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GoMovies is a perfect site to stream thousands of movies and TV series.
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It also has a premium membership option for an ad-free movie streaming experience. In addition, titles are categorized in genres and years, allowing you to find the preferred one quickly. It comes with a well-designed and intuitive interface, enabling you to find your preferred movies or TV shows easily. XMovies is another great website with an enormous collection of the latest and popular movies. Along with that, you can rely on Stream2Watch to watch sports, cartoons, news, etc. It's a perfect website if you want to catch the live stream or TV premiere of your favorite movie. You can watch almost all the popular TV channels from the UK, USA, Canada, France, and other countries. Stream2Watch is basically a streaming website that enables you to live stream the TV channels. You can also find the preferred title quickly by utilizing the built-in search engine. The site is very user-friendly and easy to use. It has a massive collection of titles under various categories and genres.
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I am happy to hear any feedback and questions.123Movies is one of the most widespread sites for streaming movies.
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Source code that created this post can be found here.
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I hope movie will be the same after I learn how to analyze movie data. The more faces in a movie poster, the lower the score will be.
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This result is not bad, the results of the test set are not far from the results of the training set. 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.