A simple web application created for a fictional outdoors group to use as an aid to learn how to identify whether a mushroom is edible or poisonous based on selected characteristics. This was an individual school project that required cleaning a raw dataset and developing a tool that integrates real-time queries and data visualizations.
The data set was selected from the UC Irvine Machine Learning Repository then analyzed and processed using a Jupyter Notebook. The prediction model was made using Random Forest Classification and Recursive Feature Elimination resulting in 98% accuracy.
The application processes prediction requests using a REST API built in Flask. The frontend was built with Bootstrap and data visualizations are provided by Chart.js library.
- Python 3
- Flask
- HTML/CSS/JavaScript
- Bootstrap
- Chart.js
UC Irvine Machine Learning Repository Mushroom Data Set
