Implement Supervised ML Algorithms with Visualizations

[GauravSingh0248]

🧠 Feature Request: Supervised ML Module

I would like to implement a module named supervised_module.py that includes commonly used supervised machine learning algorithms along with visualizations for better understanding.

✨ Algorithms to be included:

• Linear Regression
• Logistic Regression
• Decision Tree
• Support Vector Machine (SVM)
• K-Nearest Neighbors (KNN)

📊 Visualizations:

• Training vs test split visualization
• Decision boundaries for classification
• Regression line and error visualization
• Confusion matrix (heatmap)

📁 Structure:

• Code will be modular and reusable
• Uses matplotlib / seaborn for visuals
• Written in Python using scikit-learn

📌 Tools/Libraries:

• sklearn
• matplotlib
• numpy
• seaborn

---

Please assign this issue to me under GSSoC '25. I’m excited to contribute!

Thanks!

[viv2005ek]

I have already assigned an issue for KNN but can work on the other features

To maintain consistency across our project structure, you can follow this architecture:

Steps to proceed:

1. Inside supervised_module.py, modularize each algorithm into its own function, e.g., run_linear_regression(), run_knn_classifier(), etc.

2. Include Streamlit UI widgets like sliders, dropdowns, and toggles to customize parameters (e.g., test size, kernel for SVM).

3.Add appropriate visualizations for each model:
-Regression line & residuals
-Decision boundaries (for classifiers)
-Confusion matrix heatmaps
-Accuracy vs. parameters plots

4.Each function should return Streamlit components which can be directly rendered from app.py.
5. also make a docs/ folder to maintain documentation for each feature

[viv2005ek]

I'm not assigning a label to it right now, but if it's done properly, I'll label it as Level-3 (highest level).
While making the PR, please mention this issue as: Fixes #<issue_number>.

assigned #9
level-2 (as of now)

[GauravSingh0248]

so right now what should i do , should I go for the right one ?

[viv2005ek]

you can work on-
Linear Regression
Logistic Regression
Decision Tree
Support Vector Machine (SVM)

but maintain consistency across our project structure and follow all steps as I instructed

[GauravSingh0248]

So I would request you to assign me this .

[AKSHITA-tech2]

can you assign me this?

[manasvi-0]

@AKSHITA-tech2 if you want to you can work on unsupervised

[AKSHITA-tech2]

Yes I want to

…

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[manasvi-0]

@viv2005ek please guide @AKSHITA-tech2

[viv2005ek]

@AKSHITA-tech2 Please create a new issue and mention which algorithms you'd like to work on in the description. Once you do that, I’ll share the project structure and the steps to follow within that issue.