Forecasting, according to the Merriam Webster dictionary, is the art of declaring or indicating in advance, especially predicting based on observation, experience, or scientific reason. According to the Cambridge dictionary, prediction is a statement about what you think will happen in the future. Prediction is made about the outcome of the future based on a pattern of evidence. This is done on the basis of previous knowledge or evidence. In statistics, prediction is a conclusion based on statistical inference while in science it is a rigorous and often quantitative analysis of past and present data or events to predict what will happen under certain conditions. Say no to plagiarism. Get a tailor-made essay on "Why Violent Video Games Shouldn't Be Banned"? Get an Original Essay Forecasting has been applied in virtually every area of ​​our lives; in medical research, engineering, geography, forecasting, finance and market, sports, gaming, technology, communication, construction and so on. Predictions have come a long way into our daily lives. Amazon, Jumia, and Konga predict what else you might want to buy every time you shop. Netflix and other movie sites predict the movie you might want to watch. Google predicts how you will respond to your emails. Match.com and other dating sites are even trying to predict who you might fall in love with. We can see the prediction in homes where children predict when their father will be home, wives predicting their husbands' movement. Even in the institution where a teacher predicts the grade that a student will eventually be able to graduate based on the current grade and its seriousness. These predictions have become part of us to the point that we don't even notice them anymore. Machine learning was applied to aid in this prediction. Machine Learning is a current application of artificial intelligence based on the idea that we should be able to give machines access to data and let them learn on their own. Machine learning can take large amounts of data that humans cannot understand and process it at great speed. Machine learning has been around since the 20th century, but is only now finding use thanks to the powerful computers we have now that are capable of running it. In the 20th century, there were no powerful computers that could run it, and even today only a few computers can run it well and efficiently. The availability of large data also improves the use of machine learning because the algorithms used in the machine need as much large data as possible to be trained with to ensure accuracy and efficiency. There are three methods used in machine learning: supervised, unsupervised, and reinforced learning. In supervised learning, you train the algorithm with the data that contains the answer. For example, when you train a machine to identify your friends by name, you have to identify them for the computer. If you have trained an algorithm with data where you want the machine to understand the pattern on its own, it is called unsupervised learning. If you give a machine a goal and expect the machine, through trial and error, to reach the goal, it is called reinforcement learning. Few publicized examples of machine learning applications are: Google's self-driving car, online recommendation offerings like those from Amazon and Netflix, knowing what customers are saying about you on Twitter, fraud detection, voice and image recognition. Please note: this is just an example. Get a custom paper from our expert writers now. Get a Custom Essay Prediction has been so ubiquitous that we apply it to almost every.