The aim of the iris flower classification is to predict flowers based on their specific features by help of machine learning models.
• This project dataset contains iris dataset iris.csv . Which contains four features (length and width of sepals and petals) of 50 samples of three species of Iris (Iris setosa, Iris virginica and Iris versicolor). These measures were used to create a linear discriminant model to classify the species.
• Required packages are for this project is pandas, numpy, matplotlib, seaborn, pickle, sklearn, KFold, Logistic Regression, Decision Tree, K-Neighbors, Naïve Bayes, SVC.
• Code completely implemented in python.
• Model trained and tested with supportive models like Decision Tree, K-Neighbors, Naïve Bayes, SVC
• Confusion matrix deployed with accuracy, precision, f1 score, recall, support for the above models
• Getting accuracy of the whole models and finally saving the models