Binary Classification Streamlit App 🍄

This Streamlit app is designed to classify mushrooms as either edible or poisonous using binary classification models. It provides an interactive interface for training, evaluating, and comparing different machine learning models. Note: This project is part of the CPE312 Intro to Data Science course for Computer Engineering at SWU.

Features

• Model Selection: Choose between SVM, Logistic Regression, and Random Forest.
• Hyperparameter Tuning: Adjust model hyperparameters for customized training.
• Performance Metrics: View metrics including Accuracy, Precision, Recall, Confusion Matrix, ROC Curve, and Precision-Recall Curve.
• Data Display: Option to view raw mushroom dataset.

Getting Started

Prerequisites

Ensure you have the required libraries:

pip install -r requirements.txt

Running the App

streamlit run app.py

Project Structure

• app.py: Main Streamlit app file.
• data/: Directory containing the mushrooms.csv dataset.
• requirements.txt: Lists required dependencies.

Usage

Use the sidebar to select a classifier, adjust parameters, and view model performance metrics. The app dynamically displays evaluation metrics to help analyze each model's effectiveness.

Citations

1. Mushroom [Dataset]. (1981). UCI Machine Learning Repository. https://doi.org/10.24432/C5959T.
2. Build a Machine Learning Web App with Streamlit and Python. Coursera Project Network.