Inspiration
From the challenge. There were 2 ways to do it, one with clustering and one with buckets and I thought that it'd be cool to try it to learn it cause it's real customer data I get to play with.
What it does
It finds anomalys in the detection.
How I built it
- Use num ID to filter similar sounding words, add to anomaly if exists only once
- Use similar to filter similar sounding words, add to anomaly if it is unique
- Date time of the columns to calculate the time since last purchase for each item
- Run Isolation Forest clustering on each group, and add it to anomaly if it is an outlier
Took 22883.20790910721 seconds(~6 hours)
Challenges I ran into
It was a really big dataset, so it was really hard to work with due to the small unique exceptions
Accomplishments that I'm proud of
It not crashing
What I learned
How to interact with real world datasets
What's next for Anomaly Detection
Make it better and faster, maybe add unsupervised learning
