A. AutoML, short for Automated Machine Learning, streamlines the model development process by automating various stages, including importing essential libraries like numpy and pandas, initializing diverse algorithms such as SVM and DecisionTreeClassifier, and implementing techniques like logistic regression. Through sample weights and optimization, AutoML fine-tunes model parameters to enhance performance on datasets. Leveraging majority vote, AutoML combines predictions from multiple models for improved accuracy. Additionally, AutoML facilitates seamless integration with GitHub repositories and offers tutorials for beginners to swiftly navigate its functionalities, making it a comprehensive solution for automating machine learning workflows, particularly evident in tasks like implementing AdaBoost and evaluating models on x_test data.