达到最优这是未知的。
二是,该算法无法克服非平衡数据集的数据分布问题,容易产生分布边缘化问题。由于负类样本的分布决定了其可选择的近邻,如果一个负类样本处在负类样本集的分布边缘,则由此负类样本和相邻样本产生的“人造”样本也会处在这个边缘,且会越来越边缘化,从而模糊了正类样本和负类样本的边界,而且使边界变得越来越模糊。这种边界模糊性,虽然使数据集的平衡性得到了改善,但加大了分类算法进行分类的难度。

https://www.jianshu.com/p/9a68934d1f56
https://zhuanlan.zhihu.com/p/44055312
https://www.cnblogs.com/wqbin/p/11117616.html

from imblearn.over_sampling import SMOTE #conda install -c glemaitre imbalanced-learn
sm=SMOTE(random_state=2)
X_sm,y_sm=sm.fit_sample(X_train,y_train)

print(X_sm.shape,y_sm.shape)
print(len(y_sm[y_sm==1]),len(y_sm[y_sm==0]))
print(len(y_train[y_train==1]),len(y_train[y_train==0]))
print(len(X_test))

(426448, 31) (426448,)
213224 213224
381 213224
71202

六、模型训练 和 评价

1、Logistic Regression (采用class weight)

from sklearn.linear_model import LogisticRegression
import matplotlib.pyplot as plt
from sklearn import metrics

class_weight：用于标示分类模型中各种类型的权重，可以是一个字典或者’balanced’字符串，默认为不输入，也就是不考虑权重，即为None。如果选择输入的话，可以选择balanced让类库自己计算类型权重，或者自己输入各个类型的权重。举个例子，比如对于0,1的二元模型，我们可以定义class_weight={0:0.9,1:0.1}，这样类型0的权重为90%，而类型1的权重为10%。如果class_weight选择balanced，那么类库会根据训练样本量来计算权重。某种类型样本量越多，则权重越低，样本量越少，则权重越高。当class_weight为balanced时，类权重计算方法如下：n_samples / (n_classes * np.bincount(y))。n_samples为样本数，n_classes为类别数量，np.bincount(y)会输出每个类的样本数，例如y=[1,0,0,1,1],则np.bincount(y)=[2,3]。
那么class_weight有什么作用呢？
在分类模型中，我们经常会遇到两类问题：
第一种是误分类的代价很高。比如对合法用户和非法用户进行分类，将非法用户分类为合法用户的代价很高，我们宁愿将合法用户分类为非法用户，这时可以人工再甄别，但是却不愿将非法用户分类为合法用户。这时，我们可以适当提高非法用户的权重。
第二种是样本是高度失衡的，比如我们有合法用户和非法用户的二元样本数据10000条，里面合法用户有9995条，非法用户只有5条，如果我们不考虑权重，则我们可以将所有的测试集都预测为合法用户，这样预测准确率理论上有99.95%，但是却没有任何意义。这时，我们可以选择balanced，让类库自动提高非法用户样本的权重。提高了某种分类的权重，相比不考虑权重，会有更多的样本分类划分到高权重的类别，从而可以解决上面两类问题。

https://blog.csdn.net/kingzone_2008/article/details/81067036

logreg=LogisticRegression(class_weight='balanced')
logreg.fit(X_train,y_train)
y_logreg=logreg.predict(X_test)
y_logreg_prob=logreg.predict_proba(X_test)[:,1]
y_logreg_prob

array([0.00138142, 0.00026409, 0.00039966, ..., 0.00110574, 0.0001552 ,0.00018182])

print("Confusion Matrix:\n",metrics.confusion_matrix(y_test,y_logreg))
print("Accuracy:\n",metrics.accuracy_score(y_test,y_logreg))
print("Precision:\n",metrics.precision_score(y_test,y_logreg))
print("Recall:\n",metrics.recall_score(y_test,y_logreg))
print("AUC:\n",metrics.roc_auc_score(y_test,y_logreg_prob))
auc=metrics.roc_auc_score(y_test,y_logreg_prob)

Confusion Matrix:[[71090     1][    0   111]]
Accuracy:0.9999859554506896
Precision:0.9910714285714286
Recall:1.0
AUC:1.0

2、Logistic Regression (采用SMOTE-Regular)

logreg=LogisticRegression()
logreg.fit(X_sm,y_sm)
y_logreg=logreg.predict(X_test)
y_logreg_prob=logreg.predict_proba(X_test)[:,1]
y_logreg_prob

array([0.02475453, 0.02826518, 0.04893115, ..., 0.10006813, 0.00403899,0.00970458])

print("Confusion Matrix:\n",metrics.confusion_matrix(y_test,y_logreg))
print("Accuracy:\n",metrics.accuracy_score(y_test,y_logreg))
print("Precision:\n",metrics.precision_score(y_test,y_logreg))
print("Recall:\n",metrics.recall_score(y_test,y_logreg))
print("AUC:\n",metrics.roc_auc_score(y_test,y_logreg_prob))
auc=metrics.roc_auc_score(y_test,y_logreg_prob)

Confusion Matrix:[[70167   924][   18    93]]
Accuracy:0.9867700345495913
Precision:0.09144542772861357
Recall:0.8378378378378378
AUC:0.9505760983163186

fpr,tpr,thresholds=metrics.roc_curve(y_test,y_logreg_prob)
#返回值：fpr假阳率 tpr真阳率

plt.plot(fpr,tpr,'b', label='AUC = %0.2f'% auc)
plt.plot([0,1],[0,1],'r-.')
plt.xlim([-0.2,1.2])
plt.ylim([-0.2,1.2])
plt.title('Receiver Operating Characteristic\nLogistic Regression')
plt.legend(loc='lower right')
plt.ylabel('True Positive Rate')
plt.xlabel('False Positive Rate')
plt.show()

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