metadata

title: Svm Classifier
emoji: 📊
colorFrom: blue
colorTo: red
sdk: streamlit
sdk_version: 1.41.1
app_file: app.py
pinned: false
license: mit
short_description: SVM Classifier and the various Kernels

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

SVM Business Classification App 🤖

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Overview 📊

This Streamlit app demonstrates the application of Support Vector Machines (SVMs) with different kernel types to a non-linear business classification problem 📈. The app allows users to explore how various kernel types and hyperparameters impact classification performance 🔍.

Dataset 📁

The app uses a simulated dataset representing customer behaviors, which requires non-linear classification 📊. The dataset is structured to evaluate the effectiveness of SVMs with polynomial or RBF kernels 🤔.

Features 🎉

The app offers the following features:

Kernel Selection 🌐: Choose from Linear, Polynomial, and RBF kernel types to evaluate their impact on classification performance.
Hyperparameter Tuning 🔧: Adjust regularization (C), epsilon, polynomial degree, and gamma values to optimize model performance 📈.
Data Visualization 📊: Visualize the dataset using a scatter plot to understand the underlying structure 🔍.
Model Evaluation 📝: Assess model performance using accuracy scores, classification reports, and confusion matrices 📊.

Usage 📚

Select a kernel type from the tabs 📁.
Adjust hyperparameters using the sliders 🔧.
Evaluate model performance using the provided metrics and visualizations 📊.

Example Use Cases 📈

Business Problem Solving 💼: Use the app to explore how different SVM kernels impact classification performance in a non-linear business problem 📊.
Education and Research 📚: Utilize the app as a teaching tool to demonstrate the concepts of SVMs and kernel selection 🤔.

Conclusion 🎉

This app provides an interactive platform to explore the application of SVMs with different kernel types to a non-linear business classification problem 📊. By adjusting hyperparameters and evaluating model performance, users can gain insights into the strengths and weaknesses of each kernel type 🔍.