O𝒊𝒃𝒔𝒊𝒑_𝒕𝒂𝒔𝒌𝒏𝒐_1

IRIS FLOWER CLASSIFICATION USING PYTHON

This project aims to solve the IRIS Flower Classification problem using machine learning techniques, specifically employing a decision tree algorithm and evaluating the model’s performance using a confusion matrix. The IRIS Flower Classification problem involves identifying different species of iris flowers based on their measurements. The decision tree algorithm is a popular supervised machine learning technique employed in this project. The algorithm builds a tree-like model, where each internal node represents a feature or attribute, each branch represents a decision rule, and each leaf node represents the outcome or class label. By recursively partitioning the dataset based on feature values, the decision tree algorithm learns to accurately classify iris flowers into their respective species. The IRIS Flower Classification project includes the following components: Preprocessing the dataset: Cleaning and preparing the iris flower dataset for training and testing the model. Building the decision tree model: Implementing the decision tree algorithm using a suitable machine learning library. Training the model: Fitting the decision tree model to the training dataset to learn the patterns and relationships. Testing the model: Evaluating the performance of the trained model using the test dataset and generating predictions. Confusion matrix analysis: Constructing the confusion matrix to assess the model’s accuracy and identifying misclassifications. Evaluation metrics calculation: Computing evaluation metrics such as accuracy, precision, recall, and F1 score based on the confusion matrix. Model deployment and usage: Providing a method to utilize the trained decision tree model to predict the species of iris flowers in new, unseen data.