For this project, the recommender system I would like to put into effevt would be one that would recommend Films, more specifically the Star Wars Skywalker Film Franchise.
This is an industry, where the Ratings or Cristics Reviews for a specific Film could make or break a companies fiscal year. Therefore, there is an abundancy of data to be collected on these various Films.
First, I wanted to do was created my own smaller Data-Set of the Star Wars Films using a simple scaling metric to interpert and the same Critics spanning from 1977-2019. Therefore, I decided to use the website Meta Critic.
Since this website includes various Critics that use the same numerical ratings system of a 0-100 scale. In addition, this gave me the opportunity to scrap real Reviews from the time of the Film’s release, instead of using arbitrary values.
star_wars_data <- read.csv("https://raw.githubusercontent.com/josephsimone/Data-612/master/project_1/star_wars_critic_ratings.csv")
colnames(star_wars_data) <- gsub("ï..Critics", "Critics", colnames(star_wars_data))
star_wars_data## Critics The.Phantom.Menace Attack.of.the.Clones
## 1 Chicago Tribune 88 100
## 2 Entertainment Weekly 67 88
## 3 The New York Times 80 40
## 4 The Globe and Mail 63 38
## 5 The Washington Post 40 NA
## 6 San Fransico 50 50
## 7 Reel Views 88 83
## 8 Daily News 63 91
## 9 Los Angeles Times NA 50
## Revenge.of.the.Sith A.New.Hope Empire.Strikes.Back Return.of.the.Jedi
## 1 100 88 100 100
## 2 75 NA 100 83
## 3 93 80 NA 20
## 4 50 100 50 NA
## 5 90 100 60 70
## 6 50 100 100 75
## 7 NA 100 80 75
## 8 90 100 90 100
## 9 70 100 100 90
## The.Force.Awakens The.Last.Jedi Rise.of.Skywalker
## 1 75 NA 75
## 2 83 83 50
## 3 90 90 50
## 4 63 50 50
## 5 75 75 50
## 6 NA 75 50
## 7 75 75 50
## 8 80 90 NA
## 9 60 100 50In this seection, the Data-Frame was first converted to long format.
Then split into training and testing sets based on 0.75 split ratio.
## Critics Film Review
## 1 Chicago Tribune The.Phantom.Menace 88
## 2 Entertainment Weekly The.Phantom.Menace 67
## 3 The New York Times The.Phantom.Menace 80
## 4 The Globe and Mail The.Phantom.Menace 63
## 5 The Washington Post The.Phantom.Menace 40
## 6 San Fransico The.Phantom.Menace 50## Critics Film Review
## 1 Chicago Tribune The.Phantom.Menace NA
## 2 Entertainment Weekly The.Phantom.Menace NA
## 3 The New York Times The.Phantom.Menace NA
## 4 The Globe and Mail The.Phantom.Menace NA
## 5 The Washington Post The.Phantom.Menace NA
## 6 San Fransico The.Phantom.Menace NASince, there are now two different dataset randomly selected, it is time to move onto the RMSE calculations.
raw_avg <- sum(train_data_set$Review, na.rm = TRUE) / length(which(!is.na(train_data_set$Review)))
rmse_raw_train <- sqrt(sum((train_data_set$Review[!is.na(train_data_set$Review)] - raw_avg)^2) /
length(which(!is.na(train_data_set$Review))))
rmse_raw_train## [1] 20.39405rmse_raw_test <- sqrt(sum((test_data_set$Review[!is.na(test_data_set$Review)] - raw_avg)^2) /
length(which(!is.na(test_data_set$Review))))
rmse_raw_test## [1] 19.18502We can observe that RMSE values are significantly larger than expected in a smaller sample space.
Critics_bias <- train_data_set %>% filter(!is.na(Review)) %>%
group_by(Critics) %>%
summarise(sum = sum(Review), count = n()) %>%
mutate(bias = sum/count-raw_avg) %>%
select(Critics, CriticsBias = bias)
CriticsBias<-Critics_bias$CriticsBiasFilm_bias <- train_data_set %>% filter(!is.na(Review)) %>%
group_by(Film) %>%
summarise(sum = sum(Review), count = n()) %>%
mutate(bias = sum/count-raw_avg) %>%
select(Film, FilmBias = bias)
FilmBias<-Film_bias$FilmBiastrain_data_set <- train_data_set %>% left_join(Critics_bias, by = "Critics") %>%
left_join(Film_bias, by = "Film") %>%
mutate(RawAvg = raw_avg) %>%
mutate(Baseline = RawAvg + CriticsBias + FilmBias)
train_data_set## Critics Film Review CriticsBias
## 1 Chicago Tribune The.Phantom.Menace 88 16.5357143
## 2 Entertainment Weekly The.Phantom.Menace 67 -0.4071429
## 3 The New York Times The.Phantom.Menace 80 -5.7738095
## 4 The Globe and Mail The.Phantom.Menace 63 -15.4642857
## 5 The Washington Post The.Phantom.Menace 40 -4.6071429
## 6 San Fransico The.Phantom.Menace 50 -3.7738095
## 7 Reel Views The.Phantom.Menace 88 0.5357143
## 8 Daily News The.Phantom.Menace NA 15.6428571
## 9 Los Angeles Times The.Phantom.Menace NA 3.7261905
## 10 Chicago Tribune Attack.of.the.Clones 100 16.5357143
## 11 Entertainment Weekly Attack.of.the.Clones 88 -0.4071429
## 12 The New York Times Attack.of.the.Clones 40 -5.7738095
## 13 The Globe and Mail Attack.of.the.Clones 38 -15.4642857
## 14 The Washington Post Attack.of.the.Clones NA -4.6071429
## 15 San Fransico Attack.of.the.Clones NA -3.7738095
## 16 Reel Views Attack.of.the.Clones 83 0.5357143
## 17 Daily News Attack.of.the.Clones 91 15.6428571
## 18 Los Angeles Times Attack.of.the.Clones NA 3.7261905
## 19 Chicago Tribune Revenge.of.the.Sith 100 16.5357143
## 20 Entertainment Weekly Revenge.of.the.Sith NA -0.4071429
## 21 The New York Times Revenge.of.the.Sith 93 -5.7738095
## 22 The Globe and Mail Revenge.of.the.Sith 50 -15.4642857
## 23 The Washington Post Revenge.of.the.Sith 90 -4.6071429
## 24 San Fransico Revenge.of.the.Sith 50 -3.7738095
## 25 Reel Views Revenge.of.the.Sith NA 0.5357143
## 26 Daily News Revenge.of.the.Sith NA 15.6428571
## 27 Los Angeles Times Revenge.of.the.Sith 70 3.7261905
## 28 Chicago Tribune A.New.Hope NA 16.5357143
## 29 Entertainment Weekly A.New.Hope NA -0.4071429
## 30 The New York Times A.New.Hope NA -5.7738095
## 31 The Globe and Mail A.New.Hope 100 -15.4642857
## 32 The Washington Post A.New.Hope 100 -4.6071429
## 33 San Fransico A.New.Hope 100 -3.7738095
## 34 Reel Views A.New.Hope NA 0.5357143
## 35 Daily News A.New.Hope 100 15.6428571
## 36 Los Angeles Times A.New.Hope NA 3.7261905
## 37 Chicago Tribune Empire.Strikes.Back 100 16.5357143
## 38 Entertainment Weekly Empire.Strikes.Back NA -0.4071429
## 39 The New York Times Empire.Strikes.Back NA -5.7738095
## 40 The Globe and Mail Empire.Strikes.Back 50 -15.4642857
## 41 The Washington Post Empire.Strikes.Back 60 -4.6071429
## 42 San Fransico Empire.Strikes.Back 100 -3.7738095
## 43 Reel Views Empire.Strikes.Back 80 0.5357143
## 44 Daily News Empire.Strikes.Back 90 15.6428571
## 45 Los Angeles Times Empire.Strikes.Back 100 3.7261905
## 46 Chicago Tribune Return.of.the.Jedi 100 16.5357143
## 47 Entertainment Weekly Return.of.the.Jedi 83 -0.4071429
## 48 The New York Times Return.of.the.Jedi 20 -5.7738095
## 49 The Globe and Mail Return.of.the.Jedi NA -15.4642857
## 50 The Washington Post Return.of.the.Jedi 70 -4.6071429
## 51 San Fransico Return.of.the.Jedi NA -3.7738095
## 52 Reel Views Return.of.the.Jedi 75 0.5357143
## 53 Daily News Return.of.the.Jedi NA 15.6428571
## 54 Los Angeles Times Return.of.the.Jedi 90 3.7261905
## 55 Chicago Tribune The.Force.Awakens 75 16.5357143
## 56 Entertainment Weekly The.Force.Awakens NA -0.4071429
## 57 The New York Times The.Force.Awakens 90 -5.7738095
## 58 The Globe and Mail The.Force.Awakens 63 -15.4642857
## 59 The Washington Post The.Force.Awakens 75 -4.6071429
## 60 San Fransico The.Force.Awakens NA -3.7738095
## 61 Reel Views The.Force.Awakens 75 0.5357143
## 62 Daily News The.Force.Awakens 80 15.6428571
## 63 Los Angeles Times The.Force.Awakens 60 3.7261905
## 64 Chicago Tribune The.Last.Jedi NA 16.5357143
## 65 Entertainment Weekly The.Last.Jedi 83 -0.4071429
## 66 The New York Times The.Last.Jedi 90 -5.7738095
## 67 The Globe and Mail The.Last.Jedi 50 -15.4642857
## 68 The Washington Post The.Last.Jedi 75 -4.6071429
## 69 San Fransico The.Last.Jedi 75 -3.7738095
## 70 Reel Views The.Last.Jedi 75 0.5357143
## 71 Daily News The.Last.Jedi NA 15.6428571
## 72 Los Angeles Times The.Last.Jedi 100 3.7261905
## 73 Chicago Tribune Rise.of.Skywalker 75 16.5357143
## 74 Entertainment Weekly Rise.of.Skywalker 50 -0.4071429
## 75 The New York Times Rise.of.Skywalker NA -5.7738095
## 76 The Globe and Mail Rise.of.Skywalker NA -15.4642857
## 77 The Washington Post Rise.of.Skywalker 50 -4.6071429
## 78 San Fransico Rise.of.Skywalker 50 -3.7738095
## 79 Reel Views Rise.of.Skywalker 50 0.5357143
## 80 Daily News Rise.of.Skywalker NA 15.6428571
## 81 Los Angeles Times Rise.of.Skywalker 50 3.7261905
## FilmBias RawAvg Baseline
## 1 -6.6071429 74.60714 84.53571
## 2 -6.6071429 74.60714 67.59286
## 3 -6.6071429 74.60714 62.22619
## 4 -6.6071429 74.60714 52.53571
## 5 -6.6071429 74.60714 63.39286
## 6 -6.6071429 74.60714 64.22619
## 7 -6.6071429 74.60714 68.53571
## 8 -6.6071429 74.60714 83.64286
## 9 -6.6071429 74.60714 71.72619
## 10 -1.2738095 74.60714 89.86905
## 11 -1.2738095 74.60714 72.92619
## 12 -1.2738095 74.60714 67.55952
## 13 -1.2738095 74.60714 57.86905
## 14 -1.2738095 74.60714 68.72619
## 15 -1.2738095 74.60714 69.55952
## 16 -1.2738095 74.60714 73.86905
## 17 -1.2738095 74.60714 88.97619
## 18 -1.2738095 74.60714 77.05952
## 19 0.8928571 74.60714 92.03571
## 20 0.8928571 74.60714 75.09286
## 21 0.8928571 74.60714 69.72619
## 22 0.8928571 74.60714 60.03571
## 23 0.8928571 74.60714 70.89286
## 24 0.8928571 74.60714 71.72619
## 25 0.8928571 74.60714 76.03571
## 26 0.8928571 74.60714 91.14286
## 27 0.8928571 74.60714 79.22619
## 28 25.3928571 74.60714 116.53571
## 29 25.3928571 74.60714 99.59286
## 30 25.3928571 74.60714 94.22619
## 31 25.3928571 74.60714 84.53571
## 32 25.3928571 74.60714 95.39286
## 33 25.3928571 74.60714 96.22619
## 34 25.3928571 74.60714 100.53571
## 35 25.3928571 74.60714 115.64286
## 36 25.3928571 74.60714 103.72619
## 37 8.2500000 74.60714 99.39286
## 38 8.2500000 74.60714 82.45000
## 39 8.2500000 74.60714 77.08333
## 40 8.2500000 74.60714 67.39286
## 41 8.2500000 74.60714 78.25000
## 42 8.2500000 74.60714 79.08333
## 43 8.2500000 74.60714 83.39286
## 44 8.2500000 74.60714 98.50000
## 45 8.2500000 74.60714 86.58333
## 46 -1.6071429 74.60714 89.53571
## 47 -1.6071429 74.60714 72.59286
## 48 -1.6071429 74.60714 67.22619
## 49 -1.6071429 74.60714 57.53571
## 50 -1.6071429 74.60714 68.39286
## 51 -1.6071429 74.60714 69.22619
## 52 -1.6071429 74.60714 73.53571
## 53 -1.6071429 74.60714 88.64286
## 54 -1.6071429 74.60714 76.72619
## 55 -0.6071429 74.60714 90.53571
## 56 -0.6071429 74.60714 73.59286
## 57 -0.6071429 74.60714 68.22619
## 58 -0.6071429 74.60714 58.53571
## 59 -0.6071429 74.60714 69.39286
## 60 -0.6071429 74.60714 70.22619
## 61 -0.6071429 74.60714 74.53571
## 62 -0.6071429 74.60714 89.64286
## 63 -0.6071429 74.60714 77.72619
## 64 3.6785714 74.60714 94.82143
## 65 3.6785714 74.60714 77.87857
## 66 3.6785714 74.60714 72.51190
## 67 3.6785714 74.60714 62.82143
## 68 3.6785714 74.60714 73.67857
## 69 3.6785714 74.60714 74.51190
## 70 3.6785714 74.60714 78.82143
## 71 3.6785714 74.60714 93.92857
## 72 3.6785714 74.60714 82.01190
## 73 -20.4404762 74.60714 70.70238
## 74 -20.4404762 74.60714 53.75952
## 75 -20.4404762 74.60714 48.39286
## 76 -20.4404762 74.60714 38.70238
## 77 -20.4404762 74.60714 49.55952
## 78 -20.4404762 74.60714 50.39286
## 79 -20.4404762 74.60714 54.70238
## 80 -20.4404762 74.60714 69.80952
## 81 -20.4404762 74.60714 57.89286test_data_set <- test_data_set %>% left_join(Critics_bias, by = "Critics") %>%
left_join(Film_bias, by = "Film") %>%
mutate(RawAvg = raw_avg) %>%
mutate(Baseline = RawAvg + CriticsBias + FilmBias)
test_data_set## Critics Film Review CriticsBias
## 1 Chicago Tribune The.Phantom.Menace NA 16.5357143
## 2 Entertainment Weekly The.Phantom.Menace NA -0.4071429
## 3 The New York Times The.Phantom.Menace NA -5.7738095
## 4 The Globe and Mail The.Phantom.Menace NA -15.4642857
## 5 The Washington Post The.Phantom.Menace NA -4.6071429
## 6 San Fransico The.Phantom.Menace NA -3.7738095
## 7 Reel Views The.Phantom.Menace NA 0.5357143
## 8 Daily News The.Phantom.Menace 63 15.6428571
## 9 Los Angeles Times The.Phantom.Menace NA 3.7261905
## 10 Chicago Tribune Attack.of.the.Clones NA 16.5357143
## 11 Entertainment Weekly Attack.of.the.Clones NA -0.4071429
## 12 The New York Times Attack.of.the.Clones NA -5.7738095
## 13 The Globe and Mail Attack.of.the.Clones NA -15.4642857
## 14 The Washington Post Attack.of.the.Clones NA -4.6071429
## 15 San Fransico Attack.of.the.Clones 50 -3.7738095
## 16 Reel Views Attack.of.the.Clones NA 0.5357143
## 17 Daily News Attack.of.the.Clones NA 15.6428571
## 18 Los Angeles Times Attack.of.the.Clones 50 3.7261905
## 19 Chicago Tribune Revenge.of.the.Sith NA 16.5357143
## 20 Entertainment Weekly Revenge.of.the.Sith 75 -0.4071429
## 21 The New York Times Revenge.of.the.Sith NA -5.7738095
## 22 The Globe and Mail Revenge.of.the.Sith NA -15.4642857
## 23 The Washington Post Revenge.of.the.Sith NA -4.6071429
## 24 San Fransico Revenge.of.the.Sith NA -3.7738095
## 25 Reel Views Revenge.of.the.Sith NA 0.5357143
## 26 Daily News Revenge.of.the.Sith 90 15.6428571
## 27 Los Angeles Times Revenge.of.the.Sith NA 3.7261905
## 28 Chicago Tribune A.New.Hope 88 16.5357143
## 29 Entertainment Weekly A.New.Hope NA -0.4071429
## 30 The New York Times A.New.Hope 80 -5.7738095
## 31 The Globe and Mail A.New.Hope NA -15.4642857
## 32 The Washington Post A.New.Hope NA -4.6071429
## 33 San Fransico A.New.Hope NA -3.7738095
## 34 Reel Views A.New.Hope 100 0.5357143
## 35 Daily News A.New.Hope NA 15.6428571
## 36 Los Angeles Times A.New.Hope 100 3.7261905
## 37 Chicago Tribune Empire.Strikes.Back NA 16.5357143
## 38 Entertainment Weekly Empire.Strikes.Back 100 -0.4071429
## 39 The New York Times Empire.Strikes.Back NA -5.7738095
## 40 The Globe and Mail Empire.Strikes.Back NA -15.4642857
## 41 The Washington Post Empire.Strikes.Back NA -4.6071429
## 42 San Fransico Empire.Strikes.Back NA -3.7738095
## 43 Reel Views Empire.Strikes.Back NA 0.5357143
## 44 Daily News Empire.Strikes.Back NA 15.6428571
## 45 Los Angeles Times Empire.Strikes.Back NA 3.7261905
## 46 Chicago Tribune Return.of.the.Jedi NA 16.5357143
## 47 Entertainment Weekly Return.of.the.Jedi NA -0.4071429
## 48 The New York Times Return.of.the.Jedi NA -5.7738095
## 49 The Globe and Mail Return.of.the.Jedi NA -15.4642857
## 50 The Washington Post Return.of.the.Jedi NA -4.6071429
## 51 San Fransico Return.of.the.Jedi 75 -3.7738095
## 52 Reel Views Return.of.the.Jedi NA 0.5357143
## 53 Daily News Return.of.the.Jedi 100 15.6428571
## 54 Los Angeles Times Return.of.the.Jedi NA 3.7261905
## 55 Chicago Tribune The.Force.Awakens NA 16.5357143
## 56 Entertainment Weekly The.Force.Awakens 83 -0.4071429
## 57 The New York Times The.Force.Awakens NA -5.7738095
## 58 The Globe and Mail The.Force.Awakens NA -15.4642857
## 59 The Washington Post The.Force.Awakens NA -4.6071429
## 60 San Fransico The.Force.Awakens NA -3.7738095
## 61 Reel Views The.Force.Awakens NA 0.5357143
## 62 Daily News The.Force.Awakens NA 15.6428571
## 63 Los Angeles Times The.Force.Awakens NA 3.7261905
## 64 Chicago Tribune The.Last.Jedi NA 16.5357143
## 65 Entertainment Weekly The.Last.Jedi NA -0.4071429
## 66 The New York Times The.Last.Jedi NA -5.7738095
## 67 The Globe and Mail The.Last.Jedi NA -15.4642857
## 68 The Washington Post The.Last.Jedi NA -4.6071429
## 69 San Fransico The.Last.Jedi NA -3.7738095
## 70 Reel Views The.Last.Jedi NA 0.5357143
## 71 Daily News The.Last.Jedi 90 15.6428571
## 72 Los Angeles Times The.Last.Jedi NA 3.7261905
## 73 Chicago Tribune Rise.of.Skywalker NA 16.5357143
## 74 Entertainment Weekly Rise.of.Skywalker NA -0.4071429
## 75 The New York Times Rise.of.Skywalker 50 -5.7738095
## 76 The Globe and Mail Rise.of.Skywalker 50 -15.4642857
## 77 The Washington Post Rise.of.Skywalker NA -4.6071429
## 78 San Fransico Rise.of.Skywalker NA -3.7738095
## 79 Reel Views Rise.of.Skywalker NA 0.5357143
## 80 Daily News Rise.of.Skywalker NA 15.6428571
## 81 Los Angeles Times Rise.of.Skywalker NA 3.7261905
## FilmBias RawAvg Baseline
## 1 -6.6071429 74.60714 84.53571
## 2 -6.6071429 74.60714 67.59286
## 3 -6.6071429 74.60714 62.22619
## 4 -6.6071429 74.60714 52.53571
## 5 -6.6071429 74.60714 63.39286
## 6 -6.6071429 74.60714 64.22619
## 7 -6.6071429 74.60714 68.53571
## 8 -6.6071429 74.60714 83.64286
## 9 -6.6071429 74.60714 71.72619
## 10 -1.2738095 74.60714 89.86905
## 11 -1.2738095 74.60714 72.92619
## 12 -1.2738095 74.60714 67.55952
## 13 -1.2738095 74.60714 57.86905
## 14 -1.2738095 74.60714 68.72619
## 15 -1.2738095 74.60714 69.55952
## 16 -1.2738095 74.60714 73.86905
## 17 -1.2738095 74.60714 88.97619
## 18 -1.2738095 74.60714 77.05952
## 19 0.8928571 74.60714 92.03571
## 20 0.8928571 74.60714 75.09286
## 21 0.8928571 74.60714 69.72619
## 22 0.8928571 74.60714 60.03571
## 23 0.8928571 74.60714 70.89286
## 24 0.8928571 74.60714 71.72619
## 25 0.8928571 74.60714 76.03571
## 26 0.8928571 74.60714 91.14286
## 27 0.8928571 74.60714 79.22619
## 28 25.3928571 74.60714 116.53571
## 29 25.3928571 74.60714 99.59286
## 30 25.3928571 74.60714 94.22619
## 31 25.3928571 74.60714 84.53571
## 32 25.3928571 74.60714 95.39286
## 33 25.3928571 74.60714 96.22619
## 34 25.3928571 74.60714 100.53571
## 35 25.3928571 74.60714 115.64286
## 36 25.3928571 74.60714 103.72619
## 37 8.2500000 74.60714 99.39286
## 38 8.2500000 74.60714 82.45000
## 39 8.2500000 74.60714 77.08333
## 40 8.2500000 74.60714 67.39286
## 41 8.2500000 74.60714 78.25000
## 42 8.2500000 74.60714 79.08333
## 43 8.2500000 74.60714 83.39286
## 44 8.2500000 74.60714 98.50000
## 45 8.2500000 74.60714 86.58333
## 46 -1.6071429 74.60714 89.53571
## 47 -1.6071429 74.60714 72.59286
## 48 -1.6071429 74.60714 67.22619
## 49 -1.6071429 74.60714 57.53571
## 50 -1.6071429 74.60714 68.39286
## 51 -1.6071429 74.60714 69.22619
## 52 -1.6071429 74.60714 73.53571
## 53 -1.6071429 74.60714 88.64286
## 54 -1.6071429 74.60714 76.72619
## 55 -0.6071429 74.60714 90.53571
## 56 -0.6071429 74.60714 73.59286
## 57 -0.6071429 74.60714 68.22619
## 58 -0.6071429 74.60714 58.53571
## 59 -0.6071429 74.60714 69.39286
## 60 -0.6071429 74.60714 70.22619
## 61 -0.6071429 74.60714 74.53571
## 62 -0.6071429 74.60714 89.64286
## 63 -0.6071429 74.60714 77.72619
## 64 3.6785714 74.60714 94.82143
## 65 3.6785714 74.60714 77.87857
## 66 3.6785714 74.60714 72.51190
## 67 3.6785714 74.60714 62.82143
## 68 3.6785714 74.60714 73.67857
## 69 3.6785714 74.60714 74.51190
## 70 3.6785714 74.60714 78.82143
## 71 3.6785714 74.60714 93.92857
## 72 3.6785714 74.60714 82.01190
## 73 -20.4404762 74.60714 70.70238
## 74 -20.4404762 74.60714 53.75952
## 75 -20.4404762 74.60714 48.39286
## 76 -20.4404762 74.60714 38.70238
## 77 -20.4404762 74.60714 49.55952
## 78 -20.4404762 74.60714 50.39286
## 79 -20.4404762 74.60714 54.70238
## 80 -20.4404762 74.60714 69.80952
## 81 -20.4404762 74.60714 57.89286rmse_base_train <- sqrt(sum((train_data_set$Review[!is.na(train_data_set$Review)] -
train_data_set$Baseline[!is.na(train_data_set$Review)])^2) /
length(which(!is.na(train_data_set$Review))))
rmse_base_test <- sqrt(sum((test_data_set$Review[!is.na(test_data_set$Review)] -
test_data_set$Baseline[!is.na(test_data_set$Review)])^2) /
length(which(!is.na(test_data_set$Review))))The Largest Bias are determined by the NA Critic’s Reviews that did not review the Film.
## [1] 14.25514## [1] 14.31247The table below represents the RMSE values for both the Training and Testing Sets and the Raw Average and Baseline Predictors.
| RMSE | |
|---|---|
| Training: Raw Average | 20.39405 |
| Training: Baseline Predictor | 14.25514 |
| Testing: Raw Average | 19.18502 |
| Testing: Baseline Predictor | 14.31247 |
The RMSE values improved the Baseline Predictors in both the Training and Test Sets.
Even with a smaller Data-Set that included incomplete values, this was enough information to be able to visualize and apply specific Films and Critic bias into our model.