This project evaluates multiple machine learning models on the Iris dataset. iris_evaluation.py includes preprocessing with standardization, hyperparameter tuning for select models, and cross-validation. iris_evaluation_normalization.py is similar to iris_evaluation.py, only difference is that iris_evaluation_normalization.py preprocesses the data using normalization instead of standardization.

• Python 3.x
• scikit-learn
• numpy
• xgboost

You can install these packages using pip:

pip install numpy scikit-learn xgboost

To run the script, navigate to the root directory:

python iris_evaluation.py

python iris_evaluation_normalization.py

The script outputs the performance metrics (Accuracy, F1 Score (Weighted), and ROC AUC (OvR)) for each evaluated model:

• Naive Bayes
• Support Vector Machine
• Random Forest
• XGBoost
• K-Nearest Neighbors

After standardization and hyperparameter tuning steps, the final performance metrics are displayed. The metrics for each model after each step are recorded in output.txt. The metrics for models preprocessing through normalization are recorded in normalization_output.txt