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

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

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.

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"

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

• "category"
• "gender"
• "job"

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.

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.