Loy Credit

June 20, 2019
Is this credit card transaction normal? Fraud detection in credit cards based on Two-Class Decision Forest.
# Problem This problem is centered around predicting if credit card transaction (with its related info/variables) is a fraud or no. The input dataset of the transactions contain only numerical input variables which are the result of previous PCA (Principal Component Analysis) transformations. Unfortunately, due to confidentiality issues, the original features and additional background information are not available, but the way you build the model doesn't change. <br><br> ![enter image description here][1] <br><br> Features V1, V2, ... V28 are the principal components obtained with PCA, the only features which have not been transformed with PCA are 'Time' and 'Amount'. The feature 'Time' contains the seconds elapsed between each transaction and the first transaction in the dataset. The feature 'Amount' is the transaction Amount, this feature can be used for example-dependant cost-sensitive learning. Feature 'Class' is the response variable and it takes value 1 in case of fraud and 0 otherwise. The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions. Using those datasets you build a model that when predicting it will analyze a transaction's input variables and predict a fraud value of false or true. <br><br> ![enter image description here][2] <br><br> # DataSet The training and testing data is based on a public dataset available at Kaggle originally from Worldline and the Machine Learning Group (http://mlg.ulb.ac.be) of ULB (Université Libre de Bruxelles), collected and analysed during a research collaboration. <br><br> The datasets contains transactions made by credit cards in September 2013 by european cardholders. This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. <br><br> By: Andrea Dal Pozzolo, Olivier Caelen, Reid A. Johnson and Gianluca Bontempi. Calibrating Probability with Undersampling for Unbalanced Classification. In Symposium on Computational Intelligence and Data Mining (CIDM), IEEE, 2015 <br><br> More details on current and past projects on related topics are available on http://mlg.ulb.ac.be/BruFence and http://mlg.ulb.ac.be/ARTML <br><br> # Binary Classification Binary or binomial classification is the task of classifying the elements of a given set into two groups (predicting which group each one belongs to) on the basis of a classification rule. Contexts requiring a decision as to whether or not an item has some qualitative property, some specified characteristic <br><br> # Two-Class Decision Forest module Decision forests are fast, supervised ensemble models. This module is a good choice if you want to predict a target with a maximum of two outcomes. If you are not sure how to configure a decision tree model for the best results, we recommend that you use the Tune Model Hyperparameters module to train and test multiple models. tuning iterates over multiple possibilities and finds the optimal solution for you. <br><br> This decision forest algorithm is an ensemble learning method intended for classification tasks. Ensemble methods are based on the general principle that rather than relying on a single model, you can get better results and a more generalized model by creating multiple related models and combining them in some way. Generally, ensemble models provide better coverage and accuracy than single decision trees. There are many ways to create individual models and combine them in an ensemble. This particular implementation of a decision forest works by building multiple decision trees and then voting on the most popular output class. Voting is one of the better-known methods for generating results in an ensemble model. <br><br> ![enter image description here][3] <br><br> ![enter image description here][4] <br><br> ![enter image description here][5] <br><br> [1]: https://raw.githubusercontent.com/laploy/ML.NET/master/Credit-Card/creditCard--dataset-preview.jpg [2]: https://raw.githubusercontent.com/laploy/ML.NET/master/Credit-Card/creditCard-azure-data-visual.jpg [3]: https://raw.githubusercontent.com/laploy/ML.NET/master/Credit-Card/creditCard-azure-model.jpg [4]: https://raw.githubusercontent.com/laploy/ML.NET/master/Credit-Card/creditCard-azure-score.jpg [5]: https://raw.githubusercontent.com/laploy/ML.NET/master/Credit-Card/creditCard-azure-eval.jpg