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README.md

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 title: Svm Classifier
-emoji: 🏢
+emoji: 📊
 colorFrom: blue
 colorTo: red
 sdk: streamlit
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 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 📁
+------------
+
+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 📚
+---------
+
+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 🎉
+----------
+
+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 🔍.