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> ## Documentation Index
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> Use this file to discover all available pages before exploring further.

# I Built an Interactive Machine Learning Platform to Learn ML Visually

**[Open Source](https://daily.dev/sources/opensourcesquad)** · [@mahirmalik](https://daily.dev/mahirmalik) · 3 min read · 1 upvotes · 1 comments

## Summary

An open-source project called Confluence lets learners explore machine learning interactively rather than just reading about it. It runs 38 algorithms and 25 datasets on a FastAPI backend using real scikit-learn models, offering decision boundary visualizations, training animations, prediction explanations, side-by-side algorithm comparisons, evaluation metrics, an algorithm encyclopedia, Python code generation, and an AI assistant for concepts. The creator built it after finding existing ML learning resources scattered and mostly static, and is now seeking feedback from learners, teachers, and practitioners.

## Content

When I first started learning machine learning, I noticed a pattern.

I'd read a chapter about an algorithm, watch a YouTube video, open a Jupyter notebook, read the scikit-learn documentation, search Stack Overflow when something didn't work, and then try to piece everything together myself.

Even after all of that, I still found myself asking questions like:

- Why did changing one hyperparameter completely change the decision boundary?
- Why does one algorithm perform better than another on the same dataset?
- What actually happens while a model is training?
- Why did the model make this prediction?
-

There were plenty of great resources, but they were scattered.

Some websites had nice visualizations but only supported a handful of algorithms. Others focused on the mathematics but weren't interactive. Most demos were static, and very few let me experiment with real datasets while seeing the impact of every change.

So I decided to build the tool I wished I'd had while learning.

## Introducing **Confluence**

Confluence is an open-source platform for exploring machine learning through experimentation.

Instead of only reading about an algorithm, you can interact with it.

Choose a dataset, adjust hyperparameters, compare algorithms side by side, inspect predictions, and watch how models learn in real time.

Everything runs on a FastAPI backend using real scikit-learn models rather than simplified browser-only implementations.

## What's included?

Currently, the project includes:

- 38 machine learning algorithms
- 25 datasets (real-world and synthetic)
- Interactive decision boundary visualizations
- Training animations
- Prediction explanations
- Side-by-side algorithm comparison
- Interactive metrics and evaluation tools
- Algorithm encyclopedia
- Python code generation based on your experiment
- AI assistant for learning concepts

## What I learned

Building Confluence taught me much more than machine learning.

I had to think about application architecture, frontend performance, backend API design, visualization pipelines, state management, caching, deployment, and creating an experience that felt responsive even while running real ML computations.

It reminded me that building projects is one of the fastest ways to learn.

## I'd love your feedback

The project is still evolving, and I have a long list of ideas I want to build next.

If you're learning machine learning, teaching it, or using it professionally, I'd really appreciate your thoughts.

What would make a platform like this genuinely useful?

Website:

[https://confluence.website](https://confluence.website)

GitHub:

[https://github.com/mahirmlk/Confluence](https://github.com/mahirmlk/Confluence)

Thanks for taking the time to check it out. Every suggestion, issue report, and contribution helps make the project better.

## Community discussion

Top comments from developers on daily.dev.

**@aywalh** · 0 upvotes

> how can i get in contact with uu do u have discord bcs maybe u can help me with something

## Similar posts on daily.dev

- [Explore Machine Learning as a Knowledge Graph](https://daily.dev/posts/explore-machine-learning-as-a-knowledge-graph-ximnjfccr) · The Palindrome · 12 upvotes · 1 comments

---

Tags: [#machine-learning](https://daily.dev/tags/machine-learning), [#python](https://daily.dev/tags/python), [#data-visualization](https://daily.dev/tags/data-visualization), [#fastapi](https://daily.dev/tags/fastapi), [#scikit](https://daily.dev/tags/scikit)

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