Once the data modeling is complete, the last step is to visualize the results and interpret them.In fact, the purpose of Data Scientist is primarily to make the data talk, to On the IMDb website, it is possible to filter the searches, and thus to display all the movies for one year, such as the year 2017. We are going to analyze a dataset from Netflix database to explore the characteristics that people share in movies’ taste, based on how they rate them.This dataset has two files, we will import both and work with both of them.We will want to find out how the structure of the dataset works and how many records do we have in each of these tables.We will start by considering a subset of users and discovering what are their favourite genre. To be able to use and visualize these two data Genre and Movie, I have to type them in category and I get:The two data Genre and Movie are therefore category type.Then, I display the statistical summary of the dataset with describe().With this summary, I have access to a lot of information about my dataset, such as number of rows, average data, standard deviation, minimum, maximum, and all three quartiles.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.I can visualize audience ratings (audienceRating) based on critics ratings on all movies released between 2000 and 2017.We see that there is a high concentration of points, following a straight line, which means that in most cases, the audience ratings of the movies are in agreement with those of the critics ratings.
It was therefore necessary to parse this HTML code, and to recover only the concerned data between certain HTML tags and to apply this on several pages and on all the years of the year 2000 to the year 2017.In my Python script, I send a GET HTML request to the IMDb site to retrieve the concerned page at regular times. more ninja. I am reading your post from the beginning, it was so interesting to read & I feel thanks to you for posting such a good blog, keep updates regularly.It is very excellent blog and useful article thank you for sharing with us , keep postingI'm glad to hear that, Data Science. For some movies, there is for example, no gross, no votes or no duration of the film. We also note that the films that have high ratings from critics are those who have brought back a lot of money.On this graph, we can see that the more people enjoy a movie, the more they vote and give a good rating.The film that garnered the most votes is the movie “The Dark Knight: The Dark Knight” with 1865768 votes.On this graph, we notice that the movies between 60 minutes and 150 minutes (2h30) are the ones that bring the most. As we have a high number of dimensions and data to be plotted, the preferred method on this situations are the ‘heatmaps’.In order to improve the performance of the model, we’ll only use ratings for 1000 movies.In addition, as k-means algorithm does not deal well with sparse datasets, we will need to cast it as the We will take an arbitrary number of clusters in order to make an analysis of the results obtained and spot certain trends and commonalities within each group. During this phase, it is possible to use machine learning techniques to predict the information you want. Introduction. We will do this by defining a function that will calculate each user’s average rating for all science fiction and romance movies.In order to have a more delimited subset of people to study, we are going to bias our grouping to only get ratings from those users that like either romance or science fiction movies.We can see that there are 183 number of records ,and for each one, there is a rating for a romance and science fiction movie.Now, we will make some Visualization Analysis in order to obtain a good overview of the biased dataset and its characteristics.The biase that we have created previously is perfectly clear now. Classroom, Online and Corporate training in R ProgrammingThanks for sharing amazing information about python .Gain the knowledge and hands-on experience in I really appreciate information shared above. To improve visibility, I therefore divided in 6 years (2000 to 2005, 2006 to 2011 and 2012 to 2017).Therefore, between 2000 and 2017, the public gives scores close to the ratings of the critics on a large majority of the films and one deduces that the public and the critics have the same opinion on a movie.The first dashboard is for Action, Adventure, Animation, Biography, Comedy and Crime movies from 2000 to 2017.The second dashboard is for genre movies Documentary, Drama, Family, Fantasy, Horror and Music between 2000 and 2017.The third dashboard is for genre movies Mystery, Romance, Science Fiction, Thriller, War and Western between 2000 to 2017.The 3 dashboards show that the action, adventure, animation, and family films are the ones that reported the most, the audience ratings of the movies are quite close to those of the critics ratings, and the films that are well rated by the public and the critics are the ones who brought in a lot of money.The preparation of the data, the modeling of these data, then the visualization of these data with a wide variety of graphs, and finally the interpretation of these graphs made it possible to conduct an analysis and a global view of movies released in the cinema between 2000 and 2017.In each issue we cover all things awesome in the markets, economy, crypto, tech, and more! The data for this little project comes from the IMDb website and, in particular, from my personal ratings of 442 titles recorded there.