The aim is to classify iris flowers among three species (setosa, versicolor, or virginica) from measurements of sepals and petals' length and width.
The iris data set contains 3 classes of 50 instances each, where each class refers to a type of iris plant.
The central goal here is to design a model that makes useful classifications for new flowers or, in other words, one which exhibits good generalization.
So we are using pycaret here,PyCaret is an open-source, low-code machine learning library and end-to-end model management tool built-in Python for automating machine learning workflows. It is incredibly popular for its ease of use, simplicity, and ability to build and deploy end-to-end ML prototypes quickly and efficiently.
PyCaret is an alternate low-code library that can be used to replace hundreds of lines of code with few lines only. This makes the experiment cycle exponentially fast and efficient.
PyCaret is simple and easy to use. All the operations performed in PyCaret are sequentially stored in a Pipeline that is fully automated for deployment. Whether it’s imputing missing values, one-hot-encoding, transforming categorical data, feature engineering, or even hyperparameter tuning, PyCaret automates all of it.
- !pip install gradio/!pip install -q gradio
- !pip install pycaret
- import gradio as gr
- import pycaret
