Credit Card Fraud Detection

Overview

This project focuses on detecting fraudulent credit card transactions using machine learning techniques. The dataset used contains anonymized transaction data, and the goal is to build a model that can accurately distinguish between fraudulent and non-fraudulent transactions.

Dataset

The dataset used for this project can be accessed via Google Drive at the following link:

Credit Card Fraud Detection Dataset

Project Structure

• CreditCardFraud_Intern.ipynb: Jupyter notebook containing data preprocessing, analysis, visualization, and machine learning model training.
• README.md: Project documentation.

Installation

To run this project, you need to have the following dependencies installed:

pip install pandas numpy matplotlib seaborn scikit-learn google-colab

Steps to Run the Project

1. Clone the repository:

   git clone https://github.com/SAMI-CODEAI/CreditCardFraudDetection.git

2. Navigate to the project directory:

   cd CreditCardFraudDetection

3. Open the Jupyter notebook:

   jupyter notebook CreditCardFraud_Intern.ipynb

4. Mount Google Drive in the notebook and load the dataset from the provided link.

Data Preprocessing

• Handling missing values
• Exploratory data analysis (EDA)
• Feature scaling and engineering

Model Training

The following models are implemented and evaluated:

• Logistic Regression
• Linear Regression
• Performance evaluation using accuracy, confusion matrix, and classification report

Results

The model's performance is analyzed by:

• Percentage of fraudulent and non-fraudulent transactions
• Visualization of transaction amounts over time
• Correlation heatmap of dataset features

Visualization

The project includes the following visualizations:

• Time vs. Amount plot
• Distribution curve of transaction amounts
• Correlation heatmap