Photo by Gliese 293 on Unsplash

Data Science Web App - Iris flower prediction

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:

  1. Write the code in Atom and save it as a .py file

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  1. Launch Anaconda prompt to have Streamlit execute the .py file in a virtual server

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  1. Change the values of the flower’s characteristics in the left panel to predict in real time the variety of the flower

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Julio Zambrano
Mechatronics Eng.

My research interests include Data Science, Machine Learning, Robotics, Deep Learning and IoT.