## 1 Introduction  
In the modern world, the price of data storage is decreasing, there is a rapid increase in the speed of data processing, and with the increase in the integration of the Internet of Things, a massive amount of data is generated and stored in data repositories around the globe. In addition, the cost of data storage is decreasing, which means that it will become more affordable in the near future. A new subfield of research known as machine learning has been given the opportunity to flourish as a result of the availability of this massive dataset. If we take a glance around, we can see examples of machine learning in almost every field. For instance, if we are watching a movie or web series, there is a recommender system that will suggest movies similar to the one we are watching. Likewise, if we buy online, it will suggest products that are either similar to the product we bought or products that were sold most frequently alongside the product we bought. This is done with association rule mining. The internet search engine is another application of machine learning that we use on a daily basis; for example, we used it to forecast tourism based on internet search [1]. When we enter a query into the search engine, the list of links returned to us is arranged from most relevant to least relevant to our search. This is accomplished through a ranking system that will be discussed further in this article. The techniques of machine learning are also helpful in the field of cybersecurity [2]. Algorithms such as isolation forests, support vector machines, neural networks, and others have been shown to be very useful in predicting any intrusion into the network. Therefore, we can say that machine learning is in every sector. The data are the most important component of machine learning. These data can originate from a wide range of areas, including social networks, logs, blogs, and various sensors such as temperature sensors, current sensors, and humidity sensors, among others. The properties of these data can vary quite a bit. For instance, the data from social networks can come in the form of logs, speech data, image data, video data (a sequence of images), and text data. The data from sensors comes in vectors, and their data type can be float, integer, or string. The data can also be saved in the form of lists or some other format; the specifics of how the data are stored are determined by the conventions that are followed by the data storage repository. In a nutshell, we can say that a complete machine-learning framework consists of three phases: data scraping, model building, tuning and testing, and model deployment. Before model building, tasks such as preprocessing, algorithm selection, statistical analysis, and correlation analysis are carried out. Figure 1 gives a concise overview of the field in which machine learning is now being applied. Machine learning is becoming an increasingly important tool in the banking industry for identifying potentially fraudulent activities. A few of the criteria that play a role in determining whether or not someone is engaging in fraudulent activity include the following: the time of the transaction, the amount involved in the transaction, the geographical location, and information on previous transactions. In a similar vein, the probability that the borrower will fail on their loan may be forecasted if the model is fed with the borrower's previous credit history as well as his behavior with his previous lenders. The energy industry is another industry that makes extensive use of machine learning. Machine learning is extremely important in many aspects of the energy industry, including but not limited to economic dispatch, customer grouping, optimal load scheduling, and the detection of energy theft. In addition, the implementation of the smart grid, which incorporates a significant amount of renewable resources, has resulted in their network being flooded with thousands of sensors.