Iris Flower Classification using Machine Learning

Project on Iris Flower Classification using machine learning is simple and is one of the most basic projects if someone wants to learn about machine learning. This project is basically used to differentiate between three species of the Iris flower, which are setosa, versicolor, and virginica.

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The application will work on the data given to the machine if the inputs of the flowers such as petals size and sepal size are entered.

What is the need?

The Irises are perennial plants, and there are three species setosa, versicolor, and virginica look almost identical. It is very difficult to classify them, some keeping in mind some of its physical appearances can be used to tell the species. This project involves the machine learning concept, and it collects data about the parameters such as petals size. Machine learning can be used to predict the output by studying the pattern of data.

Requirements for the project

At first, one will need a data set which consists of data source, variables, and instances. Then there will be a need for some applications such as the Root square error algorithm and some neural network devices. The neural network will compose of a scaling layer, two perceptron layers, and a probabilistic layer. One will also need a training strategy to test the results and outputs such as the Loss index and Optimization algorithm.

Project Implementation

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Initially, for preparing the project, first of all, one will need the data set, which will be composed of data sources and variables. One can download the file iris flower data, that contains information about the physical parameters of the different species of the flower. The variables should be marked as sepal length, width, and petal length, which will work with the data.

The second step will be working on the neural network, which must have four inputs as the variables are four in number. The scaling layer in the network will normalize the inputs, and the standard deviation method is used for calculations. The perceptron layer will act as a logistic activation function. This function will compare the data and then predict the results. The network has three outputs that will display the result either in setosa, versicolor, or virginica.
One should always test the applications rigorously before implementing it in real life. The errors should be kept in mind as it will help in predicting the accurate result. The optimization algorithm, such as the quasi-Newton method, is used for minimizing the loss index. One should consider a model which must have less error.

Conclusion

One will be able to predict the type of iris flower after doing this project correctly. It is one of the basic machine learning applications. Therefore it will give an idea of how ML is implemented and used.

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• Machine Learning

Iris Flower Classification using Machine Learning