Inspiration
We took on the challenge out of curiosity about how spam detection systems work and how they're trained. It was a great opportunity to experiment and learn.
What it does
Our system scans messages and flags spam using machine learning, helping to filter out unwanted content.
How we built it
We used Python with sci-kit-learn to train our model on a dataset of spam and legitimate messages. We focused on extracting features and tweaking the model for accuracy.
Challenges we ran into
Balancing the dataset and picking the right features were tough. We also had to ensure our model didn’t flag too many legit messages as spam.
Accomplishments that we're proud of
We’re proud of getting the model to work accurately in a short time,
What we learned
We learned a lot about machine learning, especially text classification. It was also a great exercise in teamwork and rapid development.
What's next for Spam Detection
We plan to improve the model further and maybe improve the accuracy.
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