Iris Dataset Classification Using Machine Learning

The well-known Iris dataset is used in this case study to use the K-Nearest Neighbors (KNN)method. The 150 iris flower observations in the Iris dataset include 50 observations of each of the threespecies—Setosa, Versicolor, and Virginica. This case study aims to identify the four characteristics of sepallength, sepal breadth, petal length, and petal width that may be used to categorize iris flowers into theirrespective species. The KNN method is a well-liked and straightforward classification technique that makespredictions by locating the nearest neighbors of each observation. To guarantee that all of the characteristicsin this case study are on the same scale, the dataset is first divided into training and testing sets. The nextstep is to train a KNN model with k=3, which takes into account each observation's three nearestneighbours.This study utilizes multiple machine learning algorithms for flower classification, including KNearest Neighbors (KNN) for distance-based classification, Decision Tree for rule-based decision-making,Logistic Regression for probability estimation, Support Vector Machine (SVM) for margin-basedclassification, Naïve Bayes for probabilistic modeling, and Random Forest for ensemble learning toenhance accuracy and reduce overfitting, collectively improving the reliability of speciesidentification.Lastly, the accuracy score is used to assess how well the model performed on the test set,Information may be obtained easily because the Iris dataset is readily available from a number of sources,including the Python Sci kit-learn library. Exploratory data analysis is done to see how the data aredistributed, identify trends, and understand how the features relate to one another. Common visualizationtechniques including scatter plots, pair plots, and histograms are used to analyze the data. Preprocessing ofthe information entails addressing any missing attributes (which are absent from this dataset) andnormalizing the component values to ensure that they have a zero mean and a one standard deviation. Thisis a crucial step for machine learning algorithms that are sensitive to feature scaling.