Implementing a Fraud Detection System Using Logistic Regression

In this module, we will implement the Credit Card Fraud Detection project using Python in Google Colab. The implementation follows a structured machine learning workflow, beginning with loading the transaction dataset and ending with evaluating the trained Logistic Regression model. Along the way, we will inspect the dataset, preprocess the data, split it into training and testing sets, train the model, and assess its performance using appropriate classification metrics.

Rather than focusing only on writing code, each lesson explains why every step is necessary, helping learners understand how a real-world fraud detection system is developed.

Before building the fraud detection model, we need to prepare our working environment.

Google Colab allows us to execute Python code directly from the browser without installing additional software.

Upload the dataset using the following code.

Code

from google.colab import files

uploaded = files.upload()

After uploading, the dataset will be available inside the Colab environment.