Part 2: Predictive Analytics for Targeted Advertisement Predictive analytics holds significant potential in targeted advertising by utilizing data-driven insights to effectively identify and engage prospective customers. By examining historical data, behavioral patterns, and customer preferences, predictive analytics can greatly enhance targeted advertising efforts. In particular, by discerning individual customer preferences and behavior, predictive analytics enables the creation of personalized advertising content. This ensures that ads are more relevant and appealing to the target audience, resulting in improved engagement and increased conversion rates. Consider the scenario that VCUTech, an e-commerce startup that recently launched an online marketplace platform catering to students, faculty, and staff at VCU for purchasing electronics. Although various electronic products on the company's marketplace are selling well, one laptop item is significantly underperforming. As a result, VCUTech plans to promote this product through targeted email campaigns. To minimize potential negative reactions from uninterested recipients, the company intends to send promotional emails exclusively to users who are likely to be interested. VCUTech's the data scientist team has developed two simple tree-based prediction model as follows: Model 1: Model 2: START 1. Is the user a student? Y 2. Is the user majoring CS or IS? Y Send the email START N N 3. Is the user a junior or sophomore? N Do not send the email Model 2: START 1. Is the user a student? Y 2. Is the user a junior or sophomore? Y Send the email N N Page 3 of 6 Y 3. Is the user majoring CS or IS? N Do not send the email As the senior software developer at VCUTech, you are responsible for evaluating the two prediction models using test data. This test data, collected from a pilot survey, comprises responses from 10 users to three questions in the decision tree, as well as their interest in the laptop product that VCUTech aims to promote. Q. Write the console program using C# program language. The responses and interests of 10 users in the products are stored in ten array variables as follows: int [] user1 = new int [] {1, 1, 1, 0}; int [] user2 = new int [] {0, 0, 1, 1); int [] user3 = new int [] {1, 0, 1, 0}; int [] user4 = new int [] {1, 1, 0, 1}; int [] user5 = new int [] {1, 1, 1, 1}; int [] user6 = new int [] {0, 0, 0, 0}; int [] user7 = new int [] {1, 1, 1, 1}; int [] user8 = new int [] {0, 0, 0, 1}; int [] user9= new int [] {1, 0, 1, 1}; int [] user10=new int [] {0, 1, 1, 1}; In each user array, the first three values represent the responses to the three questions in the decision tree (0 for No and 1 for Yes), while the last value indicates the user's interest in the product (0 for not interested and 1 for interested). For example, userl is a student majoring in CS or IS, is a junior or sophomore, and is not interested in the product. For each of the ten users, your program is tasked with determining whether to send the promotional email or not under each model. A model makes a correct prediction if one of the following conditions is met: (i) (ii) Otherwise, the model makes an incorrect prediction. The model decides to send the email to the user, and the user is interested in the product; The model decides not to send the email to the user, and the user is not interested in the product. At the end, your program must print out (i) the prediction accuracy for each model, which is calculated as: # of correct predictions # of predictions prediction accuracy = * 100 and print out the model with higher accuracy. Make certain that your solution is highly modular, utilizing multiple classes and methods for enhanced structure and readability. Please follow the standard naming conventions for variables, methods, classes, and objects. Use meaningful names for variables, methods, classes, and objects. Use proper indentation and space to ensure that your code is readable. Avoid using properties (you will earn 0 point if you if use properties). For additional information on properties, refer to the final pages of the class slides for the "Classes and Objects" chapter.