Objective 1: Bayesian Average Your frst objective in this project is to compute the Bayesian average for a given list of ratings. The Bayesian average is computed by adding a fixed number of extra ratings at a certain value to he exising ratings Background By comparing items based on their Bayesian average, as opposed to directly using the average of all review scores, we can improve the usefulness of these comparisons. For example, in a 5 star rating system an nem with 1 rating of 5 stars w be ranked higher than an item with 200 ratings at an average rang of 4.9 Most likely we would prefer the later item since we can be more confident that it is a highly rated item since there are 200 opinions being considered. To address this we can rank he inems by their Bayesian rating and choose 5 extra ratings of 3 stars to be added to each nem Now the first item has 1 rating of 5 and 5 ratings of 3 for a Bayesian average of 3.33 and the second Rem has 200 raings at an average of 4.9 and 5 raings of 3 for a Bayesian average of 4.85. Bayesian averages can be used in this way to lower ratings where there is not enough information to make a judgment on the Rem Tasks Setup the structure for this project and wite a method to compute the Bayesian average of a list of ratings 1. Create a new project in Eclipse that wil contain you work for al 5 objectives 2. Create a new package in the sre foider named "ratings 3 In the ratings package, create a new class named "Main" and write a main method in this class. This dass will be the entry point for the project and will be the only class with a man method. You should use his class to test your project 4. Create a class named "RatingsAnalyzer" in the ratings package. In his new class, write a public static method named "bayesianAverage" that takes three parameters (an ArrayList of Doubles, an int, then a double) and returns a double. The first parameter represents all the ratings for a particular item, the second parameter is the number of addtional ratings to add, and the third parameter is the value of these adcitional ratings. The refun value is the bayesian average of these ratings based on these parameters