This project is a Streamlit-based web application for binary classification using machine learning models. The app is designed to predict whether mushrooms are edible or poisonous based on their features.
- Interactive UI: Built with Streamlit, allowing users to select classifiers and tune model hyperparameters from the sidebar.
- Supported Classifiers:
- Support Vector Machine (SVM)
- Logistic Regression
- Random Forest
- Model Metrics: Displays accuracy, precision, and recall for predictions.
- Visualization: Plots Confusion Matrix, ROC Curve, and Precision-Recall Curve for model evaluation.
- Data Handling: Loads and encodes the mushroom dataset (
mushrooms.csv) for classification. - Raw Data View: Option to display the raw mushroom dataset in the app.
You can access the live web app here:
https://machine-learning-web-app-mushrooms-idd56fykaeyes5hxec6jyg.streamlit.app/