IN SCALA PLEASECOULD YOU COMPLETE THE CODE OF FUNCTIONS: get_csv_url, process_ratings, process_movies, groupById, favourites, suggestions and recommendationsimport io.Sourceimport scala.util._// (1) Implement the function get_csv_url which takes an url-string// as argument and requests the corresponding file. The two urls// of interest are ratings_url and movies_url, which correspond// to CSV-files.//// The function should ReTurn the CSV-file appropriately broken// up into lines, and the first line should be dropped (that is without// the header of the CSV-file). The result is a list of strings (lines// in the file).def get_csv_url(url: String) : List[String] = ???val ratings_url = """https://nms.kcl.ac.uk/christian.urban/ratings.csv"""val movies_url = """https://nms.kcl.ac.uk/christian.urban/movies.csv"""// testcases//-----------//://val movies = get_csv_url(movies_url)//ratings.length // 87313//movies.length // 9742// (2) Implement two functions that process the CSV-files from (1). The ratings// function filters out all ratings below 4 and ReTurns a list of// (userID, movieID) pairs. The movies function just ReTurns a list// of (movieID, title) pairs. Note the input to these functions, that is// the argument lines, will be the output of the function get_csv_url.def process_ratings(lines: List[String]) : List[(String, String)] = ???def process_movies(lines: List[String]) : List[(String, String)] = ???// testcases//-----------//val good_ratings = process_ratings(ratings)//val movie_names = process_movies(movies)//good_ratings.length //48580//movie_names.length // 9742// (3) Implement a grouping function that calculates a Map// containing the userIDs and all the corresponding recommendations// (list of movieIDs). This should be implemented in a tail// recursive fashion, using a Map m as accumulator. This Map m// is set to Map() at the beginning of the calculation.def groupById(ratings: List[(String, String)],m: Map[String, List[String]]) : Map[String, List[String]] = ???// testcases//-----------//val ratings_map = groupById(good_ratings, Map())//val movies_map = movie_names.toMap//ratings_map.get("414").get.map(movies_map.get(_))// = most prolific recommender with 1227 positive ratings//ratings_map.get("474").get.map(movies_map.get(_))// = second-most prolific recommender with 787 positive ratings//ratings_map.get("214").get.map(movies_map.get(_))// = least prolific recommender with only 1 positive rating// (4) Implement a function that takes a ratings map and a movie_name as argument.// The function calculates all suggestions containing// the movie in its recommendations. It ReTurns a list of all these// recommendations (each of them is a list and needs to have the movie deleted,// otherwise it might happen we recommend the same movie).def favourites(m: Map[String, List[String]], mov: String) : List[List[String]] = ???// testcases//-----------// movie ID "912" - Casablanca (1942)// "858" - Godfather// "260" - Star Wars: Episode IV - A New Hope (1977)//favourites(ratings_map, "912").length // = 80// That means there are 80 users that recommend the movie with ID 912.// Of these 80 users, 55 gave a good rating to movie 858 and// 52 a good rating to movies 260, 318, 593.// (5) Implement a suggestions function which takes a rating// map and a movie_name as arguments. It calculates all the recommended// movies sorted according to the most frequently suggested movie(s) first.def suggestions(recs: Map[String, List[String]],mov_name: String) : List[String] = ???// testcases//-----------//suggestions(ratings_map, "912")//suggestions(ratings_map, "912").length// = 4110 suggestions with List(858, 260, 318, 593, ...)// being the most frequently suggested movies// (6) Implement a recommendations function which generates at most// *two* of the most frequently suggested movies. It ReTurns the// actual movie names, not the movieIDs.def recommendations(recs: Map[String, List[String]],movs: Map[String, String],mov_name: String) : List[String] = ???// testcases//-----------// recommendations(ratings_map, movies_map, "912")// = List(Godfather, Star Wars: Episode IV - A NewHope (1977))//recommendations(ratings_map, movies_map, "260")// = List(Star Wars: Episode V - The Empire Strikes Back (1980),// Star Wars: Episode VI - Return of the Jedi (1983))// recommendations(ratings_map, movies_map, "2")// = List(Lion King, Jurassic Park (1993))// recommendations(ratings_map, movies_map, "0")// = Nil// recommendations(ratings_map, movies_map, "1")// = List(Shawshank Redemption, Forrest Gump (1994))// recommendations(ratings_map, movies_map, "4")// = Nil (there are three ratings for this movie in ratings.csv but they are not positive)

Question

IN SCALA PLEASECOULD YOU COMPLETE THE CODE OF FUNCTIONS: get_csv_url, process_ratings, process_movies, groupById, favourites, suggestions and recommendationsimport io.Sourceimport scala.util._// (1) Implement the function get_csv_url which takes an url-string// as argument and requests the corresponding file. The two urls// of interest are ratings_url and movies_url, which correspond// to CSV-files.//// The function should ReTurn the CSV-file appropriately broken// up into lines, and the first line should be dropped (that is without// the header of the CSV-file). The result is a list of strings (lines// in the file).def get_csv_url(url: String) : List[String] = ???val ratings_url = &#34;&#34;&#34;https://nms.kcl.ac.uk/christian.urban/ratings.csv&#34;&#34;&#34;val movies_url = &#34;&#34;&#34;https://nms.kcl.ac.uk/christian.urban/movies.csv&#34;&#34;&#34;// testcases//-----------//://val movies = get_csv_url(movies_url)//ratings.length // 87313//movies.length // 9742// (2) Implement two functions that process the CSV-files from (1). The ratings// function filters out all ratings below 4 and ReTurns a list of// (userID, movieID) pairs. The movies function just ReTurns a list// of (movieID, title) pairs. Note the input to these functions, that is// the argument lines, will be the output of the function get_csv_url.def process_ratings(lines: List[String]) : List[(String, String)] = ???def process_movies(lines: List[String]) : List[(String, String)] = ???// testcases//-----------//val good_ratings = process_ratings(ratings)//val movie_names = process_movies(movies)//good_ratings.length //48580//movie_names.length // 9742// (3) Implement a grouping function that calculates a Map// containing the userIDs and all the corresponding recommendations// (list of movieIDs). This should be implemented in a tail// recursive fashion, using a Map m as accumulator. This Map m// is set to Map() at the beginning of the calculation.def groupById(ratings: List[(String, String)],m: Map[String, List[String]]) : Map[String, List[String]] = ???// testcases//-----------//val ratings_map = groupById(good_ratings, Map())//val movies_map = movie_names.toMap//ratings_map.get(&#34;414&#34;).get.map(movies_map.get(_))// => most prolific recommender with 1227 positive ratings//ratings_map.get(&#34;474&#34;).get.map(movies_map.get(_))// => second-most prolific recommender with 787 positive ratings//ratings_map.get(&#34;214&#34;).get.map(movies_map.get(_))// => least prolific recommender with only 1 positive rating// (4) Implement a function that takes a ratings map and a movie_name as argument.// The function calculates all suggestions containing// the movie in its recommendations. It ReTurns a list of all these// recommendations (each of them is a list and needs to have the movie deleted,// otherwise it might happen we recommend the same movie).def favourites(m: Map[String, List[String]], mov: String) : List[List[String]] = ???// testcases//-----------// movie ID &#34;912&#34; -> Casablanca (1942)// &#34;858&#34; -> Godfather// &#34;260&#34; -> Star Wars: Episode IV - A New Hope (1977)//favourites(ratings_map, &#34;912&#34;).length // => 80// That means there are 80 users that recommend the movie with ID 912.// Of these 80 users, 55 gave a good rating to movie 858 and// 52 a good rating to movies 260, 318, 593.// (5) Implement a suggestions function which takes a rating// map and a movie_name as arguments. It calculates all the recommended// movies sorted according to the most frequently suggested movie(s) first.def suggestions(recs: Map[String, List[String]],mov_name: String) : List[String] = ???// testcases//-----------//suggestions(ratings_map, &#34;912&#34;)//suggestions(ratings_map, &#34;912&#34;).length// => 4110 suggestions with List(858, 260, 318, 593, ...)// being the most frequently suggested movies// (6) Implement a recommendations function which generates at most// *two* of the most frequently suggested movies. It ReTurns the// actual movie names, not the movieIDs.def recommendations(recs: Map[String, List[String]],movs: Map[String, String],mov_name: String) : List[String] = ???// testcases//-----------// recommendations(ratings_map, movies_map, &#34;912&#34;)// => List(Godfather, Star Wars: Episode IV - A NewHope (1977))//recommendations(ratings_map, movies_map, &#34;260&#34;)// => List(Star Wars: Episode V - The Empire Strikes Back (1980),// Star Wars: Episode VI - Return of the Jedi (1983))// recommendations(ratings_map, movies_map, &#34;2&#34;)// => List(Lion King, Jurassic Park (1993))// recommendations(ratings_map, movies_map, &#34;0&#34;)// => Nil// recommendations(ratings_map, movies_map, &#34;1&#34;)// => List(Shawshank Redemption, Forrest Gump (1994))// recommendations(ratings_map, movies_map, &#34;4&#34;)// => Nil (there are three ratings for this movie in ratings.csv but they are not positive)

Accepted Answer

Coded using Scala.