Pre-requisites & Tech Stack Used

Before building a fraud detection model, it is important to understand the tools, libraries, and technologies required for the project. Fraud detection involves working with large datasets, preprocessing transaction records, training machine learning models, and evaluating their performance. Having a properly configured environment ensures that each stage of the workflow can be executed efficiently.

In this project, we will use Google Colab as the development environment and Python libraries specifically designed for data analysis and machine learning. We will also work with a real-world credit card transaction dataset that demonstrates the challenges of highly imbalanced data.

By the end of this module, you will understand the project requirements, the technologies involved, the dataset structure, and the complete workflow followed to build the fraud detection system.

Before starting this project, ensure that you have access to the following:

Google Colab

Google Colab is a cloud-based notebook environment that allows users to write and execute Python code directly from a web browser without installing software locally.

Google Account

A Google account is required to access Google Colab and save notebooks to Google Drive.

Credit Card Fraud Dataset

The project uses a publicly available dataset containing genuine and fraudulent credit card transactions.

Basic Python Knowledge

Learners should be familiar with:

• Variables
• Loops
• Functions
• Conditional statements
• Basic Python syntax

Basic Machine Learning Knowledge

Understanding the following concepts will be helpful:

• Features and target variables
• Classification problems
• Training and testing datasets
• Model evaluation

Since the project is implemented entirely in Google Colab, no local installation of Python is required.