# A Machine Learning Approach to Predict Customer Usage of a Home Workout Platform
## 2 Theoretical Background and Previous Studies
### Machine Learning Methodology  
Machine learning is a method of implementing AI in which computers derive new information or make decisions by learning data using algorithms and programs, similar to human learning [6]. Conceptually, it is similar to data mining. Data mining refers to a set of tasks that can derive meaningful patterns, knowledge, or information from large amounts of data to produce results that can help make decisions [24].  
The concept of AI was proposed in the 1950s, and it depends on neural network algorithms. In the 1980s, theoretical research on machine learning algorithms was considerably developed, but its application based on hardware development was quite limited. By default, the installation of machine learning algorithms is limited to a naive Bayesian classifier, k-nearest neighbors (KNN), induction of rules and trees, support vector machines (SVM), neural networks, linear and logistic regression, and ensemble methods.  
Machine learning can be useful when the size of the data is too large for humans to solve because it can be defined mathematically. However, the mathematical definition of complex problems is difficult [25]. Thus, results below the human level in terms of real-world performance are often provided or are not available or difficult to be used in real-world environments.  
Recently, the use of machine learning has rapidly expanded due to the activation of big data and the remarkable development of hardware, along with the development of deep learning algorithms that are more advanced than machine learning. Based on machine learning, patterns can be automatically extracted from data. Generally, machine learning can be divided into supervised learning, unsupervised learning, and reinforcement learning [26]. Algorithms in machine learning are usually used for prediction, classification, and clustering [27].  
Unsupervised learning is a method that analyzes unanswered data [28] and finds clusters similar to input values in situations where users do not know to which class the data belong. The algorithm finds and produces similarities on its own, and this process enables the identification of the characteristics of each group [29]. Examples such as clustering of data, detection of anomalies, and speculation of data distribution are the most different to map learning [30], where the nature of the data is directly guessed with no need to create a function to calculate a particular value.  
If unsupervised learning is utilized as a classification method, the model to be used in data prediction analysis is based on supervised learning. Map learning enables a model to automatically learn the relationship of attributes from past data [21]. Unsupervised learning exploits unanswered data, but in the case of supervised learning, the answer comes from the data, which creates a general decision rule [23].  
Machine learning has been widely used in many fields, including disease diagnosis [31], stock trend prediction [32], image and speech recognition [33; 34], and information extraction [35]. In the machine learning approach, a prediction model can be trained using input data to achieve a goal without solving theoretical equations.