Information Entropy is a concept from information theory that measures the uncertainty or unpredictability of information content. Developed by Claude Shannon, it quantifies how much information is produced when an event occurs, with higher entropy indicating more unpredictability. For example, flipping a fair coin has higher entropy than a biased coin, as the outcome of the fair coin is less certain. In practical terms, Information Entropy helps in data compression and transmission. By understanding the entropy of a dataset, one can optimize how information is stored or sent, ensuring that the most important data is preserved while reducing redundancy.