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

Code and other material for Naive Bayes KS

Visualização de dados com a biblioteca Matplotlib - Python

Using k-Nearest Neighbors algorithm, training it using 2/3rd of the iris.data and using the rest of the 1/3rd for the test case, and yield prediction for those 1/3rd with an accuracy usually greater than 90% , and this algorithm is implemented without using Python scikit-learn.

Implementation & Learning of Iris Data-set and use of various Machine learning Algorithm

IFPaaS - Iris Flower Prediction as a Service. An implementation of a machine learning microservice and its deployment to the cloud using: Python, Flask, Sklearn, Docker, CloudFormation, ECS, Fargate, Pytest, Travis CI, RESTPlus and Gunicorn.

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

Repository contains deep learning projects

Sample tensorflow implementation for predicting iris

SOM - Self-organizing map

Simple Classification program to predict the species of an iris flower.

The following code uses 5 different machine learning algorithm on the Iris dataset to predict the species of the flower

This is a classification of iris flower using k nearest neighbour classifier

Implementation of K-Means clustering algorithm in python

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

This project uses the K-Nearest Neighbors (KNN) algorithm to classify Iris flowers based on their sepal and petal measurements. The dataset used in this project is the Iris Dataset, which includes 150 samples of Iris flowers, each with four features: sepal length, sepal width, petal length, and petal width.

Multilabel Classification of the famous Iris Flowers Dataset from Ronald Aylmer Fisher in 1936

Iris flower classification among setosa, verginica and versicolor.