A hands-on repository exploring Support Vector Machines (SVMs), including classification and regression, with an emphasis on kernel tricks to solve non-linearly separable problems.
This work features three key implementations, each with Jupyter notebooks and visualizations:
- Demonstrates a Support Vector Classifier (SVC) using a linear kernel, applied to linearly separable data.
- Includes visual plots of decision boundaries.
- Investigates how different kernels — Linear, Polynomial, RBF, Sigmoid — transform data into higher-dimensional spaces.
- Uses a synthetic dataset of overlapping, circular classes to illustrate how kernels make separation possible.
- Provides 2D & 3D visualizations, accuracy comparisons, and discussion of kernel effects.
- Applies Support Vector Regression to a regression problem.
- Visualizes predictions vs actual values and evaluates model performance.
- SVMs with Kernel Tricks → Transform complex, non-linear patterns into linearly separable forms.
- Interpretable Visuals → Helps internalize how kernels reshape data.
- Real-World Relevance → Kernel-based SVMs are widely used in multiple domains.
- 🖼️ Image Classification → handwriting recognition, object detection (RBF excels).
- 🧬 Genomic Analysis → distinguishing biological sequences with non-linear patterns.
- 💳 Fraud Detection → identifying complex, non-linear financial transaction patterns.
- 🏥 Healthcare → disease prediction based on intricate medical features.
I welcome feedback, suggestions, and contributions!
- Improve visualizations 🖌️
- Add new kernels 🔧
- Include additional datasets (e.g., multiclass problems) 📊
Feel free to fork this repo, raise issues, or submit PRs.
Thanks for visiting!
I hope this repository inspires you to explore kernel methods and visualize their power.
💬 Let me know how you’ve used kernels in your projects — I’d love to connect!
📧 Author: Vaibhav Tripathi
⭐ If you find this concept useful, don’t forget to star the repo!