https://github.com/taymesmaee/anomaly-detection-in-transactions
Anomaly detection in transactions means identifying unusual or unexpected patterns within transactions or related activities. These patterns, known as anomalies or outliers, deviate significantly from the expected norm and could indicate irregular or fraudulent behaviour.
https://github.com/taymesmaee/anomaly-detection-in-transactions
anomaly-detection autoencoder credit-card credit-card-fraud data-science-portfolio isolation-forest machine-learning matplotlib mlops-project pandas predictive-analytics python tensorflow tutorials
Last synced: 8 months ago
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Anomaly detection in transactions means identifying unusual or unexpected patterns within transactions or related activities. These patterns, known as anomalies or outliers, deviate significantly from the expected norm and could indicate irregular or fraudulent behaviour.
- Host: GitHub
- URL: https://github.com/taymesmaee/anomaly-detection-in-transactions
- Owner: TAYMESMAEE
- Created: 2025-04-11T10:59:19.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-07-13T17:07:51.000Z (11 months ago)
- Last Synced: 2025-07-13T19:13:11.184Z (11 months ago)
- Topics: anomaly-detection, autoencoder, credit-card, credit-card-fraud, data-science-portfolio, isolation-forest, machine-learning, matplotlib, mlops-project, pandas, predictive-analytics, python, tensorflow, tutorials
- Language: Python
- Size: 41 KB
- Stars: 0
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Anomaly Detection in Transactions 🚨
Welcome to the **Anomaly Detection in Transactions** repository! This project focuses on identifying unusual patterns in transaction data. These anomalies can indicate irregular or potentially fraudulent behavior. Understanding these patterns is crucial for businesses, especially in the financial sector.
## Table of Contents
- [Introduction](#introduction)
- [Project Overview](#project-overview)
- [Technologies Used](#technologies-used)
- [Installation](#installation)
- [Usage](#usage)
- [Data Description](#data-description)
- [Model Overview](#model-overview)
- [Results](#results)
- [Contributing](#contributing)
- [License](#license)
- [Contact](#contact)
## Introduction
In today's digital world, transaction data is abundant. However, not all transactions are normal. Some may reveal fraudulent activity or errors that require immediate attention. This project aims to build a robust model to detect these anomalies using machine learning techniques.
## Project Overview
This repository includes:
- Data preprocessing scripts
- Machine learning models
- Evaluation metrics
- Visualization tools
By using this project, you can easily detect anomalies in transaction data, helping organizations maintain integrity and trust in their financial operations.
## Technologies Used
This project employs several technologies, including:
- Python
- Pandas
- NumPy
- Scikit-learn
- Matplotlib
- Seaborn
## Installation
To get started, clone this repository to your local machine:
```bash
git clone https://github.com/TAYMESMAEE/Anomaly-Detection-in-Transactions.git
```
Next, navigate to the project directory:
```bash
cd Anomaly-Detection-in-Transactions
```
Install the required packages:
```bash
pip install -r requirements.txt
```
## Usage
After installation, you can run the main script to start detecting anomalies. Use the following command:
```bash
python main.py
```
For more detailed instructions, check the [Releases section](https://github.com/TAYMESMAEE/Anomaly-Detection-in-Transactions/releases). Here, you can download the latest version and find additional resources.
## Data Description
The dataset used in this project consists of transaction records. Each record includes features such as:
- Transaction ID
- Amount
- Timestamp
- Merchant
- User ID
These features help the model identify patterns and anomalies effectively.
## Model Overview
This project primarily uses the Isolation Forest algorithm for anomaly detection. This unsupervised learning technique is effective for identifying outliers in high-dimensional datasets.
### Key Steps in the Model:
1. **Data Preprocessing**: Clean and prepare the data for analysis.
2. **Feature Engineering**: Create new features that may help in detecting anomalies.
3. **Model Training**: Train the Isolation Forest model on the prepared dataset.
4. **Evaluation**: Assess the model's performance using metrics like precision and recall.
## Results
The model successfully identifies anomalies in transaction data. The evaluation metrics indicate a high level of accuracy, making it a reliable tool for detecting irregularities.
### Visualization
Graphs and charts help visualize the results. Here are some examples:
## Contributing
We welcome contributions! If you want to help improve this project, please follow these steps:
1. Fork the repository.
2. Create a new branch for your feature or fix.
3. Commit your changes.
4. Push to your branch.
5. Create a pull request.
## License
This project is licensed under the MIT License. Feel free to use and modify it as needed.
## Contact
For any questions or suggestions, please reach out to the project maintainer. You can also check the [Releases section](https://github.com/TAYMESMAEE/Anomaly-Detection-in-Transactions/releases) for updates and new features.
Thank you for visiting the **Anomaly Detection in Transactions** repository! We hope this project helps you in your journey to understand and detect anomalies in transaction data.