Comparing Classification Models: Logistic Regression, Naive Bayes, and SVM for Iris Dataset

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 This article is a report on my Machine Learning assignment, which involves implementing various Classification Models — specifically Logistic Regression, Naive Bayes, and Support Vector Machine — and comparing the results using popular metrics to find the best model.

For this assignment, I used the Iris dataset. It is a famous dataset from 1936, often used for testing machine learning algorithms due to its simplicity. It contains 150 samples of iris flowers with attributes such as sepal length, sepal width, petal length, and petal width, each belonging to one of three species: Setosa, Versicolor, and Virginica. You can find this dataset on Kaggle.

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