Credit Card Fraud Detection with Logistic Regression

This repository presents a machine learning model for credit card fraud detection, specifically utilizing Logistic Regression, with the provided dataset.

Dataset

The dataset used for this project can be found at the following URL: Credit Card Fraud Detection Dataset.

Colab Notebook

For a detailed implementation and analysis of the credit card fraud detection model using Logistic Regression, please refer to the Colab notebook available here: Colab Notebook.

Selected Columns

We have chosen the following columns from the dataset for our analysis:

• "amt"
• "category"
• "gender"
• "city_pop"
• "cc_num"
• "job"
• "unix_time"
• "merch_lat"
• "merch_long"
• "is_fraud"

Categorical Columns

The following columns are treated as categorical variables in our analysis:

• "category"
• "gender"
• "job"

Logistic Regression

Logistic Regression is the machine learning algorithm used for this credit card fraud detection model. It's a binary classification algorithm well-suited for fraud detection tasks, where the goal is to classify transactions as either fraudulent or legitimate.

Model Accuracy

Our machine learning model, based on Logistic Regression, achieved an accuracy of 84% in detecting credit card fraud.

Please feel free to explore, modify, and adapt this project for your own credit card fraud detection tasks.