For instance, you can replace: if () < card:
You can simplify the way you return a boolean result from your methods.
revise the quality and necessity of comments: there is probably not much sense in having a comment # Constructor. consistent naming: rename postcode to post_code. It is a little bit cleaner to do this way since now our class is more generic, it is agnostic of where the attributes are coming from. I would also pass the obtained attributes to the class constructor instead of asking for them inside it: def _init_(self, name, post_code, card_date, card_code): name attribute, consists of credit-card related logic only. I think there is this code organization issue - you have a class named Customer, but it, aside from the. """Function used to test if a number is even.""" If self.check_code() and self.check_date(): # If the code is divisible by 10, returns True, else, it returns False. # For each digit, if it is greater than 9 9 is subtracted from it. If it is valid, returns True else False."""Ĭard = (self.card_date, "%d/%m/%Y").date() """Checks current date against the credit card's date. Self.card_code = input("Card code: ").strip() """Class representing the customer and their credit card details""" The code: """Program used to check if a credit card is authentic.""" The card date must also be in the future.įinally, output their name, postcode, card number, and whether it is If the sum of the 7 digits, and the check digit are divisable by 10, If the result of the multiplication is > 9, subtract 9 from it. The 1st, 3rd, 5th, and 7th digits are multiplied by 2 The eighth digit of the card code is removed and acts as a check digit The user enters their name, postcode, the card code, and the card I am wondering if you could review my code, both appraising the actual code, but also evaluate my use of OOP/classes. Having no experience with classes in the past, I decided to employ classes in this project. Share your experience and queries through comments.Just completed a piece of code - a credit card validation program in python - as a little side project. What are you waiting for? Start learning the machine learning concepts for FREE with the help of DataFlair’s Machine Learning Tutorial Series. Machine Learning and R are the important technologies of this decade and will last forever. So, now you are ready to detect the fraud. We learnt how data can be analyzed and visualized to discern fraudulent transactions from other types of data. We used a variety of ML algorithms to implement this model and also plotted the respective performance curves for the models. Gbm_auc = roc(test_data$Class, gbm_test, plot = TRUE, col = "red")Ĭoncluding our R Data Science project, we learnt how to develop our credit card fraud detection model using machine learning. Gbm_test = predict(model_gbm, newdata = test_data, n.trees = gbm.iter) 
= TRUE)Ĭode: # Plot and calculate AUC on test data Gbm.iter = gbm.perf(model_gbm, method = "test")Ĭode: model.influence = relative.influence(model_gbm, n.trees = gbm.iter, sort. # Determine best iteration based on test data , action = nrow(train_data) / (nrow(train_data) + nrow(test_data)) We will implement gradient descent algorithm in our model as follows – These decision trees combine together to form a strong model of gradient boosting.
This model comprises of several underlying ensemble models like weak decision trees. Gradient Boosting is a popular machine learning algorithm that is used to perform classification and regression tasks. We are importing the datasets that contain transactions made by credit cards. Stay updated with latest technology trends The dataset used in this project is available here – Fraud Detection Dataset By the end of this machine learning project, you will learn how to implement machine learning algorithms to perform classification. We will use a variety of machine learning algorithms that will be able to discern fraudulent from non-fraudulent one. The aim of this R project is to build a classifier that can detect credit card fraudulent transactions. Machine Learning Project – How to Detect Credit Card Fraud For carrying out the credit card fraud detection, we will make use of the Card Transactions dataset that contains a mix of fraud as well as non-fraudulent transactions. We will go through the various algorithms like Decision Trees, Logistic Regression, Artificial Neural Networks and finally, Gradient Boosting Classifier. In this R Project, we will learn how to perform detection of credit cards. Earlier we talked about Uber Data Analysis Project and today we will discuss the Credit Card Fraud Detection Project using Machine Learning and R concepts. This is the 3rd part of the R project series designed by DataFlair. We offer you a brighter future with FREE online courses Start Now!!
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