Stable benchmark dataset. Understanding the data set structure and content by extracting some statistics will allow you to better pick your algorithm and the associated settingStep 12: Ratings - Check the rating notation distributionHow many genres are used across the list of movies?What is the count of movies with the "Comedy" genre?Which movie has 10 genres associated? README.txt; ml-20m.zip (size: 190 MB, checksum) 196 242 3 881250949 186 302 3 891717742 22 377 1 … If you have used Sql, you will know it has a JOIN function to join tables. Actually with 5 people, you can sum your min and max, and the rest will be assigned to the other 3 people (200 - 100 - 10 = 90), therefore each one should get something closer to 30 than 40, but also 2 could get 15, and the last 60.In short, when a node (here a user) has 4 times more links (rating) than the standard deviation value, this node can be considered as a Based on the elements gathered over the last few steps, and despite some of the phenomenon assessed in the data, we can consider that the rating dataset on its own is a possible candidate to build a solid recommendation engine.Again, we could eventually combine it to the tags and the genres and improve the overall recommendation results by increasing the result coverage in terms of users or movies.As a final conclusion, you can consider that the rating dataset on its own is the most promising candidate to build a recommendation engine and despite some of the phenomenon assessed for the rating dataset.However, while using this data will, you will need to pay attention to the following algorithm parameters:Defines the number of links between a pair of items. This set of rules are usually built using a transactional type of data set which identifies links between users and items. MovieLens 20M movie ratings. 1 million ratings from 6000 users on 4000 movies. Stable benchmark dataset. 20 million ratings and 465,000 tag applications applied to 27,000 movies by 138,000 users. Getting the Data¶. Then, the rule set is applied to either a user or an item to get a list of items to recommend.This imply 2 approaches to building recommendation systems:With this approach, you can build a model using past similarities between users behaviors (items previously purchased by a user or movies rated by a user for example).Collaborative filtering is based on the assumption that users who had similar behaviors in the past will have the same behaviors in the future, and that they will like items that other users with similar behaviors liked in the past.In this scenario, you can build your model by analyzing links (transactions) between 2 types of nodes, one will be the user and the other the item. The MovieLens dataset is hosted by the GroupLens website. Several versions are available. Stable benchmark dataset. (Input the full title with the year of production)What is the maximum number of tags associated with a movie?Step 12: Ratings - Check the rating notation distribution Analyze the MovieLens dataset (MovieLens App) Analyze the MovieLens dataset (MovieLens App) Join the conversation on Facebook. MovieLens 1B Synthetic Dataset. By using MovieLens, you will help GroupLens develop new experimental tools and interfaces for data exploration and recommendation. MovieLens 1M movie ratings. Includes tag genome data with 12 million relevance scores across 1,100 tags. GroupLens Research has collected and made available rating data sets from the MovieLens web site (These datasets will change over time, and are not appropriate for reporting research results. So here, your minimum and maximum compensations are respectively 10 and 100.But the average is 200/5 = 40 which is really far from the maximum. Pandas has something similar. This is a report on the movieLens dataset available here. Released 4/2015; updated 10/2016 to update links.csv and add tag genome data. The goal of a recommendation systems is to produce a list of rules. And the resulting model will allow you to extract the likelihood of a relation between a user and an item.In other word, when scoring with the model, the input information will be a user, and the output will be a list of items and their associated score.With this approach, you can recommend items that are similar to each other based on the number of links they have in common compared to other items using for example a series of associated keywords or tags, but also user clicks or orders.The above description is only meant to give you a brief (and simplified) overview of what is a recommendation system in general.As stated earlier, the link dataset only includes details to build URL to external web site.Let’s verify that every movie has a corresponding link and vice-versa using the following SQL:Based on the result, it seems that there isn’t any movies with no links and vice-versa.So, when building our application, we will be able to leverage these URL and enhance our user experience with external links.Just like with the links dataset, the movies dataset doesn’t include any transaction kind of details that could be used directly to link users together.Anyway, let’s check if all movies have genres with the following SQL:Based on the result, it seems that all movies have at least a genre.Now, let’s get the list of genres used across our 9125 movies with the following SQL:Based on the result returned by the above SQL statement, provide an answer to the question below then click on Now let’s get the number of movies associated with each genres by adjusting the previous SQL:You can see that 18 distinct genres are used across the 9125 movies.Based on the result returned by the above SQL statement, provide an answer to the question below then click on Now, let’s get the number of genres associated with each movies using the following SQL:As seen in the previous step, there are many movies with only one genre.Let’s count the movies per genre count using the following SQL:The result should be 2793 movies with one genre, which means almost a third of the movie set have one genre only.This means that these movies will be linked to another movie by at most one link, which will cause all relations between movies to be more or less equal in term of strength (the more links between nodes, the stronger the relationship is).You could also decide to simply exclude the movies with one genre and only keep the other but this would mean that you won’t provide results for them which would require to address them using an alternative approach.So, based on the elements gathered over the last steps, you can consider that the genre extracted from the movies on its own is not a good candidate to build a solid recommendation engine.Moreover, the genre data can only be used to address a content-based filtering approach.Now let’s have a look at the tags distribution using the following SQL:Now let’s determine the tag count distribution per movies using the following SQL:You can notice that out of the 689 movies with at least a tag, you have 483 movies with only one tag.Based on the elements gathered over the last steps, you can consider that the tag dataset on its own is not a good candidate to build a solid recommendation engine.Also the tag data can only be used to address a content-based filtering approach.Using the results provided by the previous SQL statements, provide an answer to the question below then click on Now let’s determine the rating count distribution per movies using the following SQL:This time, the list is a bit long to extract insights.However, you can notice that 3063 movies have only one rating and 1202 have only 2 ratings.Instead of browsing the results for insights, you can use some aggregates like the min, max, average, count, median and standard deviation using the following SQL:Using the results provided by the previous SQL statements, provide an answer to the question below then click on Now let’s determine the rating count distribution per user using the following SQL:You can notice that one user rated 2391 movies, and the top 10 users all rated more than 1000 movies.Again her, instead of browsing the results for insights, you can use some aggregates like the min, max, average, count, median and standard deviation using the following SQL:Using the results provided by the previous SQL statements, provide an answer to the question below then click on Now let’s determine the rating notation distribution using the following SQL:Now let’s determine the users distribution per rating notation using the following SQL:Now let’s determine the movies distribution per rating notation using the following SQL:Using the results provided by the previous SQL statements, provide an answer to the question below then click on Here are a few insights that you can gather based on the previous results:Using the rating count distribution per users results, especially the average and median metric and the standard deviation, you can assess that the distribution is skewed and that we have some outliers users (remember the top 10 users rated more than 1000 movies each, when the average is 274, and the median is 174).To keep it simple the median is the middle point of your list.