AI applications rely heavily on data for training machine learning models. Model performance and accuracy are contingent upon data quality and volume. Data is employed for training, testing, and validating models, enabling them to generate predictions, classifications, or decisions by recognizing patterns within the data. Two primary methods for acquiring online data for AI applications include:
1. Web Scraping: This involves extracting text, images, and other information from websites, blogs, and forums.
2. Public Datasets: Numerous organizations and institutions provide accessible datasets for AI model training, including platforms like Kaggle, the UCI Machine Learning Repository, and government databases.