Fall 2021 Capstone Project by Jeffrey Romero
All project files are available for viewing/downloading on my GitHub repository.
Credit card fraud is a problem that can target any person at any time. However through the use of machine learning, it becomes easy to spot fraudulent credit card transactions. The goal of this project is to create a machine learning model that can predict whether a credit card transaction is fraudulent or not. The input data used to train the machine learning model has been downloaded from Kaggle.
A machine learning model can only predict fraudulent transactions based off formatted input data. There are a series of steps that the input data goes through before training a fraud prediction model:
This project undergoes a process of creating a machine learning model as shown in the figure below.
For a machine learning model to predict whether a transaction is fraudulent or not, input data is required. Such input data is formatted as a .csv file which contains data related to credit card fraud. This input data is then sanitized so the model can make sense of it.
Multiple models will be trained with different machine learning algorithms. Each algorithm has its own technical approach when predicting fraudulent outcomes.
The different machine learning models are compared with each other. The model with the most accurate fraudulent transaction prediction rate will be chosen.
There could be more cases of fraud compared to regular transactions. This imbalance of data can affect the model's prediction accuracy. To fix this, the model can be improved by using different data optimization algorithms.