Supervised ML - Classification Using Python 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% .

A project created to build and use a convolutional neural network model to classify the denomination of Indian coins and classify the validity of coin as genuine or fake/invalid. This project also contains a dataset of around 2000 images of valid and invalid coins, on which the NN model was trained.

# Fraud Detection Model This code reads in a dataset of financial transactions and builds a model to predict whether a given transaction is fraudulent or not. ## Dataset The dataset used in this code is `fraud.csv`. It contains the following columns: - `step`: