📃🎉 Additional datasets for tensorflow.keras

Iris Recognition System Implemented in Python.

A step-wise tutorial to demonstrate the steps required to deploy a ML model using AWS Lambda, Github Actions, API Gateway and use Streamlit to access the model API through a UI.

The "IRIS Flower Classification" GitHub repository is a project dedicated to classifying iris flowers based on their attributes.

Perceptron algorithm for classification implemented from scratch

Web application for exploring Iris dataset , built using streamlit and deployed on heroku

Simple Web App for Iris Flower Classifier using Machine Learning : Advanced Python

Iris Classification served with FastAPI and Streamlit in Docker

Iris recognition using applied machine learning on CASIA iris images dataset

This project is for the Identification of Iris flower species is presented

A step-wise tutorial to demonstrate the steps required to deploy a ML model using Google Cloud Platform's AI Platform and use Streamlit to access the model API through a UI.

K-means clustering on Iris dataset. We are given a data set of items, with certain features, and values for these features. The task is to categorize those items into groups. To achieve this, we will use the kMeans algorithm; an unsupervised learning algorithm.

An application made from Flask that connects a machine learning model for the Iris toy dataset to a web interface.

DevDay León 2019 – Taller de APIs con Python.

"Iris-Classification-API" is a Github Repository that uses Flask to create an API that accepts JSON data, generates a request ID, and utilizes the Scikit-Learn library to classify Iris data. The API allows users to query the classification progress and returns the results of flower classification.

Implementation of some machine learning algorithms for classification on the iris flowers data set

A ML project on the classification of the Iris dataset, demonstrating data preprocessing, model training, and evaluation using Python and scikit-learn.