Credit Card Fraud

In this example, we use VerticaPy to detect fraudulent credit card transactions. You can download the Jupyter notebook here.

The Credit Card Fraud Detection dataset contains credit card transactions from September 2013 by European cardholders. It contains numerical input variables from a principal component analysis (PCA) transformation.

To preserve the cardholders' confidentiality, we cannot access the original features and background information about the data.

"Time" and "Amount" are the only features that have not been transformed with PCA.

  • V1, V2,..., V28 : principal components from PCA
  • Time : Number of seconds elapsed between this transaction and the first transaction in the dataset
  • Amount : Transaction amount
  • Class : Response variable, where a value of 1 indicates fraudulent activity

"Amount" will be useful for example-dependent cost-sensitive learning.

We will follow the entire Data Science cycle (Data Exploration, Data Preparation, Data Modeling, Model Evaluation, Model Deployment) to solve this problem.

Initialization

This example uses the following version of VerticaPy:

In [1]:
import verticapy as vp
vp.__version__
Out[1]:
'0.9.0'

Connect to Vertica. This example uses an existing connection called "VerticaDSN." For details on how to create a connection, use see the connection tutorial.

In [2]:
vp.connect("VerticaDSN")

Let's create a Virtual DataFrame of the dataset.

In [3]:
creditcard = vp.read_csv('data/creditcard.csv', 
                         parse_nrows = 1000)
display(creditcard)
123
Time
Int
123
V1
Float
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V2
Float
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V3
Float
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V4
Float
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V5
Float
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V6
Float
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V7
Float
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V8
Float
123
V9
Float
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V10
Float
123
V11
Float
123
V12
Float
123
V13
Float
123
V14
Float
123
V15
Float
123
V16
Float
123
V17
Float
123
V18
Float
123
V19
Float
123
V20
Float
123
V21
Float
123
V22
Float
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V23
Float
123
V24
Float
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V25
Float
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V26
Float
123
V27
Float
123
V28
Float
123
Amount
Numeric(9,3)
123
Class
Int
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93601.107029376948430.2164410003713510.5383781497082331.47639799266774-0.251941920499494-0.3406797069124960.154091744631261-0.08650254563322860.144420201120718-0.157374627687008-0.1075602976940881.06632565737920.620788366839222-0.0708893376688794-0.203857982828647-0.8534794193307680.336478013774869-1.17953172110404-0.501729107425755-0.111852535538935-0.06003745010764930.0598841227860989-0.04752636647783270.4439924186361190.640295758747984-0.3376571306685210.04210848389191540.02426471000685826.720
9462-1.035105817766890.9960131278175142.19247013559677-0.170445302779918-0.302319738525738-0.1422557677002870.2234317744485960.2890434101184210.0941408906153459-0.157858732844487-0.386373549409945-0.628633662180404-1.023958211220710.09843387469763141.562288761964390.168577701728536-0.148826411284152-0.285927210472601-0.4833638941512240.180287626805096-0.098000008392506-0.17024584412346-0.137227527043150.05522827893583530.07934755420630780.3020326021134110.3913773499747970.1965998806652895.990
9564-0.6583049340956840.406790567371532.03746063876455-0.2912979744171950.147909808593845-0.3508572919580210.945373213135361-0.1725600167339780.0251333984849018-0.778134723733662-0.1962898019372620.6329552618263730.374025171567011-0.531597207426503-0.615062565100624-0.5180784591851660.00388089531948063-1.2889540744158-0.7978044852172220.0641328738661188-0.156095760618683-0.2388049648914450.08987717739851130.421195367129561-0.3524871342279070.0747827424634443-0.0941921697507859-0.092493130400881354.990
96640.959601658512230.3707113046139330.8886133700455652.343244103022470.3524908410847551.36551460619135-0.2777707304627410.516053179747465-0.7009290450550260.4682162393672580.7456115099471320.373531002993358-0.2239182858666670.4174381420698961.67840014345469-0.5157016002371870.662478668271888-2.5142351135941-2.5896875748614-0.298404520795831-0.155547160729115-0.4032393156847710.356504034074842-0.696881458915448-0.198717970162506-0.2202679155151420.06854619613585830.02079731541614097.550
97641.21251089095254-0.0990542956039435-1.192094094634260.2863235615097552.160516251832093.61631350966622-0.404206898066650.8423309516479930.169360420742526-0.0305221765721243-0.5419572580231060.237212014866616-0.2181296487032740.197436520971338-0.147850245921019-0.0978301417256899-0.51005250120389-0.07093928064214850.3551705836531590.0147774396345371-0.167496336516951-0.494694569848075-0.1497854509684641.011226554671010.883547704730387-0.3294338105920750.0203701456794310.017037086350501234.70
9867-1.494667840742710.8372413999636292.628210617906073.14541437889465-0.6090983888544240.258495000688512-0.01218861335878050.102136035246365-0.2861635968242871.19855632464938-0.550295668711412-0.1068459885207450.208014288229433-0.6805104027696350.507763583158711-0.2602640225587780.2466313239022870.008855504384242280.899415801359582-0.0283519350532205-0.1400473580571390.3550441838116020.3327199884352280.718192517148152-0.2193660461143240.118926551385282-0.317485702168288-0.34078339765064328.280
9967-0.6534446272949950.1602250768520381.592255555240821.296832283575650.997174923623236-0.3430003024091340.469936930779834-0.132470081994361-0.197794077439788-0.105005538081425-0.5436622109269710.3022330226359640.141211078726449-0.2078804435531010.0241083998795536-1.206876591915640.39483638189259-0.7509556302064260.4405584811704080.2259203469185410.03836296797903680.336449012374641-0.01488283283567180.102959010243-0.265322060067868-0.348636686626220.0112384491290326-0.049478339972149219.850
10068-0.7701963826479880.4835724164120241.90107177893088-0.001876250116188520.0242447851356973-1.171903181582750.666483360897814-0.0946029012075535-0.136665621886762-0.842507532652561-0.4082350970881010.503046644959921-0.010588597193309-0.200012764882671-0.908689458907517-0.323594882408827-0.0221036727295719-0.796683932841244-0.479505428522999-0.0338308901120332-0.0573359108181297-0.1558012807637610.01810780200743670.941555368544963-0.1685818411260970.05617099692751760.05897936129031080.13418792127799622.360
Rows: 1-100 | Columns: 31

Data Exploration

Let's explore the data by displaying descriptive statistics of all the columns.

In [4]:
creditcard.describe()

Out[4]:
count
mean
std
min
approx_25%
approx_50%
approx_75%
max
"Time"28480694813.841365702747488.22832975130.053836.351912568384348.356547619139145.62236534172792.0
"V1"2848064.94593944284826e-061.9586974640224-56.407509631329-0.922687561939835-0.004023521563535611.315890444824242.45492999121121
"V2"2848065.70457762978566e-061.65130867214891-72.7157275629303-0.5957557556492920.06488872801405330.80634597197939322.0577289904909
"V3"284806-8.9455359328619e-061.51625015150494-48.3255893623954-0.8897478621394210.1797950030480911.027902405428869.38255843282114
"V4"284806-1.35415518936858e-061.4158708761878-5.68317119816995-0.850673053289735-0.02276909609958510.74216249847936916.8753440335975
"V5"284806-1.76537738534109e-061.38024883562916-113.743306711146-0.692237575696747-0.0541469873155910.61268391143762134.8016658766686
"V6"284806-1.78084282248157e-061.33227308968888-26.1605059358433-0.767764777497726-0.2720730111341110.39740972899175973.3016255459646
"V7"2848066.12498031875639e-061.23709145154523-43.5572415712451-0.5538828475189340.03981456264679790.570420541346974120.589493945238
"V8"284806-2.67056953047529e-061.19435414910998-73.2167184552674-0.2087661827582030.0220061628565510.32847087188162820.0072083651213
"V9"284806-1.05394689850686e-051.09861961972555-13.4340663182301-0.641658431375348-0.05166901173298820.5959896806161515.5949946071278
"V10"2848064.08381472133854e-061.08884949581936-24.5882624372475-0.535699872562504-0.09292037159809670.45328255250960223.7451361206545
"V11"284806-1.38169915208807e-061.02071455331551-4.79747346479757-0.763765051282868-0.03382187485375190.73984174842771112.0189131816199
"V12"2848066.93843535334324e-060.999196282625864-18.6837146333443-0.4043222887970750.1419286303375090.6187311497957377.8483920756446
"V13"284806-8.12075363651004e-070.995275883057862-5.79188120632084-0.649459842407078-0.01334910490294320.6614371689126657.12688295859376
"V14"284806-1.84962341608303e-060.95859678593258-19.2143254902614-0.4267411848911070.05116431665091320.49276491045535310.5267660517847
"V15"2848061.13497169209891e-050.91529757725878-4.49894467676621-0.5830265047062520.04647603158499510.6497512300068888.87774159774277
"V16"284806-7.66515009784341e-080.876254424770488-14.1298545174931-0.4675522387000860.06626739121742440.52342780734466717.3151115176278
"V17"284806-1.82546167118454e-060.849337996047683-25.1627993693248-0.483346901949957-0.06605825142364860.3994048505087349.25352625047285
"V18"284806-3.95506512618923e-060.838175023391719-9.49874592104677-0.499033588630568-0.003262777455159320.5005742148118095.04106918541184
"V19"284806-2.80927640719469e-070.81404191608048-7.21352743017759-0.4548850313037110.003652402667366090.4586940743659245.59197142733558
"V20"284806-7.33947797666482e-070.770926278808211-54.497720494566-0.211671047672314-0.06249770509640540.13334617592955339.4209042482199
"V21"284806-7.64172187575889e-070.734525190677675-34.8303821448146-0.2286085901976-0.02938689104486870.18606448848130427.2028391573154
"V22"284806-2.66232723466359e-060.725701443601768-10.933143697655-0.5421584039904380.006747921053238860.52840312158958410.5030900899454
"V23"284806-9.87528822847108e-070.624461169497999-44.8077352037913-0.161700875369553-0.0111335096927520.14775835082307922.5284116897749
"V24"284806-2.58634991744588e-060.605646558279992-2.83662691870341-0.3534686481854890.04096385948910910.4388432916332714.58454913689817
"V25"2848062.60318278485781e-060.521277134452016-10.2953970749851-0.3173985280137780.01697338816639960.3498730114756877.51958867870916
"V26"284806-8.96574869783747e-070.482227622472413-2.60455055280817-0.327442353708538-0.05385973923824510.241233936262753.5173456116238
"V27"284806-4.98388964024732e-070.403633115942523-22.5656793207827-0.07102288983282510.001439592508895260.091310343991825931.6121981061363
"V28"284806-8.01132016653443e-070.330083566760313-15.4300839055349-0.0529499817329360.01126499591763050.078347660770601833.8478078188831
"Amount"28480688.3497556582376250.1205377537440.05.6372294372294422.001091370558477.028498293515425691.16
"Class"2848060.001727491696101910.04152726241384280.00.00.00.01.0
Rows: 1-31 | Columns: 9

It'll be difficult to work on the principal components (V1 through V28) without knowing what they mean. The only features we can work on are Time and Amount.

Let's convert the number of seconds elapsed to the correct date and time. We know that the records were ingested in September 2013, so we'll use that to create the new feature.

In [5]:
creditcard["Time"].apply("TIMESTAMPADD(second, {}::int, '2013-09-01 00:00:00'::timestamp)")
Out[5]: