## 9 Applications of Machine Learning
The study showed that the SVM along with feature selection can yield up to 92.37% accuracy. * Financial sector: It is a sector that generates a humongous amount of data every day. These data vary a lot, it can be transaction data, loan application data, borrowers' personal data, stock data, company information data, and others. Deep learning algorithms can be used to predict stock prices [140]. The base model for the classification problem which is logistic regression can be used to predict the loan defaulter with an accuracy of 81.1% [141]. Similar research done by the authors of [142] showed that the loan default prediction accuracy can be 100% with the AdaBoost model. In the financial sector financial crisis also play a considerable role in economics. Therefore, to predict the financial crisis authors of [143] developed a hybrid model of K-means and genetic ant colony algorithm. To access the model authors used tested using the Qualitative Bankruptcy dataset [144], Polish dataset [145], and Weislaw dataset [146] data. The proposed model showed an accuracy ranging from 97.55% to 97.93% for different datasets. * Autonomous Driving: Enhancing the safety of transportation has become feasible as a result of the quickening pace at which artificial intelligence is being incorporated into motor vehicles. It has been demonstrated that safety features such as collision detection, park assists, and lane change assist are particularly beneficial in the prevention of accidents [147; 148; 149]. Taking it a step further, machine learning methods for multiple object detection, trajectory prediction, motion, and speed estimation have been crucial in the development of autonomous vehicles. Even if the number of autonomous vehicles is still expanding and they are not entirely self-sufficient, additional research and data are required to demonstrate the effectiveness of such automobiles. Cybersecurity is another issue that needs to be addressed in relation to autonomous vehicles; this is due to the fact that these vehicles are equipped with a large number of sensors that provide driving assistance and that their battery management systems communicate data to the cloud. A detailed survey done in [150] presents various cyber threats and their prevention technique in autonomous vehicles. * E-Commerce: Another industry that largely relies on machine learning is the e-commerce industry. The forecast of the product demand is done with the help of machine learning. For instance, during the winter months, sales of seasonal products like jackets are at an all-time high, and the demand for products that are used on a daily basis can skyrocket if there is any kind of severe weather alert. This kind of projection contributes to the more efficient management of supply chains [151]. In addition, online retailers often send personalized notifications to customers depending on the users' search histories or the products they have bought in the past. The recommender system is another area in which e-commerce companies invest a lot of money. This system helps retailers propose products that are similar to ones that customers have already purchased or are considering purchasing [152; 153]. In the media industry, comparable articles are suggested to users based on their reading histories. An SVM-based recommender system was proposed in ref. [154]. Some of the most prominent challenges in news recommender systems including cold start and data sparsity are discussed in ref. [155]. Furthermore, in the entertainment industry, similar shows are suggested to users depending on whether or not they have seen them or put them on their whitelists. A k-means and KNN algorithm-based recommender system is proposed in ref. [156] Both industries take a similar strategy. * Satellite Communication: Machine learning (ML) has demonstrated significant potential in improving various aspects of satellite communication systems, making them more efficient, reliable, and adaptable.