Implementation of Kernel SVM Classification Algorithm in Python | IRIS | Machine Learning | 2021

Google Colab: https://colab.research.google.com/
Iris Dataset Link: https://drive.google.com/file/d/11Wypj0cnLQfI1508rwPjSo8GroRGX6Z2
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Application of Support Vector Machine (SVM) in IRIS Dataset. Flower classification by SVM algorithm. The fun fact is Scikit Learn library is very useful for classification algorithm implementation. Only three lines of code can do the job!

The Basics of Support Vector Machine (SVM):
In a supervised classification problem, data need to be separated into training and testing sets. Each sample point in the training set consists of a class label and several features or observed variables. The objective of SVM is to build a model based on training data samples to prognosticate the class labels of test data given solely test data features. SVM algorithm performs linear categorization by obtaining the hyperplane that boosts the margin between two classes. The data points that determine the hyperplane are support vectors. In essence, for a set of given labeled training data, the SVM algorithm obtains a most favorable hyperplane which classifies new test data.

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