Iris_​Data_​Analysis

Logistic Regression Model Evaluation Summary

The Logistic Regression model was trained using the training dataset and evaluated on the test dataset. The model achieved perfect accuracy (100%) with no misclassifications. The evaluation metrics, including precision, recall, and F1 score, all indicate excellent performance, showing that the model can accurately classify iris species.

Decision Tree Model Evaluation Summary

The dataset was split into 70% training and 30% testing using stratified sampling. A Decision Tree model was trained and evaluated on the test data. The model achieved high accuracy (approximately 97.8%) with only one misclassification, showing strong performance in classifying iris species. The evaluation metrics, including accuracy and F1 score, indicate that the model performs very well and can reliably predict the target variable.The model demonstrates strong predictive performance and can be considered reliable for this classification task.