Iris Flower Classification using python || Machine Learning project

Hello Viewers,
This time I have created a classification model for Iris dataset. The project is about building a machine learning classification model for the Iris dataset. The dataset contains measurements of 150 iris flowers from three different species, and the goal is to predict the species of a new flower based on its measurements. The project will use Python and its machine learning libraries, including NumPy, Pandas, Scikit-learn, and Matplotlib.

The project will start with data exploration and preprocessing, where the dataset will be cleaned, transformed, and split into training and testing sets. The models' performance is evaluated using various metrics such as accuracy, precision, recall, and F1-score.

The results are visualized using Matplotlib.

This project provides a comprehensive guide on how to build a machine learning classification model using the Iris dataset. It is suitable for individuals with beginner to intermediate level machine learning skills and will help them gain an understanding of the various steps involved in building a classification model.

Ready to dive deeper into the project? Check out the source code and start exploring! :-

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