Analyzing Fraudulent E-commerce Transaction by Triple K

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  The website dashboard of Analyzing Fraudulent E-commerce Transaction by Triple K

Inspiration

Since the onset of COVID-19, e-commerce transactions have surged dramatically, accompanied by a corresponding spike in fraudulent activities. In response to the growing demand for enhanced security on e-commerce platforms, Triple K has embarked on a project aimed at identifying the primary factors contributing to fraud and developing effective prevention strategies. Our mission is to fortify the e-commerce environment, safeguarding users from financial losses and ensuring a secure, trustworthy shopping experience.

What it does

"Analyzing Fraudulent E-commerce Transactions by Triple K" leverages advanced data analysis to identify patterns indicative of fraudulent activities. Our system processes transaction data, cleans and prepares it, and shares insights through an interactive web interface. This allows stakeholders to visualize and understand potential fraud patterns and take preventive measures.

How we built it

Dataset

We began by obtaining a synthetic dataset designed to simulate realistic e-commerce transactions. The dataset is retrieved from Kaggle 🚨 Fraudulent E-Commerce Transactions 💳

Backend

Using Python, we performed extensive data cleaning to ensure the dataset was free of inconsistencies and ready for analysis. FastAPI, a modern web framework for building APIs with Python, was used to develop an API that serves the cleaned data to the front end.

Frontend

The front end, built with HTML, CSS, and JavaScript, uses Chart.js to create interactive visualizations that display transaction patterns and potential fraud indicators.

Collaboration Platform

GitHub was instrumental in our collaboration, providing version control and facilitating teamwork throughout the development process.

Challenges we ran into

One of the main challenges we faced was dealing with synthetic data that included fake geographical locations, IP addresses, and customer IDs. These inaccuracies made it difficult to acquire a realistic demographic profile and map the data to actual locations. Ensuring that our analysis remained meaningful despite these limitations required creative problem-solving and adjustments to our approach.

Accomplishments that we're proud of

We are proud to have developed a seamless pipeline that cleanses transaction data and presents it in a user-friendly and insightful manner. The integration of Chart.js for visualization and FastAPI for data sharing has resulted in a powerful tool that aids in understanding and identifying fraudulent activities. Our effective use of GitHub for collaboration ensured that the project was completed efficiently and cohesively.

What we learned

As first-time participants, the journey has been both exhilarating and enlightening, fueling our passion for future endeavors in the hackathon realm. This project provided valuable lessons in data cleaning and preparation, the use of APIs for data sharing, and the importance of effective data visualization. We learned how to leverage FastAPI for building robust APIs and the strengths of Chart.js in creating interactive and informative charts. Additionally, we learned to navigate the challenges posed by synthetic data and still derive meaningful insights. The experience also highlighted the importance of teamwork and version control in software development projects.

What's next for Analyzing Fraudulent E-commerce Transaction by Triple K

Moving forward, we plan to enhance our system by incorporating more complex data analysis techniques and expanding the dataset to include more diverse transaction scenarios. We aim to develop more detailed visualizations and provide deeper insights into transaction patterns. Additionally, we hope to integrate machine learning models for real-time fraud detection in the future, providing a comprehensive solution for e-commerce platforms to ensure a safer online shopping experience.

Built With

• chartjs
• css
• fastapi
• github
• html
• javascript
• python