adil-imran / Credit_Card_Fraud_Detection_PyCaret

Credit_Card_Fraud_Detection_PyCaret Auto ML

Overview

1.1 The agenda is that credit card companies are able to recognize fraud credit card transactions.
1.2 Customer should not charged for fraud transactions

Content

• The dataset 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. The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions.

• It contains only numerical input variables which are the result of a PCA transformation. Unfortunately, due to confidentiality issues, we cannot provide the original features and more background information about the data. 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'. 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.

In this notebook we will perform the following task:

• Data Analysis
• Model Building and Prediction using PyCaret(Auto ML)

Findings¶

• PyCaret handled the imbalanced data in a very effective way.
• Model Accuracy is 99%
• Kappa scored 85