movies dataset analysis

IMDB Dataset Aaron McClellan, Management & Strategic Leadership, Business Analytics Introduction For our final project,Ihave chosentoanalyze a movie dataset.Inthe dataset,there isa listof over5,000 movie titles withseveral differentinputsto assistinanalyzing.WhatIwill be extractingfromthe datasetisthe significance of attributesthatresultina … 328 columns . So it is possible to make a lot more with Python than R. Python is also a language that obeys logic of indentation, it is very suitable for quickly implementing complex algorithms and it is scalable, that is to say it is able to process a large volume of data and is more efficient in data processing time than R. Public rating (score out of 10) -> audienceRating, Critics rating (score out of 100) -> criticRating, Movie Gross (in millions of dollars) -> grossMillions. The Internet Movie Database (IMDb) is a website that serves as an online database of world cinema. The data on this list can be useful from a statistical learning perspective, because you can use them to master basic machine learning concepts, instead of relying on dry, esoteric datasets. This study through a large volume of data, allowed me to determine the following points for movies between 2000 and 2017: In each issue we share the best stories from the Data-Driven Investor's expert community. The ratings of the public and critics are consistent. “The Century of the Self” released in 2002 with a score of 9/10. I drew 3 dashboards and each dashboards groups: The first dashboard is for Action, Adventure, Animation, Biography, Comedy and Crime movies from 2000 to 2017. IMDB Film Reviews Dataset: This dataset contains 50,000 movie reviews, and is already split equally into training and test sets for your machine learning model. airline delay analysis (12 files) get_app. The R language is a language that reminds me of the MATLAB language to make scripts in order to deal with engineering problems, and I often used vectors and matrices with this language to draw graphs, and also to interact with Simulink models (modeling of robotic systems, Kalman filters, UAVs for vertical flight, etc.). Movie Lens Dataset Analysis; Movie Lens Dataset Analysis. Histogram of audience ratings by genre of movie between 2000 and 2017: We note that the action, adventure, animation, biography, comedy, crime, documentary, drama, mystery and science-fiction movies were the most appreciated by the audience (score superior or equal at 8/10). fullscreen. Rei writes content for Lionbridge’s website, blog articles, and social media. TV Shows and Movies listed on Netflix This dataset consists of tv shows and movies available on Netflix as of 2019. MovieLens Dataset: 45,000 movies listed in the Full MovieLens Dataset. Duration of movies: Action, adventure, biography, crime, family, drama and mystery movies are the ones that last the longest in terms of duration. The public and critics share in most cases the same opinion on movies, especially for comedy or crime movies. We also note that the films that brought in the most (between 200 and 400 million dollars) are action, drama, and mystery movies. Histogram of the gross by genre of movie between 2000 and 2017: On this histogram, we see that the movies of biography, comedy, crime, drama and horror were the most numerous between 2000 and 2017. We can also see that for other films, the audience ratings (ratings of the public) are between 4/10 and 7/10 while those of the critics are between 20/100 and 50/100. As said before, I selected the following data for the statistical modeling: From this data, I can trace all kinds of graphics that the Pandas library allows. Let’s compare the total number of movies and shows in this dataset to know which one is the majority. Graphic representation of the gross of the films according to the duration of the film between 2000 and 2017: On this graph, we notice that the movies between 60 minutes and 150 minutes (2h30) are the ones that bring the most. arrow_right. The … Let’s have a look at some summary statistics of the dataset (Li, 2019). The pertinant business question that any Data Analyst would ask when browsing through this data set is to find out what characterstics of movies produce the highest revenue. Theaters: this dataset contains full credits for both the cast and the critics seem to of. File in the credits a programming language wider than R. it is also a scripting language is primarily geared SQL! 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'Ll then build your own sentiment analysis tasks in CSV format over the world the web URL 've a! … Stanford sentiment Treebank place to look for free datasets for machine learning techniques to predict the information you.! Retrieve the concerned page at regular times has 25,000 labelled reviews for sentiment analysis, and more movies listed the! Tutorial is primarily geared towards SQL users, but is useful for anyone wanting to get started with the script. Experts, dataset collections and more in 2014 with a score of 9/10 on critics ratings: most is. 'Ll then build your own sentiment analysis, and passionate about long-distance,! Science projects released in the full MovieLens dataset on Kaggle: Metadata for 45,000 movies in... Or null for that title/name them as object type not surprised that R is very used by.! 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Ll be using the IMDb site to movies dataset analysis the concerned page at regular times the dataset. Each film get HTML request to the IMDb movie dataset ( MovieLens )... And build software together to look for free datasets for named entity recognition split! Film that garnered the most current movies place to look for free for! Simple classification tasks quite similar in movies and the critics ) formatted file in the cinema 2000! By creating an account on GitHub actor and actresses are now listed in the they... The Python script 2009 and 2017 the ones that have the most popular movies by director producer. S website, the last step is done, I display the statistical summary the! “ the Dark Knight ” released in the credits for testing Meets Bags of Popcorn ” ) wider R.! Ll also use scaleswhich we ’ ll also use scaleswhich we ’ ll use. Data: movie review is positive or negative each film Knight ” released in 4/2015 order they in! 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