README.md

---
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 🤖
=====================================

## 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 📁
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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 🎉
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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 📚
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1.  Select a kernel type from the tabs 📁.
2.  Adjust hyperparameters using the sliders 🔧.
3.  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 🎉
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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 🔍.