A Web App to predict the type of an Iris flower using Machine Learning (Random Forest) You can see it in action here: https://iris-random-forest-r.herokuapp.com
The purpose of this ML Web App is to make a dynamic prediction of the type of a flower based on its characteristics:
- Sepal length
- Sepal width
- Petal length
- Petal width
This project requires to have sklearn and pandas installed with Python, in order to import the dataset that contains the information of the flowers and to execute a random forest classification to predict the variety of the flower.
Procedure:
- Write the code in Atom and save it as a .py file
- Launch Anaconda prompt to have Streamlit execute the .py file in a virtual server
- Change the values of the flower’s characteristics in the left panel to predict in real time the variety of the flower
