https://github.com/arnavballincode/binary_classifier
A Streamlit web application for binary classification of mushrooms as edible or poisonous using machine learning models like SVM, Logistic Regression, and Random Forest.
https://github.com/arnavballincode/binary_classifier
Last synced: 10 months ago
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A Streamlit web application for binary classification of mushrooms as edible or poisonous using machine learning models like SVM, Logistic Regression, and Random Forest.
- Host: GitHub
- URL: https://github.com/arnavballincode/binary_classifier
- Owner: ArnavBallinCode
- Created: 2024-11-01T07:14:05.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-01-14T04:27:18.000Z (over 1 year ago)
- Last Synced: 2025-07-02T08:42:50.839Z (12 months ago)
- Language: Python
- Size: 104 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
Binary Classification Web App
This is a web app built to dynamically select a classifier from the given options along with their respective hyper-parameters and view the results in three formats.
## Classifiers Used
* Support Vector Classifier (SVC)
* Logistic Regression
* Random Forest Classifier
## Metrics Used
* Confusion Matrix
* ROC Curve
* Precision-Recall Curve
## Built With
* **Streamlit** - The web framework used
* **scikit-learn** - Machine learning library
* **pandas** - Data manipulation and analysis
* **numpy** - Numerical computing
## Deployment
(https://arnavballincode-binary-classifier-app-kt94ps.streamlit.app/)
## Getting Started
### Prerequisites
Make sure you have the following installed:
* Python 3.6 or higher
* pip (Python package installer)
### Installation
1. **Clone the repository:**
```bash
git clone https://github.com/yourusername/binary-classification-web-app.git
cd binary-classification-web-app
```
2. **Create a virtual environment:**
```bash
python -m venv myenv
```
3. **Activate the virtual environment:**
- On Windows:
```bash
myenv\Scripts\activate
```
- On macOS and Linux:
```bash
source myenv/bin/activate
```
4. **Install the required packages:**
```bash
pip install -r requirements.txt
```
5. **Run the app:**
```bash
streamlit run app.py
```