Object Identification: Comprehensive Approach Using Machine Learning Algorithms and Python Tools

Computer vision systems have made advancements in object detection using Artificial Intelligence. This paper presents a comprehensive approach utilizing traditional machine learning models such as decision trees, support vector machines, logistic regression, k-nearest neighbors, and naive Bayes. These models are trained on a publicly available dataset from Github, offering diverse objects for recognition. Practical guidelines are provided for easy experimentation. Evaluation metrics include accuracy, precision, recall, and the F1 score. This paper serves as a valuable resource for object identification in the field.