Fraud Detection Model

This project involves the development of a fraud detection model for a financial company. The model's goal is to proactively detect fraudulent transactions and provide insights for an actionable plan. The dataset used contains 6,362,620 rows and 10 columns in CSV format.

Table of Contents

• Overview
• Project Structure
• Data Cleaning
• Model Description
• Variable Selection
• Performance Evaluation
• Key Predictive Factors
• Fraud Prevention
• Monitoring and Evaluation

Overview

This project focuses on developing a machine learning model to predict fraudulent transactions and gain actionable insights. It involves various steps, from data cleaning to model development and performance evaluation.

Project Structure

• data/: Contains the dataset in CSV format.
• scripts/: Includes code scripts for data cleaning, model development, and evaluation.
• models/: Stores trained machine learning models.
• notebooks/: Jupyter notebooks detailing the data analysis, model development, and evaluation process.
• docs/: Documentation files for the project.

Data Cleaning

The data cleaning process includes handling missing values, detecting outliers, and addressing multicollinearity. Refer to the data cleaning scripts for details.

Model Description

The fraud detection model is designed to identify fraudulent transactions using machine learning techniques. Detailed information about the model's architecture and algorithms can be found in the model development notebooks.

Variable Selection

The selection of variables for the model is crucial. Refer to the notebooks for insights into how variables were chosen and their importance in fraud prediction.

Performance Evaluation

The model's performance is assessed using various evaluation metrics, including accuracy, precision, recall, and F1-score. Refer to the notebooks for performance details.

Key Predictive Factors

Key factors that predict fraudulent customer behavior are identified and analyzed. The notebooks provide insights into these factors.

Fraud Prevention

Recommendations for fraud prevention and infrastructure updates are discussed based on model findings.

Monitoring and Evaluation

The project outlines steps for monitoring the effectiveness of implemented fraud prevention measures. It discusses how to determine if the actions have been successful.

Installation

To run the project and its components, you'll need Python and several libraries. You can install the required libraries using the following command:

pip install -r requirements.txt