test_corr <- round ( cor ( test_sample $ pred_score, test_sample $ imdb_score ), 2 ) test_rmse <- round ( sqrt ( mean (( test_sample $ pred_score - test_sample $ imdb_score ) ^ 2 ))) test_mae <- round ( mean ( abs ( test_sample $ pred_score - test_sample $ imdb_score ))) c ( test_corr ^ 2, test_rmse, test_mae ) # 0.1521 1.0000 1.0000 However, on average, on the set of the observations I have previously seen, I am going to make 1 score difference when estimating.Ĭheck how good the model is on the test set. The correlation between predicted score and actual score for the training set is 14.44%, which is very close to theoretical R-Squared for the model, this is good news. train_corr <- round ( cor ( train_sample $ pred_score, train_sample $ imdb_score ), 2 ) train_rmse <- round ( sqrt ( mean (( train_sample $ pred_score - train_sample $ imdb_score ) ^ 2 ))) train_mae <- round ( mean ( abs ( train_sample $ pred_score - train_sample $ imdb_score ))) c ( train_corr ^ 2, train_rmse, train_mae ) # 0.1444 1.0000 1.0000 #F-statistic: 95.9 on DF, p-value: <0.0000000000000002Ĭheck how good the model is on the training set.
#Residual standard error: 1.04 on 4026 degrees of freedom library ( ggplot2 ) library ( dplyr ) library ( Hmisc ) library ( psych ) movie |t|) Looking at the variables, I think I might be able to find something interesting. When I was browsing Kaggle dataset, I came across an IMDB movie dataset which contains 5043 movies and 28 variables.
I have to admit that we miss good movies sometimes because some critics reviews are controversial, another time we regret after watching a movie because it was not what we expected. We are movie-goers, we have heavily relied on how many gold stars a movie gets before we decide whether we watch it or not.
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