Real-life situations
Discussion 4
| Real-life application | Response | Predictor | Goal | Explanation | |
| Classification | Determine if a tumor is benign or malignant | benign / malignant | Size of the tumor, age of the tumor | Prediction | Involves classification of a tumor as benign or malignant based on its age and size. |
| Determine if a loan application qualifies or not | Qualify/ not-qualify | Credit worth, Amount requested | Prediction | It involves finding out if a loan application qualifies or not. | |
| Determine if launching a new product will be a success or failure | success/failure | The budget allocated for Marketing, product price and demand | Prediction | Interested in predicting if a product launch will be a success or a failure. | |
| Regression | Determine the impact of the amount of rainfall on the amount of yield | Yield amount | Amount of rainfall | Inference | Involves finding a relationship between rainfall amount and yield. |
| Determine the influence of number of sales and inflation rate on the product price | Product price | Sales and inflation rate | Inference | Involves finding a relationship between sales, inflation and product price | |
| Determine the impact of exam results from hours spent studying and practice | Exam results | Number of hours spent studying and practicing | Inference | Involves finding a relationship between exam results and the number of hours spent studying and practicing | |
| Cluster | Marketing and sales targets, when a business is trying to make an advertisement or target a specific group of people, it will need to separate people into a different cluster to see the concentration of the cluster of their interest (Olson & Shi, 2007). | – | – | – | – |
| Clustering is useful in determining if news falls under the fake news category. The content of the news is clustered, and the percentage of the clusters used to determine if the news is fake. | – | – | – | — | |
| When you need to establish distribution centers or distribute the internet, one will need to find the clusters of potential customers and set up distribution centers where all customers find it in close proximity. | – | – | – | – |
References
Olson & Shi, Y. (2007). Introduction to business data mining. Englewood Cliffs: McGraw-Hill/Irwin.