Credit card fraud detection is an important aspect of financial security that involves identifying and preventing unauthorized or fraudulent use of credit card information. Various techniques and strategies are employed to detect and mitigate credit card fraud.

Machine learning techniques can be applied to build models that learn from historical data to identify patterns and anomalies associated with fraudulent transactions. These models can analyze numerous factors like transaction history, spending patterns, location, and user behavior to detect fraudulent activities.

The following datasetcontains 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.

Goals: Examime the dataset and develop ML models to detect if a transaction can be classified as fradulent or not.

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