Iris Database Analysis Using Classification And Regression Algorithm

  • Pratiksha G . Kande, Yogesh R. Risodkar, Manish R . Patil, Arti S . Kothule


Dataset is harder to handle, predict and classify. So using machine learning algorithm we can easily predict and classify the data. we use the kNN algorithm for the classification purpose and linear regression for prediction purpose. This are the type of supervised learning by using this algorithm we can analyze the data. We use K Nearest Neighbor (KNN) for classification because it is simple for implementation and give significant classification performance. So using this algorithm we can classify the test datapoint into several classes and also we visualized all the data points using various plot. We have taken the IRIS dataset of  flower with three unique types of Iris flower that is Setosa, Versicolor and Virginica with four features of flower and we can predict the type of the test flower using linear regression and K Nearest Neighbor algorithm. we evaluating and applying the kNN algorithm and linear regression on Iris dataset.