You will now think of some real-life applications for statistical learning.
Classification is essential as it can be applied in real applications in several cases. The first real-life application is the stock market price direction. The goal of this application is prediction. The response is up, down, input: previous price movement % change, two previous day price movement % change. It helps in providing better directions on the market price. The second real-life application is illness classification. The goal of this application is a reference. The response is that the ill, healthy, input, resting heart rate, resting breach rate, mile run time. This aspect is classified in the best to ensure that a particular study and findings are made from the illness classification. The third real-life application is car part replacement. In this application, its goals are in prediction. The response is required to be replaced, good, input, age of part, mileage utilized from current amperage.
There are three real-life applications in which regression might be useful. The first real-life application is in C.E.O’s salary. The goal of this application is inference because it is inferred from the exact salary of the C.E.O on which regression is correctly applied. The aspects that interfered with this application are age, industry experience, industry, years of education. In inferring this, they can be used in ensuring that salary is paid in consideration. The response is the salary, which depends on the listed aspects. The second real-life application is car part replacement. The goal of this application is inference. This is because it uses regression in critically discussing the car part replacement. The response is the life of the car part. The predictors are the age of part, mileage used for, and current amperage. The third real-life application of the regression is illness classification. The prediction and the response include the age of death, input, such as current age, gender, resting heart rate, resting breath rate, and mile run time.
There is three real-life application in which cluster analysis might be useful. The first real-life application is a cancer type clustering. This entails the diagnosis of cancer types more accurately. This helps in shedding more light on the cancer types and the methods of prevention and treatment. The second real-life application is the Netflix movie recommendation. Clustering helps in proposing movies based on users who have watched and rated the same film. It makes it easy to understand the movie. The third real-life application of clustering is a marketing survey — clustering of demographics for an item to see that clusters of consumers purchasing the products.