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

Code and other material for Naive Bayes KS

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.

Visualização de dados com a biblioteca Matplotlib - Python

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

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

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.

Sample tensorflow implementation for predicting iris

Repository contains deep learning projects

SOM - Self-organizing map

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

Machine Learning model that will allow us to classify species of iris flowers. This application will introduce many rudimentary features and concepts of Machine Learning. Essentially the Hello World of Machine Learning.

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

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