Description: The geometric distribution is a discrete probability distribution that models the number of trials needed to achieve the first success in a Bernoulli experiment. This type of distribution is particularly useful in situations where one wants to analyze events that occur independently and with a constant probability of success in each trial. In the context of data science and predictive analytics, the geometric distribution can be used to model various scenarios, such as the success rate in event occurrences or the frequency of failures in processes. Its main characteristics include memorylessness, meaning that the probability of success in the next trial does not depend on previous results. This makes it a valuable tool for optimizing models in unsupervised learning and data mining, where the goal is to identify patterns and trends in large volumes of information. Additionally, its simplicity and ease of calculation make it accessible for applications in applied statistics and big data, where a clear understanding of the probabilities involved in decision-making processes is required.
