Hey folks,
I’m trying to move from tutorials into building actual machine learning projects, but I keep getting stuck when it comes to choosing a dataset.
Kaggle is great, but honestly, a lot of the datasets there feel too big or too messy for someone just getting started.
So I wanted to crowdsource a list:
What are your favorite beginner-friendly datasets that are fun, small-ish, and good for learning?
I’m thinking of datasets that:
- Aren’t massive (something you can play with on a laptop)
- Have a clear target or goal (classification, regression, clustering, etc.)
- Are clean enough that you don’t spend 90% of your time wrangling missing values
- Bonus if they’re quirky, fun, or make for interesting visualizations
Here are a few I’ve found so far:
- Titanic dataset – Predict survival (classic starter project)
- Iris dataset – Flower classification (super clean and small)
- Wine quality – Predict wine ratings based on physicochemical properties
- Spotify Songs – Analyze genres, moods, popularity trends
- IMDb Top 250 / Movies dataset – Fun for NLP or recommendation systems
- UCI ML Repository – Tons of smaller datasets, though the site’s kind of clunky
But I’d love to discover more. What’s a dataset you used early on that helped you actually finish a project?
Also, if you have links to your GitHub repo or blog post using the dataset, drop them—I’m sure others would love to see how you approached it.
Let’s build a go-to list for everyone transitioning from “I’m learning” to “I’m doing.”
submitted by /u/Weak_Town1192
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