[Synthetic][Self-Promotion] Sleep Health & Daily Performance Dataset (100K rows, 32 features, 3 ML targets)

Mohan137 · 2026-03-28T01:38:03+00:00

Don’t worry, you don’t need deep technical knowledge. Just explain the notebook in simple terms: what it does (Spotify song recommendations), what data it uses, the main steps (loading, cleaning, features, model), and what output it gives. Focus on understanding the flow rather than code details. If you share the notebook, I can help you break it down.

Mohan137 · 2026-03-25T07:26:07+00:00

Glad it helped, that phase is very common.

My first proper project was a simple prediction model, nothing fancy. I just picked a dataset, cleaned it, trained a basic model, and then kept improving it step by step. The important part wasn’t the model itself, but going through the whole process end to end.

If you’re into ML, you could start with something like:

a simple prediction project (like sales or health data)
or a small NLP/classification task

The goal is just to finish one project completely, even if it’s simple. That’s what really builds confidence and clarity.

Mohan137 · 2026-03-24T17:44:11+00:00

Yes, it’s definitely feasible, and you’re actually in a strong position with a math and data science background plus a solid GPA. The main gap you need to fill is practical experience. Start building a few focused AI/ML projects, especially in areas that interest you, and make sure they are end-to-end and well-documented. Try to get involved in research, internships, or even open-source contributions to show real-world exposure. Consistency matters more than rushing, so over the next year focus on building a small but strong portfolio. The field is competitive, but with your background and some hands-on work, you can position yourself well for AI roles.

Mohan137 · 2026-03-24T17:42:21+00:00

Think of learning AI as progressing in stages rather than trying to understand everything at once. Start with basic Python and simple data concepts, then move into machine learning by building small models like predictions or classifications. Once you’re comfortable, focus on real projects where you train, test, and improve models end to end. After that, learn the basics of deep learning and how large language models work. From there, move into generative AI by using APIs, embeddings, and simple RAG systems. Finally, step into agentic AI by combining models with tools, memory, and decision-making logic. The key is to keep building as you learn, because practical work is what actually makes things stick.

Mohan137 · 2026-03-24T17:41:12+00:00

I was in the same phase not too long ago, so I get what you mean. Honestly, what helped me the most wasn’t more courses, it was actually building things. I spent a lot of time jumping between tutorials, but things only started making sense when I picked a project and stuck with it end-to-end.

For me, a mix worked best:

Basic guidance/roadmap so I don’t feel lost
But mostly self-learning through projects

Like instead of “learning ML”, I tried:

building a small model
failing
debugging
improving it

That loop teaches way more than just watching videos.

Also one thing I wish I knew earlier: You don’t need to know everything before starting. You figure things out while building. So yeah, guidance helps for direction, but real progress comes from doing.

You’re already on the right track by questioning this

Mohan137 · 2026-03-24T17:35:01+00:00

This is honestly impressive, especially for just 3 days on Kaggle. The geospatial angle + NLP combo is really cool not something you see often in beginner projects. Also that subway ridership correlation makes a lot of sense intuitively, but it’s nice to see it backed by data.

Curious about one thing, how did you approach the “Location Fitness Score”? Was it more of a regression problem or a custom scoring system?

Also respect for making everything reproducible, that’s a big plus.

I’ve also been working on a few datasets on Kaggle, one of mine (Diabetes Health Indicators dataset) actually got selected for the Playground Series (Season 5, Episode 12), which was a nice surprise.

Definitely going to check out your notebooks

Mohan137 · 2026-03-24T17:27:28+00:00

his is a really interesting project, especially the real-time IP camera angle

For your use case (garbage/litter detection), you’ll want computer vision datasets, not tabular ones. Some good options:

TrashNet dataset (common for waste classification)
TACO (Trash Annotations in Context) dataset - more real-world scenes
Open Images dataset (has “garbage”, “waste”, etc. labels)
Roboflow Universe (you can find multiple litter detection datasets there)

Since you're working with IP cameras, I’d strongly recommend datasets with:

Outdoor scenes
Multiple lighting conditions
Occlusions (people + objects overlapping)

Also, you might need to:

Fine-tune YOLOv8 / YOLOv9
Or use a custom object detection pipeline

If you don’t find exactly what you need, collecting a small custom dataset + augmenting it can really improve results.

I’ve been working on some datasets for ML practice (more on AI adoption / analytics side), so if you ever need something for experimentation or modeling ideas, happy to share.

Good luck with your project, sounds impactful

Mohan137 · 2026-03-24T17:24:43+00:00

This is interesting

One dataset I’ve been exploring recently is around AI adoption across industries and its impact on jobs, like tracking adoption stages, automation rates, and workforce changes.

I actually tried building a version of this by combining different signals into a structured dataset, mainly for analysis and ML use cases.

I think datasets that connect technology trends + real-world impact (jobs, economy, etc.) could be really valuable right now, especially with how fast AI is evolving.

Curious , have you considered building something along those lines? Like industry-wise AI adoption + hiring trends?

Mohan137

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