Evaluating Machine Learning Techniques for Image Analysis Across a Dataset

This study explores the application of multiple machine learning techniques for image analysis using a structured dataset. Specifically, we implemented Convolutional Neural Networks (CNNs), ResNet architectures, and Random Forest classifiers to evaluate their performance in extracting features, classifying images, and identifying patterns. Comparative analysis of these methods highlights their strengths and limitations in terms of accuracy, computational efficiency, and scalability. The results provide insights into selecting appropriate machine learning approaches for diverse image analysis tasks and demonstrate the potential of combining deep learning and traditional machine learning techniques for improved performance.