Overview

Fraud is a major concern for any company. The goal of this project is to create a system that monitors event transactions to accurately detect fraudulent events and present the results of the fraud predictions in an easy-to-use user interface. The provided dataset contains a mix of categorical and numerical data in varied formats, including html, datetime objects, lists of dictionaries, along with normal text and numerical values.

Features

Fraud Detection: Accurately detect fraudulent events in event transactions.
User Interface: Present the results of fraud predictions in an easy-to-use user interface.
Data Processing: Clean and process the provided dataset to prepare it for analysis.
Data Analysis: Explore the dataset to identify patterns and markers of fraudulent events.
NLP Feature Engineering: Utilize natural language processing techniques to extract meaningful features from text data.
Machine Learning: Develop machine learning models to predict fraudulent events based on the processed data.
Model Selection and Results: Select the best performing model and present the results of fraud predictions.
Production and Deployment: Deploy the fraud detection system in a production environment.

Summary

The fraud detection system aims to accurately detect fraudulent events in event transactions and present the results in an easy-to-use user interface. It involves data processing, data analysis, NLP feature engineering, machine learning, model selection, and deployment. The system utilizes a provided dataset containing a mix of categorical and numerical data in varied formats. Through data cleaning, feature engineering, and machine learning modeling, the system is able to predict fraudulent events with high accuracy. The results are presented in a user-friendly interface, making it easy for users to identify and take action against fraudulent transactions.

Jeffjohannsen

Fraud_Detection