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The "IRIS Flower Classification" GitHub repository is a project dedicated to classifying iris flowers based on their attributes. open-source machine-learning-algorithms logistic-regression machinelearning iris iris-data iris-dataset iris-flower-classification iris-classification. Updated on Oct 31, 2023. Python.
The Iris Flower Classification project focuses on developing a machine learning model to classify iris flowers into their respective species based on specific measurements. Iris flowers are classified into three species: setosa, versicolor, and virginica, each of which exhibits distinct characteristics in terms of measurements.
This project explores the fascinating world of machine learning through the lens of the Iris flower dataset, one of the most famous datasets used for classification tasks. Our objective is to build a predictive model capable of distinguishing between the three species of Iris flowers — setosa, versicolor, and virginica — based on the physical dimensions of their petals and sepals.
The project involves classifying Iris flower species (Setosa, Versicolor, Virginica) using the famous Iris dataset. It covers data exploration, model training, evaluation, and prediction. Two machine learning models are implemented: Naive Bayes and MLP Classifier.
Iris-Flower-Classification Project. The objective of this project is to develop a machine learning model capable of learning from the measurements of iris flowers and accurately classifying them into their respective species. The model's primary goal is to automate the classification process based on the distinct characteristics of each iris ...
The Iris flower dataset consists of three species: setosa, versicolor, and virginica. These species can be distinguished based on their measurements. Your objective is to train a machine learning model that can learn from these measurements and accurately classify the Iris flowers into their respective species - Gtindi/Iris_Flower_Classification
For example, if we choose k equal to 5, then we may get 4 labels that are Iris setosa and 1 that is Iris virginica. In this case, the estimator would predict Iris-setosa because that is the most common label. To instantiate the learner, pass the value of hyper-parameter k to the constructor of the learner. Refer to the docs for more info on KNN ...
The Iris flower dataset consists of three species: setosa, versicolor, and virginica. These species can be distinguished based on their measurements. Your objective is to train a machine learning m...
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This Project is thorugh application of machine learning with python programming. It focuses on IRIS flower classification using Machine Learning with scikit tools. Here some of algorithm are used that are some types of machine learning subparts algorithms of supervised and Unsupervised learning. Algorithm used for predicting and get accuracy are -
