Kaggle-Credit Card Fraud

    技术2022-07-14  75

    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 TimeV1V2V3V4V5V6V7V8V9...V21V22V23V24V25V26V27V28AmountClass00.0-1.359807-0.0727812.5363471.378155-0.3383210.4623880.2395990.0986980.363787...-0.0183070.277838-0.1104740.0669280.128539-0.1891150.133558-0.021053149.62010.01.1918570.2661510.1664800.4481540.060018-0.082361-0.0788030.085102-0.255425...-0.225775-0.6386720.101288-0.3398460.1671700.125895-0.0089830.0147242.69021.0-1.358354-1.3401631.7732090.379780-0.5031981.8004990.7914610.247676-1.514654...0.2479980.7716790.909412-0.689281-0.327642-0.139097-0.055353-0.059752378.66031.0-0.966272-0.1852261.792993-0.863291-0.0103091.2472030.2376090.377436-1.387024...-0.1083000.005274-0.190321-1.1755750.647376-0.2219290.0627230.061458123.50042.0-1.1582330.8777371.5487180.403034-0.4071930.0959210.592941-0.2705330.817739...-0.0094310.798278-0.1374580.141267-0.2060100.5022920.2194220.21515369.99052.0-0.4259660.9605231.141109-0.1682520.420987-0.0297280.4762010.260314-0.568671...-0.208254-0.559825-0.026398-0.371427-0.2327940.1059150.2538440.0810803.67064.01.2296580.1410040.0453711.2026130.1918810.272708-0.0051590.0812130.464960...-0.167716-0.270710-0.154104-0.7800550.750137-0.2572370.0345070.0051684.99077.0-0.6442691.4179641.074380-0.4921990.9489340.4281181.120631-3.8078640.615375...1.943465-1.0154550.057504-0.649709-0.415267-0.051634-1.206921-1.08533940.80087.0-0.8942860.286157-0.113192-0.2715262.6695993.7218180.3701450.851084-0.392048...-0.073425-0.268092-0.2042331.0115920.373205-0.3841570.0117470.14240493.20099.0-0.3382621.1195931.044367-0.2221870.499361-0.2467610.6515830.069539-0.736727...-0.246914-0.633753-0.120794-0.385050-0.0697330.0941990.2462190.0830763.6801010.01.449044-1.1763390.913860-1.375667-1.971383-0.629152-1.4232360.048456-1.720408...-0.0093020.3138940.0277400.5005120.251367-0.1294780.0428500.0162537.8001110.00.3849780.616109-0.874300-0.0940192.9245843.3170270.4704550.538247-0.558895...0.0499240.2384220.0091300.996710-0.767315-0.4922080.042472-0.0543379.9901210.01.249999-1.2216370.383930-1.234899-1.485419-0.753230-0.689405-0.227487-2.094011...-0.231809-0.4832850.0846680.3928310.161135-0.3549900.0264160.042422121.5001311.01.0693740.2877220.8286132.712520-0.1783980.337544-0.0967170.115982-0.221083...-0.0368760.074412-0.0714070.1047440.5482650.1040940.0214910.02129327.5001412.0-2.791855-0.3277711.6417501.767473-0.1365880.807596-0.422911-1.9071070.755713...1.1516630.2221821.0205860.028317-0.232746-0.235557-0.164778-0.03015458.8001512.0-0.7524170.3454852.057323-1.468643-1.158394-0.077850-0.6085810.003603-0.436167...0.4996251.353650-0.256573-0.065084-0.039124-0.087086-0.1809980.12939415.9901612.01.103215-0.0402961.2673321.289091-0.7359970.288069-0.5860570.1893800.782333...-0.0246120.1960020.0138020.1037580.364298-0.3822610.0928090.03705112.9901713.0-0.4369050.9189660.924591-0.7272190.915679-0.1278670.7076420.087962-0.665271...-0.194796-0.672638-0.156858-0.888386-0.342413-0.0490270.0796920.1310240.8901814.0-5.401258-5.4501481.1863051.7362393.049106-1.763406-1.5597380.1608421.233090...-0.5036000.9844602.4585890.042119-0.481631-0.6212720.3920530.94959446.8001915.01.492936-1.0293460.454795-1.438026-1.555434-0.720961-1.080664-0.053127-1.978682...-0.177650-0.1750740.0400020.2958140.332931-0.2203850.0222980.0076025.0002016.00.694885-1.3618191.0292210.834159-1.1912091.309109-0.8785860.445290-0.446196...-0.295583-0.571955-0.050881-0.3042150.072001-0.4222340.0865530.063499231.7102117.00.9624960.328461-0.1714792.1092041.1295661.6960380.1077120.521502-1.191311...0.1439970.402492-0.048508-1.3718660.3908140.1999640.016371-0.01460534.0902218.01.1666160.502120-0.0673002.2615690.4288040.0894740.2411470.138082-0.989162...0.018702-0.061972-0.103855-0.3704150.6032000.108556-0.040521-0.0114182.2802318.00.2474910.2776661.185471-0.092603-1.314394-0.150116-0.946365-1.6179351.544071...1.6501800.200454-0.1853530.4230730.820591-0.2276320.3366340.25047522.7502422.0-1.946525-0.044901-0.405570-1.0130572.9419682.955053-0.0630630.8555460.049967...-0.579526-0.7992290.8703000.9834210.3212010.1496500.7075190.0146000.8902522.0-2.074295-0.1214821.3220210.4100080.295198-0.9595370.543985-0.1046270.475664...-0.403639-0.2274040.7424350.3985350.2492120.2744040.3599690.24323226.4302623.01.1732850.3534980.2839051.133563-0.172577-0.9160540.369025-0.327260-0.246651...0.0670030.227812-0.1504870.4350450.724825-0.3370820.0163680.03004141.8802723.01.322707-0.1740410.4345550.576038-0.836758-0.831083-0.264905-0.220982-1.071425...-0.284376-0.323357-0.0377100.3471510.559639-0.2801580.0423350.02882216.0002823.0-0.4142890.9054371.7274531.4734710.007443-0.2003310.740228-0.029247-0.593392...0.0772370.457331-0.0385000.642522-0.183891-0.2774640.1826870.15266533.0002923.01.059387-0.1753191.2661301.186110-0.7860020.578435-0.7670840.4010460.699500...0.0136760.2137340.0144620.0029510.294638-0.3950700.0814610.02422012.990..................................................................284777172764.02.079137-0.028723-1.3433920.358000-0.045791-1.3454520.227476-0.3783550.665911...0.2357580.829758-0.0020630.0013440.262183-0.105327-0.022363-0.0602831.000284778172764.0-0.7645230.588379-0.907599-0.4188470.901528-0.7608020.7585450.414698-0.730854...0.003530-0.4318760.1417590.587119-0.2009980.267337-0.152951-0.06528580.000284779172766.01.975178-0.616244-2.628295-0.4062462.3278043.664740-0.5332970.8429371.128798...0.0860430.543613-0.0321290.7683790.477688-0.0318330.014151-0.06654225.000284780172766.0-1.7275031.1083562.2195611.148583-0.8841990.793083-0.5272980.8664290.853819...-0.0947080.236818-0.2042801.1581850.627801-0.3999810.5108180.23326530.000284781172766.0-1.139015-0.1555101.894478-1.1389571.4517770.0935980.1913530.092211-0.062621...-0.191027-0.631658-0.1472490.2129310.354257-0.241068-0.161717-0.14918813.000284782172767.0-0.2680612.540315-1.4009154.8466610.6391050.186479-0.0459110.936448-2.419986...-0.263889-0.8579040.235172-0.681794-0.6688940.044657-0.066751-0.07244712.820284783172768.0-1.7960921.929178-2.828417-1.6898442.1995723.123732-0.2707141.6574950.465804...0.2711701.1457500.0847830.721269-0.529906-0.2401170.129126-0.08062011.460284784172768.0-0.6696620.923769-1.543167-1.5607292.8339603.2408430.1815761.282746-0.893890...0.1838560.202670-0.3730230.6511221.0738230.844590-0.286676-0.18771940.000284785172768.00.0328870.545338-1.185844-1.7298282.9323153.4015290.3374340.925377-0.165663...-0.266113-0.7163360.1085190.688519-0.4602200.1619390.2653680.0902451.790284786172768.0-2.0761752.142238-2.522704-1.8880631.9827853.732950-1.217430-0.5366440.272867...2.016666-1.5882690.5884820.632444-0.2010640.1992510.4386570.1729238.950284787172769.0-1.029719-1.110670-0.636179-0.8408162.424360-2.9567330.283610-0.332656-0.247488...0.3537220.4884870.2936320.107812-0.9355861.1382160.0252710.2553479.990284788172770.02.007418-0.280235-0.2081130.335261-0.715798-0.751373-0.458972-0.1401400.959971...-0.208260-0.4303470.4167650.064819-0.6083370.268436-0.028069-0.0413673.990284789172770.0-0.4469511.302212-0.1685830.9815770.578957-0.6056411.253430-1.042610-0.417116...0.8518000.305268-0.148093-0.0387120.010209-0.3626660.5030920.22992160.500284790172771.0-0.5155130.971950-1.014580-0.6770370.912430-0.3161870.3961370.532364-0.224606...-0.280302-0.8499190.3002450.000607-0.3763790.128660-0.015205-0.0214869.810284791172774.0-0.8635060.8747010.420358-0.5303650.356561-1.0462380.7570510.230473-0.506856...-0.108846-0.480820-0.074513-0.003988-0.1131490.280378-0.0773100.02307920.320284792172774.0-0.7241231.485216-1.132218-0.6071900.709499-0.4826380.5483930.343003-0.226323...0.4146211.307511-0.0595450.242669-0.665424-0.269869-0.170579-0.0306923.990284793172775.01.971002-0.699067-1.697541-0.6176431.7187973.911336-1.2593061.0562091.315006...0.1887580.6944180.1630020.726365-0.058282-0.1918130.061858-0.0437164.990284794172777.0-1.266580-0.4004610.956221-0.7239191.531993-1.7886000.3147410.0047040.013857...-0.157831-0.8833650.088485-0.076790-0.0958330.132720-0.0284680.1264940.890284795172778.0-12.51673210.187818-8.476671-2.510473-4.586669-1.394465-3.6325165.4985834.893089...-0.944759-1.5650260.890675-1.2532761.7867170.3207632.0907121.2328649.870284796172780.01.884849-0.143540-0.9999431.506772-0.035300-0.6136380.190241-0.2490580.666458...0.1440080.634646-0.042114-0.0532060.316403-0.4614410.018265-0.04106860.000284797172782.0-0.2419230.7122470.399806-0.4634060.244531-1.3436680.929369-0.2062100.106234...-0.228876-0.5143760.2795980.371441-0.5592380.1131440.1315070.0812655.490284798172782.00.2195290.881246-0.6358910.960928-0.152971-1.0143070.4271260.121340-0.285670...0.0999360.3371200.2517910.057688-1.5083680.1440230.1812050.21524324.050284799172783.0-1.775135-0.0042351.1897860.3310961.1960635.519980-1.5181852.0808251.159498...0.1033020.654850-0.3489290.7453230.704545-0.1275790.4543790.13030879.990284800172784.02.039560-0.175233-1.1968250.234580-0.008713-0.7265710.017050-0.1182280.435402...-0.268048-0.7172110.297930-0.359769-0.3156100.201114-0.080826-0.0750712.680284801172785.00.1203160.931005-0.546012-0.7450971.130314-0.2359730.8127220.115093-0.204064...-0.314205-0.8085200.0503430.102800-0.4358700.1240790.2179400.0688032.690284802172786.0-11.88111810.071785-9.834783-2.066656-5.364473-2.606837-4.9182157.3053341.914428...0.2134540.1118641.014480-0.5093481.4368070.2500340.9436510.8237310.770284803172787.0-0.732789-0.0550802.035030-0.7385890.8682291.0584150.0243300.2948690.584800...0.2142050.9243840.012463-1.016226-0.606624-0.3952550.068472-0.05352724.790284804172788.01.919565-0.301254-3.249640-0.5578282.6305153.031260-0.2968270.7084170.432454...0.2320450.578229-0.0375010.6401340.265745-0.0873710.004455-0.02656167.880284805172788.0-0.2404400.5304830.7025100.689799-0.3779610.623708-0.6861800.6791450.392087...0.2652450.800049-0.1632980.123205-0.5691590.5466680.1088210.10453310.000284806172792.0-0.533413-0.1897330.703337-0.506271-0.012546-0.6496171.577006-0.4146500.486180...0.2610570.6430780.3767770.008797-0.473649-0.818267-0.0024150.013649217.000

    284807 rows × 31 columns

    data.head() #发现无法展示完全所有列 TimeV1V2V3V4V5V6V7V8V9...V21V22V23V24V25V26V27V28AmountClass00.0-1.359807-0.0727812.5363471.378155-0.3383210.4623880.2395990.0986980.363787...-0.0183070.277838-0.1104740.0669280.128539-0.1891150.133558-0.021053149.62010.01.1918570.2661510.1664800.4481540.060018-0.082361-0.0788030.085102-0.255425...-0.225775-0.6386720.101288-0.3398460.1671700.125895-0.0089830.0147242.69021.0-1.358354-1.3401631.7732090.379780-0.5031981.8004990.7914610.247676-1.514654...0.2479980.7716790.909412-0.689281-0.327642-0.139097-0.055353-0.059752378.66031.0-0.966272-0.1852261.792993-0.863291-0.0103091.2472030.2376090.377436-1.387024...-0.1083000.005274-0.190321-1.1755750.647376-0.2219290.0627230.061458123.50042.0-1.1582330.8777371.5487180.403034-0.4071930.0959210.592941-0.2705330.817739...-0.0094310.798278-0.1374580.141267-0.2060100.5022920.2194220.21515369.990

    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 TimeV1V2V3V4V5V6V7V8V9V10V11V12V13V14V15V16V17V18V19V20V21V22V23V24V25V26V27V28AmountClass00.0-1.359807-0.0727812.5363471.378155-0.3383210.4623880.2395990.0986980.3637870.090794-0.551600-0.617801-0.991390-0.3111691.468177-0.4704010.2079710.0257910.4039930.251412-0.0183070.277838-0.1104740.0669280.128539-0.1891150.133558-0.021053149.62010.01.1918570.2661510.1664800.4481540.060018-0.082361-0.0788030.085102-0.255425-0.1669741.6127271.0652350.489095-0.1437720.6355580.463917-0.114805-0.183361-0.145783-0.069083-0.225775-0.6386720.101288-0.3398460.1671700.125895-0.0089830.0147242.69021.0-1.358354-1.3401631.7732090.379780-0.5031981.8004990.7914610.247676-1.5146540.2076430.6245010.0660840.717293-0.1659462.345865-2.8900831.109969-0.121359-2.2618570.5249800.2479980.7716790.909412-0.689281-0.327642-0.139097-0.055353-0.059752378.66031.0-0.966272-0.1852261.792993-0.863291-0.0103091.2472030.2376090.377436-1.387024-0.054952-0.2264870.1782280.507757-0.287924-0.631418-1.059647-0.6840931.965775-1.232622-0.208038-0.1083000.005274-0.190321-1.1755750.647376-0.2219290.0627230.061458123.50042.0-1.1582330.8777371.5487180.403034-0.4071930.0959210.592941-0.2705330.8177390.753074-0.8228430.5381961.345852-1.1196700.175121-0.451449-0.237033-0.0381950.8034870.408542-0.0094310.798278-0.1374580.141267-0.2060100.5022920.2194220.21515369.990 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() TimeV1V2V3V4V5V6V7V8V9V10V11V12V13V14V15V16V17V18V19V20V21V22V23V24V25V26V27V28AmountClasscount284807.0000002.848070e+052.848070e+052.848070e+052.848070e+052.848070e+052.848070e+052.848070e+052.848070e+052.848070e+052.848070e+052.848070e+052.848070e+052.848070e+052.848070e+052.848070e+052.848070e+052.848070e+052.848070e+052.848070e+052.848070e+052.848070e+052.848070e+052.848070e+052.848070e+052.848070e+052.848070e+052.848070e+052.848070e+05284807.000000284807.000000mean94813.8595753.919560e-155.688174e-16-8.769071e-152.782312e-15-1.552563e-152.010663e-15-1.694249e-15-1.927028e-16-3.137024e-151.768627e-159.170318e-16-1.810658e-151.693438e-151.479045e-153.482336e-151.392007e-15-7.528491e-164.328772e-169.049732e-165.085503e-161.537294e-167.959909e-165.367590e-164.458112e-151.453003e-151.699104e-15-3.660161e-16-1.206049e-1688.3496190.001727std47488.1459551.958696e+001.651309e+001.516255e+001.415869e+001.380247e+001.332271e+001.237094e+001.194353e+001.098632e+001.088850e+001.020713e+009.992014e-019.952742e-019.585956e-019.153160e-018.762529e-018.493371e-018.381762e-018.140405e-017.709250e-017.345240e-017.257016e-016.244603e-016.056471e-015.212781e-014.822270e-014.036325e-013.300833e-01250.1201090.041527min0.000000-5.640751e+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+010.0000000.00000025T201.500000-9.203734e-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-025.6000000.00000050
    转载请注明原文地址:https://ipadbbs.8miu.com/read-25654.html
    最新回复(0)