CREDIT_CARD_FRAUD_DETECTION

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.

These Machine Learning Algorihtm are used in this project :

1. Logistic Regresion
2. Support Vector Machine
3. K Nearest Neighbour
4. Decision Tree Regression

-- Project Status: [Completed]

Getting Started

1. You can access the raw dataset here within this repo.
2. All of the scripts are being kept here.

Result

This Machine Learning Model achieves a accuracy of 99% using Above Mentioned Algorithms .

I hope this will be useful someday, thankyou for seeing !✌🏻