https://github.com/abhipatel35/svm-hyperparameter-optimization-for-breast-cancer

Utilizing SVM for breast cancer classification, this project compares model performance before and after hyperparameter tuning using GridSearchCV. Evaluation metrics like classification report showcase the effectiveness of the optimized model.
https://github.com/abhipatel35/svm-hyperparameter-optimization-for-breast-cancer

breast-cancer cancer-diagnosis classification data-analysis data-science gridsearchcv healthcare hyperparameter-tuning jupyter-notebook machine-learning medical-imaging pycharm python scikit-learn support-vector-machine svm

Last synced: 5 days ago
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Utilizing SVM for breast cancer classification, this project compares model performance before and after hyperparameter tuning using GridSearchCV. Evaluation metrics like classification report showcase the effectiveness of the optimized model.

• Host: GitHub
• URL: https://github.com/abhipatel35/svm-hyperparameter-optimization-for-breast-cancer
• Owner: abhipatel35
• Created: 2024-02-13T01:10:04.000Z (9 months ago)
• Default Branch: main
• Last Pushed: 2024-02-14T01:23:33.000Z (9 months ago)
• Last Synced: 2024-10-16T21:55:58.421Z (20 days ago)
• Topics: breast-cancer, cancer-diagnosis, classification, data-analysis, data-science, gridsearchcv, healthcare, hyperparameter-tuning, jupyter-notebook, machine-learning, medical-imaging, pycharm, python, scikit-learn, support-vector-machine, svm
• Language: Python
• Homepage:
• Size: 6.84 KB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Breast Cancer Classification with SVM Hyperparameter Tuning

## Overview

This repository contains code for a machine learning project focused on classifying breast cancer using Support Vector Machine (SVM) with hyperparameter tuning. The project utilizes the Breast Cancer Wisconsin (Diagnostic) Dataset, implementing GridSearchCV to optimize the SVM model's performance.

## Project Structure

- `main.py`: python file containing the code for the project.

- `README.md`: This file providing an overview of the project.

## Dataset

The dataset used in this project is the Breast Cancer Wisconsin (Diagnostic) Dataset, which contains features computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. The dataset includes features describing characteristics of cell nuclei present in the image.

## Dependencies

- Python 3.x

- scikit-learn

- Jupyter Notebook

## Usage

1. Clone the repository:

   ```bash

   git clone https://github.com/abhipatel35/SVM-Hyperparameter-Optimization-for-Breast-Cancer.git

2. Navigate to the project directory:

   ```bash

   cd breast-cancer-svm

3. Install dependencies:

   ```bash

   pip install -r requirements.txt

4. Open and run the 'main.py' python file in Jupyter Notebook or PyCharm.

## Results

- The initial SVM model performance is evaluated without hyperparameter tuning.

- GridSearchCV is employed to optimize SVM hyperparameters (C, gamma, kernel).

- Model performance is compared before and after hyperparameter tuning using metrics like classification reports.