Credit Card Fraud Detection using OCR & Autoencoders in Keras.

In today’s world the credit card frauds are very vulnerable to each one of us. In a day all around the world millions of transactions get carried out. It is very much possible that the person doesn’t carry out the transaction still the credit is done from his/her account.

There are many ways to detect whether the transaction is a fraud or not.

In our project we detect the transaction on basis of behavior of the transaction.

The detection of fraud transactions can be used by banking companies and other Money transaction applications to inform their customers if a fraud transaction occurs and strict measures can be then taken.

It can be also considered as a safety measure taken to avoid fraud transaction.

If a fraud transaction occurs the company can immediately inform its customers and verify with them.

We are basically going to use 2 different models to optimize our output thereby maintaining our accuracy high.

Image for post

**Architecture **of our System.

OUTLINE

Module 1:

OCR- a font, a font created specifically to aid Optical Character Recognition algorithms. We’ll then devise a computer vision and image processing algorithm that can localize the four groupings of four digits on a credit card, Extract each of these four groupings followed by segmenting each of the sixteen numbers individually, Recognize each of the sixteen credit card digits by using template matching and the OCR- a font.

Image for post

So this is how Module 1 works:

1. Takes a reference image and extracts the digits.

2. Stores the digit templates in a dictionary.

3. Localizes the four credit card number groups, each holding four digits (for a total of 16 digits).

4. Extracts the digits to be “matched”.

5. Performs template matching on each digit, comparing each individual ROI to each of the digit templates 0–9, whilst storing a score for each attempted match.

6. Finds the highest score for each candidate digit, and builds a list called output which contains the credit card number.

7. Outputs the credit card number and credit card type to our terminal and displays the output image to our screen with database details .

8. Comparing with Luhn algorithm it tells whether it is valid or invalid card.

Image for post

Retrieval of Customer details from the database by OCR technique.

#fraud-detection #credit-cards #data-mining #data analysis

What is GEEK

Buddha Community

Credit Card Fraud Detection using OCR & Autoencoders in Keras.

Chloe Butler

1667425440

Pdf2gerb: Perl Script Converts PDF Files to Gerber format

pdf2gerb

Perl script converts PDF files to Gerber format

Pdf2Gerb generates Gerber 274X photoplotting and Excellon drill files from PDFs of a PCB. Up to three PDFs are used: the top copper layer, the bottom copper layer (for 2-sided PCBs), and an optional silk screen layer. The PDFs can be created directly from any PDF drawing software, or a PDF print driver can be used to capture the Print output if the drawing software does not directly support output to PDF.

The general workflow is as follows:

Design the PCB using your favorite CAD or drawing software.
Print the top and bottom copper and top silk screen layers to a PDF file.
Run Pdf2Gerb on the PDFs to create Gerber and Excellon files.
Use a Gerber viewer to double-check the output against the original PCB design.
Make adjustments as needed.
Submit the files to a PCB manufacturer.

Please note that Pdf2Gerb does NOT perform DRC (Design Rule Checks), as these will vary according to individual PCB manufacturer conventions and capabilities. Also note that Pdf2Gerb is not perfect, so the output files must always be checked before submitting them. As of version 1.6, Pdf2Gerb supports most PCB elements, such as round and square pads, round holes, traces, SMD pads, ground planes, no-fill areas, and panelization. However, because it interprets the graphical output of a Print function, there are limitations in what it can recognize (or there may be bugs).

See docs/Pdf2Gerb.pdf for install/setup, config, usage, and other info.

pdf2gerb_cfg.pm

#Pdf2Gerb config settings:
#Put this file in same folder/directory as pdf2gerb.pl itself (global settings),
#or copy to another folder/directory with PDFs if you want PCB-specific settings.
#There is only one user of this file, so we don't need a custom package or namespace.
#NOTE: all constants defined in here will be added to main namespace.
#package pdf2gerb_cfg;

use strict; #trap undef vars (easier debug)
use warnings; #other useful info (easier debug)


##############################################################################################
#configurable settings:
#change values here instead of in main pfg2gerb.pl file

use constant WANT_COLORS => ($^O !~ m/Win/); #ANSI colors no worky on Windows? this must be set < first DebugPrint() call

#just a little warning; set realistic expectations:
#DebugPrint("${\(CYAN)}Pdf2Gerb.pl ${\(VERSION)}, $^O O/S\n${\(YELLOW)}${\(BOLD)}${\(ITALIC)}This is EXPERIMENTAL software.  \nGerber files MAY CONTAIN ERRORS.  Please CHECK them before fabrication!${\(RESET)}", 0); #if WANT_DEBUG

use constant METRIC => FALSE; #set to TRUE for metric units (only affect final numbers in output files, not internal arithmetic)
use constant APERTURE_LIMIT => 0; #34; #max #apertures to use; generate warnings if too many apertures are used (0 to not check)
use constant DRILL_FMT => '2.4'; #'2.3'; #'2.4' is the default for PCB fab; change to '2.3' for CNC

use constant WANT_DEBUG => 0; #10; #level of debug wanted; higher == more, lower == less, 0 == none
use constant GERBER_DEBUG => 0; #level of debug to include in Gerber file; DON'T USE FOR FABRICATION
use constant WANT_STREAMS => FALSE; #TRUE; #save decompressed streams to files (for debug)
use constant WANT_ALLINPUT => FALSE; #TRUE; #save entire input stream (for debug ONLY)

#DebugPrint(sprintf("${\(CYAN)}DEBUG: stdout %d, gerber %d, want streams? %d, all input? %d, O/S: $^O, Perl: $]${\(RESET)}\n", WANT_DEBUG, GERBER_DEBUG, WANT_STREAMS, WANT_ALLINPUT), 1);
#DebugPrint(sprintf("max int = %d, min int = %d\n", MAXINT, MININT), 1); 

#define standard trace and pad sizes to reduce scaling or PDF rendering errors:
#This avoids weird aperture settings and replaces them with more standardized values.
#(I'm not sure how photoplotters handle strange sizes).
#Fewer choices here gives more accurate mapping in the final Gerber files.
#units are in inches
use constant TOOL_SIZES => #add more as desired
(
#round or square pads (> 0) and drills (< 0):
    .010, -.001,  #tiny pads for SMD; dummy drill size (too small for practical use, but needed so StandardTool will use this entry)
    .031, -.014,  #used for vias
    .041, -.020,  #smallest non-filled plated hole
    .051, -.025,
    .056, -.029,  #useful for IC pins
    .070, -.033,
    .075, -.040,  #heavier leads
#    .090, -.043,  #NOTE: 600 dpi is not high enough resolution to reliably distinguish between .043" and .046", so choose 1 of the 2 here
    .100, -.046,
    .115, -.052,
    .130, -.061,
    .140, -.067,
    .150, -.079,
    .175, -.088,
    .190, -.093,
    .200, -.100,
    .220, -.110,
    .160, -.125,  #useful for mounting holes
#some additional pad sizes without holes (repeat a previous hole size if you just want the pad size):
    .090, -.040,  #want a .090 pad option, but use dummy hole size
    .065, -.040, #.065 x .065 rect pad
    .035, -.040, #.035 x .065 rect pad
#traces:
    .001,  #too thin for real traces; use only for board outlines
    .006,  #minimum real trace width; mainly used for text
    .008,  #mainly used for mid-sized text, not traces
    .010,  #minimum recommended trace width for low-current signals
    .012,
    .015,  #moderate low-voltage current
    .020,  #heavier trace for power, ground (even if a lighter one is adequate)
    .025,
    .030,  #heavy-current traces; be careful with these ones!
    .040,
    .050,
    .060,
    .080,
    .100,
    .120,
);
#Areas larger than the values below will be filled with parallel lines:
#This cuts down on the number of aperture sizes used.
#Set to 0 to always use an aperture or drill, regardless of size.
use constant { MAX_APERTURE => max((TOOL_SIZES)) + .004, MAX_DRILL => -min((TOOL_SIZES)) + .004 }; #max aperture and drill sizes (plus a little tolerance)
#DebugPrint(sprintf("using %d standard tool sizes: %s, max aper %.3f, max drill %.3f\n", scalar((TOOL_SIZES)), join(", ", (TOOL_SIZES)), MAX_APERTURE, MAX_DRILL), 1);

#NOTE: Compare the PDF to the original CAD file to check the accuracy of the PDF rendering and parsing!
#for example, the CAD software I used generated the following circles for holes:
#CAD hole size:   parsed PDF diameter:      error:
#  .014                .016                +.002
#  .020                .02267              +.00267
#  .025                .026                +.001
#  .029                .03167              +.00267
#  .033                .036                +.003
#  .040                .04267              +.00267
#This was usually ~ .002" - .003" too big compared to the hole as displayed in the CAD software.
#To compensate for PDF rendering errors (either during CAD Print function or PDF parsing logic), adjust the values below as needed.
#units are pixels; for example, a value of 2.4 at 600 dpi = .0004 inch, 2 at 600 dpi = .0033"
use constant
{
    HOLE_ADJUST => -0.004 * 600, #-2.6, #holes seemed to be slightly oversized (by .002" - .004"), so shrink them a little
    RNDPAD_ADJUST => -0.003 * 600, #-2, #-2.4, #round pads seemed to be slightly oversized, so shrink them a little
    SQRPAD_ADJUST => +0.001 * 600, #+.5, #square pads are sometimes too small by .00067, so bump them up a little
    RECTPAD_ADJUST => 0, #(pixels) rectangular pads seem to be okay? (not tested much)
    TRACE_ADJUST => 0, #(pixels) traces seemed to be okay?
    REDUCE_TOLERANCE => .001, #(inches) allow this much variation when reducing circles and rects
};

#Also, my CAD's Print function or the PDF print driver I used was a little off for circles, so define some additional adjustment values here:
#Values are added to X/Y coordinates; units are pixels; for example, a value of 1 at 600 dpi would be ~= .002 inch
use constant
{
    CIRCLE_ADJUST_MINX => 0,
    CIRCLE_ADJUST_MINY => -0.001 * 600, #-1, #circles were a little too high, so nudge them a little lower
    CIRCLE_ADJUST_MAXX => +0.001 * 600, #+1, #circles were a little too far to the left, so nudge them a little to the right
    CIRCLE_ADJUST_MAXY => 0,
    SUBST_CIRCLE_CLIPRECT => FALSE, #generate circle and substitute for clip rects (to compensate for the way some CAD software draws circles)
    WANT_CLIPRECT => TRUE, #FALSE, #AI doesn't need clip rect at all? should be on normally?
    RECT_COMPLETION => FALSE, #TRUE, #fill in 4th side of rect when 3 sides found
};

#allow .012 clearance around pads for solder mask:
#This value effectively adjusts pad sizes in the TOOL_SIZES list above (only for solder mask layers).
use constant SOLDER_MARGIN => +.012; #units are inches

#line join/cap styles:
use constant
{
    CAP_NONE => 0, #butt (none); line is exact length
    CAP_ROUND => 1, #round cap/join; line overhangs by a semi-circle at either end
    CAP_SQUARE => 2, #square cap/join; line overhangs by a half square on either end
    CAP_OVERRIDE => FALSE, #cap style overrides drawing logic
};
    
#number of elements in each shape type:
use constant
{
    RECT_SHAPELEN => 6, #x0, y0, x1, y1, count, "rect" (start, end corners)
    LINE_SHAPELEN => 6, #x0, y0, x1, y1, count, "line" (line seg)
    CURVE_SHAPELEN => 10, #xstart, ystart, x0, y0, x1, y1, xend, yend, count, "curve" (bezier 2 points)
    CIRCLE_SHAPELEN => 5, #x, y, 5, count, "circle" (center + radius)
};
#const my %SHAPELEN =
#Readonly my %SHAPELEN =>
our %SHAPELEN =
(
    rect => RECT_SHAPELEN,
    line => LINE_SHAPELEN,
    curve => CURVE_SHAPELEN,
    circle => CIRCLE_SHAPELEN,
);

#panelization:
#This will repeat the entire body the number of times indicated along the X or Y axes (files grow accordingly).
#Display elements that overhang PCB boundary can be squashed or left as-is (typically text or other silk screen markings).
#Set "overhangs" TRUE to allow overhangs, FALSE to truncate them.
#xpad and ypad allow margins to be added around outer edge of panelized PCB.
use constant PANELIZE => {'x' => 1, 'y' => 1, 'xpad' => 0, 'ypad' => 0, 'overhangs' => TRUE}; #number of times to repeat in X and Y directions

# Set this to 1 if you need TurboCAD support.
#$turboCAD = FALSE; #is this still needed as an option?

#CIRCAD pad generation uses an appropriate aperture, then moves it (stroke) "a little" - we use this to find pads and distinguish them from PCB holes. 
use constant PAD_STROKE => 0.3; #0.0005 * 600; #units are pixels
#convert very short traces to pads or holes:
use constant TRACE_MINLEN => .001; #units are inches
#use constant ALWAYS_XY => TRUE; #FALSE; #force XY even if X or Y doesn't change; NOTE: needs to be TRUE for all pads to show in FlatCAM and ViewPlot
use constant REMOVE_POLARITY => FALSE; #TRUE; #set to remove subtractive (negative) polarity; NOTE: must be FALSE for ground planes

#PDF uses "points", each point = 1/72 inch
#combined with a PDF scale factor of .12, this gives 600 dpi resolution (1/72 * .12 = 600 dpi)
use constant INCHES_PER_POINT => 1/72; #0.0138888889; #multiply point-size by this to get inches

# The precision used when computing a bezier curve. Higher numbers are more precise but slower (and generate larger files).
#$bezierPrecision = 100;
use constant BEZIER_PRECISION => 36; #100; #use const; reduced for faster rendering (mainly used for silk screen and thermal pads)

# Ground planes and silk screen or larger copper rectangles or circles are filled line-by-line using this resolution.
use constant FILL_WIDTH => .01; #fill at most 0.01 inch at a time

# The max number of characters to read into memory
use constant MAX_BYTES => 10 * M; #bumped up to 10 MB, use const

use constant DUP_DRILL1 => TRUE; #FALSE; #kludge: ViewPlot doesn't load drill files that are too small so duplicate first tool

my $runtime = time(); #Time::HiRes::gettimeofday(); #measure my execution time

print STDERR "Loaded config settings from '${\(__FILE__)}'.\n";
1; #last value must be truthful to indicate successful load


#############################################################################################
#junk/experiment:

#use Package::Constants;
#use Exporter qw(import); #https://perldoc.perl.org/Exporter.html

#my $caller = "pdf2gerb::";

#sub cfg
#{
#    my $proto = shift;
#    my $class = ref($proto) || $proto;
#    my $settings =
#    {
#        $WANT_DEBUG => 990, #10; #level of debug wanted; higher == more, lower == less, 0 == none
#    };
#    bless($settings, $class);
#    return $settings;
#}

#use constant HELLO => "hi there2"; #"main::HELLO" => "hi there";
#use constant GOODBYE => 14; #"main::GOODBYE" => 12;

#print STDERR "read cfg file\n";

#our @EXPORT_OK = Package::Constants->list(__PACKAGE__); #https://www.perlmonks.org/?node_id=1072691; NOTE: "_OK" skips short/common names

#print STDERR scalar(@EXPORT_OK) . " consts exported:\n";
#foreach(@EXPORT_OK) { print STDERR "$_\n"; }
#my $val = main::thing("xyz");
#print STDERR "caller gave me $val\n";
#foreach my $arg (@ARGV) { print STDERR "arg $arg\n"; }

Download Details:

Author: swannman
Source Code: https://github.com/swannman/pdf2gerb

License: GPL-3.0 license

#perl

TopDevelopers Co

1620294465

Credit card fraud and technical solutions

Credit card fraud is an increasingly expensive problem. Technology offers solutions to help combat the problem and gain control.
How to prevent fraudulent transactions in credit cards is a common question plaguing the credit card user today. The credit card brings convenience and security to the users, but the same can become a cause of agony if the user is a victim of any credit card fraud. Smart systems are coming to the aid of credit card users and empowering them against cybercriminals. Using fraud detection tools and following some simple precautions, the users can protect themselves against credit card fraud.

#credit card fraud detection #types of credit card fraud #contactless payment systems #technological challenges in credit card fraud #prevent fraudulent transactions in credit cards

Sedrick Carroll

1594804560

Credit Card Fraud Detection using OCR & Autoencoders in Keras.

There are many ways to detect whether the transaction is a fraud or not.

In our project we detect the transaction on basis of behavior of the transaction.

It can be also considered as a safety measure taken to avoid fraud transaction.

If a fraud transaction occurs the company can immediately inform its customers and verify with them.

We are basically going to use 2 different models to optimize our output thereby maintaining our accuracy high.

Image for post

**Architecture **of our System.

OUTLINE

Module 1:

Image for post

So this is how Module 1 works:

1. Takes a reference image and extracts the digits.

2. Stores the digit templates in a dictionary.

3. Localizes the four credit card number groups, each holding four digits (for a total of 16 digits).

4. Extracts the digits to be “matched”.

5. Performs template matching on each digit, comparing each individual ROI to each of the digit templates 0–9, whilst storing a score for each attempted match.

6. Finds the highest score for each candidate digit, and builds a list called output which contains the credit card number.

7. Outputs the credit card number and credit card type to our terminal and displays the output image to our screen with database details .

8. Comparing with Luhn algorithm it tells whether it is valid or invalid card.

Image for post

Retrieval of Customer details from the database by OCR technique.

#fraud-detection #credit-cards #data-mining #data analysis

Barney Baumbach

1669894200

How to Making A Simple Credit Card Validation form

In this quick tutorial we will show you how to create a simple credit card form. We'll build the whole thing from scratch, with a little help from Bootstrap 3 for the interface, and Payform.js for client-side form validation.

Project Overview

Here is a sneak-peak of what we will be building in this tutorial:

Credit Card Form Demo

You can get the full code for this project from the Download button near the top of the article. An overview of the files can be seen below:

There are two .css files and two .js files which we will need to include in our HTML. All other resources such as the Bootstrap framework, jQuery, and web fonts will be included externally via CDN.

index.html

<!DOCTYPE html>
<html>
<head>
    <meta charset="utf-8">
    <meta name="viewport" content="width=device-width, initial-scale=1">
    <title>Credit Card Validation Demo</title>

    <link href="https://fonts.googleapis.com/css?family=Open+Sans" rel="stylesheet">
    <link rel="stylesheet" href="http://maxcdn.bootstrapcdn.com/bootstrap/3.3.7/css/bootstrap.min.css">
    <link rel="stylesheet" type="text/css" href="assets/css/styles.css">
    <link rel="stylesheet" type="text/css" href="assets/css/demo.css">
</head>

<body>

    <!-- The HTML for our form will go here -->

    <script src="https://ajax.googleapis.com/ajax/libs/jquery/1.12.4/jquery.min.js"></script>
    <script src="assets/js/jquery.payform.min.js" charset="utf-8"></script>
    <script src="assets/js/script.js"></script>
</body>
</html>

Now that everything is set up, we can start building our credit card form. Let's start with the HTML layout!

Layout

A credit card dialog needs to be simple, short, and straightforward. Here are the four input fields that every credit card form needs to have:

Credit card owner name
Card number
Secret code (also known as CVV/CVC/CID)
Expiration Date

All we need to do is create a <form> and add all the required input fields. For the owner, card number, and CVV we will use simple text fields. For the expiration date we'll put a combination of two selects with predefined options.

Besides that our form will have a heading, a submit button, and images for popular credit card vendors. Since we are working with Bootstrap there is a little extra markup, but it helps keep the code organized and the layout responsive.

<div class="creditCardForm">
    <div class="heading">
        <h1>Confirm Purchase</h1>
    </div>
    <div class="payment">
        <form>
            <div class="form-group owner">
                <label for="owner">Owner</label>
                <input type="text" class="form-control" id="owner">
            </div>
            <div class="form-group CVV">
                <label for="cvv">CVV</label>
                <input type="text" class="form-control" id="cvv">
            </div>
            <div class="form-group" id="card-number-field">
                <label for="cardNumber">Card Number</label>
                <input type="text" class="form-control" id="cardNumber">
            </div>
            <div class="form-group" id="expiration-date">
                <label>Expiration Date</label>
                <select>
                    <option value="01">January</option>
                    <option value="02">February </option>
                    <option value="03">March</option>
                    <option value="04">April</option>
                    <option value="05">May</option>
                    <option value="06">June</option>
                    <option value="07">July</option>
                    <option value="08">August</option>
                    <option value="09">September</option>
                    <option value="10">October</option>
                    <option value="11">November</option>
                    <option value="12">December</option>
                </select>
                <select>
                    <option value="16"> 2016</option>
                    <option value="17"> 2017</option>
                    <option value="18"> 2018</option>
                    <option value="19"> 2019</option>
                    <option value="20"> 2020</option>
                    <option value="21"> 2021</option>
                </select>
            </div>
            <div class="form-group" id="credit_cards">
                <img src="assets/images/visa.jpg" id="visa">
                <img src="assets/images/mastercard.jpg" id="mastercard">
                <img src="assets/images/amex.jpg" id="amex">
            </div>
            <div class="form-group" id="pay-now">
                <button type="submit" class="btn btn-default" id="confirm-purchase">Confirm</button>
            </div>
        </form>
    </div>
</div>

Now that we have the needed input fields, we can setup the validation rules.

Validation

All of the validation we will show here is client side and done exclusively in the JavaScript. If it is HTML validation that you are interested in, check out this article.

To kick things off we will define all the jQuery selectors we will need:

var owner = $('#owner'),
    cardNumber = $('#cardNumber'),
    cardNumberField = $('#card-number-field'),
    CVV = $("#cvv"),
    mastercard = $("#mastercard"),
    confirmButton = $('#confirm-purchase'),
    visa = $("#visa"),
    amex = $("#amex");

Then, using Payform.js, we will turn our basic input fields into specialized input for credit card data. We simply need to call the right function and the library will automatically handle text formatting and maximum string length for us:

cardNumber.payform('formatCardNumber'); CVV.payform('formatCardCVC');

Next, we want to be able to give real-time feedback to users while they are typing in their card number. To do so we will write a simple function that does two things:

Check if the current text in the field is а valid card number or not. Add appropriate coloring to the text field.
Depending on the present input characters, see if the card is either Visa, MasterCard, or American Express. This is done using the payform.parseCardType() method.

Since we want to execute the above actions every time a new character is typed in, we will use the jQuery keyup() event listener.

cardNumber.keyup(function() {
    amex.removeClass('transparent');
    visa.removeClass('transparent');
    mastercard.removeClass('transparent');

    if ($.payform.validateCardNumber(cardNumber.val()) == false) {
        cardNumberField.removeClass('has-success');
        cardNumberField.addClass('has-error');
    } else {
        cardNumberField.removeClass('has-error');
        cardNumberField.addClass('has-success');
    }

    if ($.payform.parseCardType(cardNumber.val()) == 'visa') {
        mastercard.addClass('transparent');
        amex.addClass('transparent');
    } else if ($.payform.parseCardType(cardNumber.val()) == 'amex') {
        mastercard.addClass('transparent');
        visa.addClass('transparent');
    } else if ($.payform.parseCardType(cardNumber.val()) == 'mastercard') {
        amex.addClass('transparent');
        visa.addClass('transparent');
    }
});

There is one more thing we have to do and that is is check if all the field are holding valid data when the user tries to submit the form.

Name validation can be quite tricky. To keep this tutorial light, we won't be going into that subject, and we will only check if the input name is at least 5 characters long. Payform provides us with the needed methods for validating the rest of the form.

confirmButton.click(function(e) {
    e.preventDefault();

    var isCardValid = $.payform.validateCardNumber(cardNumber.val());
    var isCvvValid = $.payform.validateCardCVC(CVV.val());

    if(owner.val().length < 5){
        alert("Wrong owner name");
    } else if (!isCardValid) {
        alert("Wrong card number");
    } else if (!isCvvValid) {
        alert("Wrong CVV");
    } else {
        // Everything is correct. Add your form submission code here.
        alert("Everything is correct");
    }
});

The above validation is for educational purposes only and shouldn't be used on commercial projects. Always include both client-side and server-side validation to your forms, especially when working with credit card data.

Styles

We are using Bootstrap, so most of the styling is done by the framework. Our CSS mostly covers the size of the input fields and various padding, margin and font tweaks.

styles.css

.creditCardForm {
    max-width: 700px;
    background-color: #fff;
    margin: 100px auto;
    overflow: hidden;
    padding: 25px;
    color: #4c4e56;
}
.creditCardForm label {
    width: 100%;
    margin-bottom: 10px;
}
.creditCardForm .heading h1 {
    text-align: center;
    font-family: 'Open Sans', sans-serif;
    color: #4c4e56;
}
.creditCardForm .payment {
    float: left;
    font-size: 18px;
    padding: 10px 25px;
    margin-top: 20px;
    position: relative;
}
.creditCardForm .payment .form-group {
    float: left;
    margin-bottom: 15px;
}
.creditCardForm .payment .form-control {
    line-height: 40px;
    height: auto;
    padding: 0 16px;
}
.creditCardForm .owner {
    width: 63%;
    margin-right: 10px;
}
.creditCardForm .CVV {
    width: 35%;
}
.creditCardForm #card-number-field {
    width: 100%;
}
.creditCardForm #expiration-date {
    width: 49%;
}
.creditCardForm #credit_cards {
    width: 50%;
    margin-top: 25px;
    text-align: right;
}
.creditCardForm #pay-now {
    width: 100%;
    margin-top: 25px;
}
.creditCardForm .payment .btn {
    width: 100%;
    margin-top: 3px;
    font-size: 24px;
    background-color: #2ec4a5;
    color: white;
}
.creditCardForm .payment select {
    padding: 10px;
    margin-right: 15px;
}
.transparent {
    opacity: 0.2;
}
@media(max-width: 650px) {
    .creditCardForm .owner,
    .creditCardForm .CVV,
    .creditCardForm #expiration-date,
    .creditCardForm #credit_cards {
        width: 100%;
    }
    .creditCardForm #credit_cards {
        text-align: left;
    }
}

With this our Credit Card Validation Form is complete!

Original article source at: https://tutorialzine.com/

#validation #card

Queenie Davis

1623360840

Credit Card Fraud Detection with Python & Machine Learning

For any bank or financial organization, credit card fraud detection is of utmost importance. We have to spot potential fraud so that consumers can not bill for goods that they haven’t purchased. The aim is, therefore, to create a classifier that indicates whether a requested transaction is a fraud.

About Credit Card Fraud Detection

In this machine learning project, we solve the problem of detecting credit card fraud transactions using machine numpy, scikit learn, and few other python libraries. We overcome the problem by creating a binary classifier and experimenting with various machine learning techniques to see which fits better.

Credit Card Fraud Dataset

The dataset consists of 31 parameters. Due to confidentiality issues, 28 of the features are the result of the PCA transformation. “Time’ and “Amount” are the only aspects that were not modified with PCA.

There are a total of 284,807 transactions with only 492 of them being fraud. So, the label distribution suffers from imbalance issues.

Please download the dataset for credit card fraud detection project: Anonymized Credit Card Transactions for Fraud Detection

Tools and Libraries used

We use the following libraries and frameworks in credit card fraud detection project.

Python – 3.x
Numpy – 1.19.2
Scikit-learn – 0.24.1
Matplotlib – 3.3.4
Imblearn – 0.8.0
Collections, Itertools

Credit Card Fraud Project Code

Please download the source code of the credit card fraud detection project (which is explained below): Credit Card Fraud Detection Machine Learning Code

Steps to Develop Credit Card Fraud Classifier in Machine Learning

Our approach to building the classifier is discussed in the steps:

Perform Exploratory Data Analysis (EDA) on our dataset
Apply different Machine Learning algorithms to our dataset
Train and Evaluate our models on the dataset and pick the best one.

Step 1. Perform Exploratory Data Analysis (EDA)

There are a total of 284,807 transactions with only 492 of them being fraud. Let’s import the necessary modules, load our dataset, and perform EDA on our dataset. Here is a peek at our dataset:

import pandas as pdfrom collections import Counterimport itertools ## Load the csv file dataframe = pd.read_csv ( “./Desktop/DataFlair/credit_card_fraud_detection/creditcard.csv” ) dataframe.head ()

#machine learning tutorials #credit card fraud classification #credit card fraud project