iTradeNetwork's Anomaly Detection Challenge!

What it does

The iTradeNetwork challenge is to develop a system to detect anomalies within their provided dataset of perishable good supply chain invoices. The dataset is almost 500,000 entries, so our solution leverages unsupervised ML to tackle the issue! The challenge does not provide any examples of anomalous data, so we were free to create our own metric. To solve this problem, our system assigns all data samples an anomaly score, allowing us to provide the end user with a stream of potential anomalies, prioritized by likelihood to be anomalous.

How we built it

After extensive data cleaning, we developed and trained an autoencoder neural network architecture to embed the data samples. From there, we can algorithmically detect anomalies that rule-sets are likely to miss. On the front end, we provide the service through an easy-to-use web application built with React/Node/Flask.

Challenges we ran into

At the start of the hackathon, our team composed of four group members. However, as time went on, the team size fell to two. With the same goal in mind, we worked with cutting-edge machine learning algorithms that neither of us have ever written. Asides from that, we were working with quite dirty data and utilizing web frameworks that we have minimal experience in.

Accomplishments that we're proud of

We are extremely happy about the algorithms we wrote to clean and analyze the data! We fought through the struggles of losing teammates and learning new packages/frameworks and were still able to get very close to our end goal.

What we learned

Lots about new and exciting machine learning algorithms: auto encoders, variational auto encoders, and clustering. We also learned some basic multivariate statistics and how to obtain the results we hoped for. Tons of details regarding React as well.

What's next for AutoAnomaly

Some of our future goals include:

  • Building our website to display the entirety of our analysis
  • Optimize the data massaging algorithms
  • Experimenting with different neural network architectures/hyperparameters
  • Allowing users to dive even deeper and discover their own insights

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