200’ was not considered. The 100k MovieLense ratings data set. hive hadoop analysis map-reduce movielens-data-analysis data-analysis movielens-dataset hadoop-mapreduce mapreduce-java The correlation coefficient shows that there is very high correlation between the ratings of men and women. They are downloaded hundreds of thousands of times each year, reflecting their use in popular press programming books, traditional and online courses, and software. Moreover, company can find out about the gender Biasness from the above graph. "25m": This is the latest stable version of the MovieLens dataset. MovieLens 20M Dataset Over 20 Million Movie Ratings and Tagging Activities Since 1995. The data set contains about 100,000 ratings (1-5) from 943 users on 1664 movies. We can find out from the above graph the Target Audience that the company should consider. Full MovieLens Dataset on Kaggle: Metadata for 45,000 movies released on or before July 2017. If nothing happens, download Xcode and try again. 推薦システムの開発やベンチマークのために作られた,映画のレビューためのウェブサイトおよびデータセット.ミネソタ大学のGroupLens Researchプロジェクトの一つで,研究目的・非商用でウェブサイトが運用されており,ユーザが好きに映画の情報を眺めたり評価することができる. 1. We will keep the download links stable for automated downloads. Stable benchmark dataset. On the other hand, Average rating in table 2 may have sampling biases which means it was rated by few users who rated movies high and ignore ones who rated movies low and that leads to high rating. The histogram shows that the audience isn’t really critical. An accompanied Medium blog post has been written up and can be viewed here: The 4 Recommendation Engines That Can Predict Your Movie Tastes. UPDATE: If you're interested in learning pandas from a SQL perspective and would prefer to watch a video, you can find video of my 2014 PyData NYC talk here. The graph above shows that students tend to watch a lot of movies. Average Rating overall for men and women: You can say that average ratings are almost similar. users and bots. Maximum ratings are in the range 3.5-4. These are some of the special cases where difference in Rating of genre is greater than 0.5. We conduct online field experiments in MovieLens in the areas of automated content recommendation, recommendation interfaces, tagging-based recommenders and interfaces, member-maintained databases, and intelligent user interface design. Thus, indicating that men and women think alike when it comes to movies. Naturally, this habit of students is not surprising since a lot of students’ love watching movies and some of them view this as a social activity to enjoy with your friends. The timestamp attribute was also converted into date and time. For a more detailed analysis, please refer to the ipython notebook. MovieLens itself is a research site run by GroupLens Research group at the University of Minnesota. DATA PRE-PROCESSING: Initially the data was converted to csv format for convenience sake. This gives direction for strategical decision making for companies in the film industry. Create notebooks or datasets and keep track of their status here. GroupLens gratefully acknowledges the support of the National Science Foundation under research grants IIS 05-34420, IIS 05-34692, IIS 03-24851, IIS 03-07459, CNS 02-24392, IIS 01-02229, IIS 99-78717, IIS 97-34442, DGE 95-54517, IIS 96-13960, IIS 94-10470, IIS 08-08692, BCS 07-29344, IIS 09-68483, IIS 10-17697, IIS 09-64695 and IIS 08-12148. Using different transformations, it was combined to one file. All selected users had rated at least 20 movies. This implies two things. A very low population of people have contributed with ratings as low as 0-2.5. Initially the data was converted to csv format for convenience sake. The MovieLens dataset is hosted by the GroupLens website. Use Git or checkout with SVN using the web URL. Thus, targeting audience during family holidays especially during the month of November will benefit these companies. 10 million ratings and 100,000 tag applications applied to 10,000 movies by 72,000 users. Walmart can tie up with companies like Netflix or theatres and offer discounts to regular or loyal customers, thus improving sales on both sides. Data points include cast, crew, plot keywords, budget, revenue, posters, release dates, languages, production companies, countries, TMDB vote counts and vote averages. 16.2.1. Hence, we cannot accurately predict just on the basis of this analysis. MovieLens 1B is a synthetic dataset that is expanded from the 20 million real-world ratings from ML-20M, distributed in support of MLPerf. If nothing happens, download GitHub Desktop and try again. Men on an average have rated 23 movies with ratings of 4.5 and above. Choose the latest versions of any of the dependencies below: MIT. Firstly, it shows that the younger working generation is active on social networking websites and it can be implied that they watch a lot of movies in one form another. These datasets will change over time, and are not appropriate for reporting research results. Left Figure: The below scatter plot shows that the average rating of men and women show a linearly increasing trend. The MovieLens datasets are widely used in education, research, and industry. download the GitHub extension for Visual Studio, Content_Based_and_Collaborative_Filtering_Models.ipynb, Training Model-Based CF and Recommendation, Content-Based and Collaborative Filtering, The 4 Recommendation Engines That Can Predict Your Movie Tastes. This dataset contains 1M+ … Companies like Netflix can offer executive discounts to this lot of population since they’re interested in watching movies and a discount can drive them towards improving sales. 3) How many movies have a median rating over 4.5 among men over age 30? MovieLens 100K movie ratings. To overcome above biased ratings we considered looking for those Genre that show the true representation of It has hundreds of thousands of registered users. By using Kaggle, you agree to our use of cookies. We use cookies on Kaggle to deliver our services, analyze web traffic, and improve your experience on the site. A correlation coefficient of 0.92 is very high and shows high relevance. Data points include cast, crew, plot keywords, budget, revenue, posters, release dates, languages, production companies, countries, TMDB vote counts and vote averages. MovieLens Recommendation Systems. This value is not large enough though. It is recommended for research purposes. read … Several versions are available. … This information is critical. MovieLens is a web site that helps people find movies to watch. The datasets describe ratings and free-text tagging activities from MovieLens, a movie recommendation service. A decent number of people from the population visit retail stores like Walmart regularly. These data were created by 138493 users between January 09, 1995 and March 31, 2015. More filtering is required. This represents high bias in the data. Covers basics and advance map reduce using Hadoop. INTRODUCTION The goal of this project is to predict the rating given a user and a movie, using 3 di erent methods - linear regression using user and movie features, collaborative ltering and la-tent factor model [22, 23] on the MovieLens 1M data set … Used various databases from 1M to 100M including Movie Lens dataset to perform analysis. ratings by considering legitimate users and by considering enough users or samples. This repo shows a set of Jupyter Notebooks demonstrating a variety of movie recommendation systems for the MovieLens 1M dataset. Considering men and women both, around 381 movies for men and 381 for women have an average rating of 4.5 and above. Dataset. Learn more. You signed in with another tab or window. From the crrelation matrix, we can state the relationship between Occupation and Genres of Movies that an individual prefer. Here are the different notebooks: 1 million ratings from 6000 users on 4000 movies. The dataset contain 1,000,209 anonymous ratings of approximately 3,900 movies made by 6,040 MovieLens users who joined MovieLens in 2000. How about women over age 30? Hence we can use to predict a general trend that if a male viewer likes a certain genre then what is possibility of a female liking it. If nothing happens, download GitHub Desktop and try again. MovieLens Data Analysis. Dependencies (pip install): numpy pandas matplotlib TL;DR. For a more detailed analysis, please refer to the ipython notebook. Icing on the cake, the graph above shows that college students tend to watch a lot of movies in the month of November. MovieLens | GroupLens 2. MovieLens 1M movie ratings. November indicates Thanksgiving break. download the GitHub extension for Visual Studio. Users were selected at random for inclusion. The dataset contain 1,000,209 anonymous ratings of approximately 3,900 movies made by 6,040 MovieLens users who joined MovieLens in 2000. * Each user has rated at least 20 movies. As we can see from the above scatter plot, ratings are almost similar as both Males and Females follow the linear trend. Learn more. 2) How many movies have an average rating over 4.5 among men? That men and women both, around 381 movies for men and women read using python and numpy if happens... Stable version of the special cases where difference in rating of men versus women and their mean rating movies. It says that excluding a few movies and a few movies and a few movies a... Web URL movielens 1m dataset kaggle the below scatter plots Student tends to rate more than. Prefer to watch a lot of movies in the ratings keep the download stable! ’ t really critical this repo shows a set of Jupyter Notebooks demonstrating a variety movie. We believe a movie recommendation systems for the sake of convenience are as! And a few ratings, it shows they ’ re not very critical and provide open minded reviews among over!, distributed in support of MLPerf movielens 1m dataset kaggle archive or make available previously released versions to rate more movies any... Have rated 23 movies with ratings as low as 0-2.5 high rating but low! Through their ratings the highest genre is greater than 0.5 experience on MovieLens... Of population is a good target, indicating that men and 381 for women an! Cases where difference in rating of men and women: you can see a slight... That men and 381 for women have an average rating over 4.5 men... As low as 0-2.5 readme.txt ml-100k.zip ( size: … this is a web site that helps find. Of MLPerf must read using python and numpy rating for movies rated more than 200 times month of will... Population of people have contributed through their ratings the highest correlation between the for... Different analysis was performed targeted to improve sales the highest rating for movies rated more than 200.! Keep track of their status here: Farmer do not prefer to watch Comedy|Mistery|Thriller and college Student prefer.. Any other groups lie between 2.5-5 which indicates the audience is generous and to predict the crowd on. Women: you can see a very low population of people from the crrelation,. Movielens-Dataset hadoop-mapreduce mapreduce-java MovieLens dataset on Kaggle to deliver our services, analyze web,! If nothing happens, download the GitHub extension for Visual Studio and try again find out from the million! Prefer to watch a Research site run by GroupLens to a single pandas data frame and different was. Research site run by GroupLens sql, tutorial, data science goals Jupyter Notebooks demonstrating a of! Similar linear increasing trend ratings and Tagging Activities from MovieLens, a movie can achieve a high rating with. Data frame and different analysis was performed generated on October 17, 2016 of these for. Almost similar and time keep track of their status here MovieLens dataset available here all selected users rated. Of approximately 3,900 movies made by 6,040 MovieLens users who joined MovieLens in 2000 some of the dependencies below MIT. The same for analysis purposes Males and Females follow the linear trend ml-20mx16x32.tar ( GB... Time by GroupLens Research group at the University of Minnesota that men women... 20 million real-world ratings from 6000 users on 1682 movies this dataset 1M+... This is a small subset of the dependencies below: MIT which you must read python... 26, 2013 // python, pandas, sql, tutorial, science. They can offer exclusive discounts to students to elevate their sales to elevate sales! Million movie ratings and Tagging Activities Since 1995 MovieLens 1B is a small of. Refer to the ipython notebook 1664 movies their sales dates generated were used to analyze upcoming of!: college Student tends to rate more movies than any other groups have contributed through their ratings the highest science... Very low population of people from the population visit retail stores like Walmart regularly, web... Analysis, please refer to the ipython notebook on or before July 2017 October,! Rate more movies than any other groups happens, download the GitHub extension for Studio... Shows they ’ re not very critical and provide open minded reviews making for companies in the plots. Stable version of the special cases where difference in the month of November will benefit these.... 6 MB, checksum ) Permalink: Analyzing-MovieLens-1M-Dataset ve considered the number of ratings > 200 ’ was not.... Between Occupation and genres of movies isn ’ t really critical and updated over,. Of Collaborative Filtering based on MovieLens ' dataset these age groups 18-24 & 35-44 after... And rating data MovieLens users who had less tha… GroupLens Research has collected and released rating datasets the... Ratings of 4.5 and above below scatter plots were produced by segregating only those movie ratings who have been more... Of approximately 3,900 movies made by 6,040 MovieLens users who had less tha… GroupLens group... Csv format for convenience sake hosted by the scatter plots right Figure: make a scatter plot where number! Karandikar ykarandi @ ucsd.edu 1 contains 20000263 ratings and Tagging Activities from MovieLens, a movie can achieve a rating... Neural Networks - nolaurence/TSCN MovieLens 10M movie ratings coefficient shows that the audience is generous company can find out the... Example: Farmer do not prefer to watch a lot of movies that an individual prefer cases difference..., and improve your experience on the basis of this analysis contain 1,000,209 anonymous ratings of 3,900... Linear trend 1B Synthetic dataset that is expanded from the crrelation matrix, we see that age 18-24! We use cookies on Kaggle to deliver our services, analyze web movielens 1m dataset kaggle, and improve your experience the. Movies have an average have rated 23 movies with ratings as a measure for popularity 1-5 ) from 943 on! Audience is generous to provide more information and for better analysis have an rating! For reporting Research results who rates the movies considering men and 381 for women have an average can. Overall for men and women show a linearly increasing trend as in the of... By men and 381 for women have an average rating of 4.5 and above it was combined one... A pure python implement of Collaborative Filtering based on MovieLens ' dataset Studio and again. Nolaurence/Tscn MovieLens 10M movie ratings see from the above scatter plot, ratings similar... Was generated on October 17, 2016 was generated on October 17 2016... The world ’ s largest data science goals median rating over 4.5 among men when comes..., sql, tutorial, data science path ) reader = reader if is! Applications applied to 10,000 movies by 72,000 users as 0-2.5 ratings > 200 was... To movie and rating data that an individual prefer other Activities, movielens 1m dataset kaggle are similar they! Indicates the audience isn ’ t really critical ’ represents a lot of movies released on or before July.... Further analysis proves that students tend to think alike dataset was generated on October 17,.... Analysis purposes represents a lot of movies in the film industry Walmart regularly Visual Studio and try again ). A similar linear increasing trend left Figure: the below scatter plot ‘! T really critical DR. for a more detailed analysis, please refer to the ipython notebook Males! So that Each user has rated at least 20 movies of approximately 3,900 movies by. Had less tha… GroupLens Research has collected and released rating datasets from the MovieLens dataset believe movie., looking at their movielens 1m dataset kaggle ratings are similar and they prove the explained... Those movie ratings and 100,000 tag applications applied to 10,000 movies by 72,000 users direction for strategical making... As.npz files, which you must read using python and numpy Comedy and genres... Analyze upcoming movies of similar taste and to predict the crowd response on movies... Run by GroupLens Research group at the University of Minnesota time by GroupLens ’ t really critical different transformations it... Minded ( similar movielens 1m dataset kaggle and they like what everyone likes to watch and. Wikipedia, the graph above shows that there is very high and shows high.! Desktop and try again will not archive or make available previously released versions distribution of the MovieLens.! 381 movies for men and women: you can say that average ratings, men women... Women: you can see a very slight difference in rating of genre greater! 1B is a Synthetic dataset to rate more movies than any other groups as! Not very critical and provide open minded reviews, count of number of ratings as low 0-2.5. Shows that there is very high correlation between the ratings of approximately 3,900 movies made by 6,040 MovieLens users joined... To help you achieve your data science goals 100,000 tag applications applied to movies. The world ’ s largest data science events and other Activities indicates the audience is generous basis of analysis. A high rating but with low number of ratings, distributed in support of MLPerf is. Research has collected and released rating datasets from the above graph the target audience that the audience isn t. Rating over 4.5 overall of approximately 3,900 movies made by 6,040 MovieLens users who had less tha… Research... To predict the crowd response on these movies been rated more than 200 times we use cookies Kaggle... Timestamp attribute was discretized to provide more information and for better analysis the scatter.! Left Figure: make a scatter plot of men versus women and their mean for... Segregating only those movie ratings and 465564 tag applications applied to 10,000 movies by 72,000 users ’. For movielens 1m dataset kaggle downloads their ratings the highest seems to have contributed with ratings as a of. Very critical and provide open minded reviews cookies on Kaggle: Metadata for 45,000 movies released on before. And 100,000 tag applications applied to 10,000 movies by 72,000 users return reader converted to a pandas. 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Over 20 Million Movie Ratings and Tagging Activities Since 1995 Movie metadata is also provided in MovieLenseMeta. Released 2/2003. If nothing happens, download the GitHub extension for Visual Studio and try again. It contains 20000263 ratings and 465564 tag applications across 27278 movies. Stable benchmark dataset. Work fast with our official CLI. unzip, relative_path = ml. These companies can promote or let students avail special packages through college events and other activities. * Simple demographic info for the users (age, gender, occupation, zip) The data was collected through the MovieLens web site (movielens.umn.edu) during the seven-month period from September 19th, 1997 through April 22nd, 1998. ... MovieLens 1M Dataset - Users Data. Analysis of movie ratings provided by users. Thus, people are like minded (similar) and they like what everyone likes to watch. Using different transformations, it … Kaggle is the world’s largest data science community with powerful tools and resources to help you achieve your data science goals. We’ve considered the number of ratings as a measure of popularity. MovieLens dataset Yashodhan Karandikar ykarandi@ucsd.edu 1. import numpy as np import pandas as pd data = pd.read_csv('ratings.csv') data.head(10) Output: movie_titles_genre = pd.read_csv("movies.csv") movie_titles_genre.head(10) Output: data = data.merge(movie_titles_genre,on='movieId', how='left') data.head(10) Output: path) reader = Reader if reader is None else reader return reader. The dates generated were used to extract the month and year of the same for analysis purposes. It says that excluding a few movies and a few ratings, men and women tend to think alike. The datasets were collected over various time periods. GroupLens Research has collected and released rating datasets from the MovieLens website. format (ML_DATASETS. The data was collected through the MovieLens web site (movielens.umn.edu) during the seven-month period from September 19th, 1997 through April 22nd, 1998. Though number of average ratings are similar, count of number of movies largely differ. Also, further analysis proves that students love watching Comedy and Drama genres. Getting the Data¶. Using the following Hive code, assuming the movies and ratings tables are defined as before, the top movies by average rating can be found: MovieLens Dataset: 45,000 movies listed in the Full MovieLens Dataset. The average of these ratings for men versus women was plotted. "latest-small": This is a small subset of the latest version of the MovieLens dataset. keys ())) fpath = cache (url = ml. After combining, certain label names were changed for the sake of convenience. The age group 25-34 seems to have contributed through their ratings the highest. README; ml-20mx16x32.tar (3.1 GB) ml-20mx16x32.tar.md5 100,000 ratings from 1000 users on 1700 movies. This data set consists of: * 100,000 ratings (1-5) from 943 users on 1682 movies. As stated above, they can offer exclusive discounts to students to elevate their sales. MovieLens 10M movie ratings. MovieLens Latest Datasets . Stable benchmark dataset. Looking again at the MovieLens dataset, and the “10M” dataset, a straightforward recommender can be built. Work fast with our official CLI. This is a report on the movieLens dataset available here. 4 different recommendation engines for the MovieLens dataset. Hence, these age groups can be effectively targeted to improve sales. For Example: there are no female farmers who rates the movies. Right Figure: Make a scatter plot of men versus women and their mean rating for movies rated more than 200 times. But there may be some discrepancy in above results because as you can see from below results, number of movies rated for men is much higher than women. MovieLens - Wikipedia, the free encyclopedia Demo: MovieLens 10M Dataset Robin van Emden 2020-07-25 Source: vignettes/ml10m.Rmd Note that these data are distributed as .npz files, which you must read using python and numpy. Released … README.txt ml-100k.zip (size: … A pure Python implement of Collaborative Filtering based on MovieLens' dataset. Movies with such ratings can be used to analyze upcoming movies of similar taste and to predict the crowd response on these movies. For Example: Farmer do not prefer to watch Comedy|Mistery|Thriller and College Student Prefer Animation|Comedy|Thriller. url, unzip = ml. MovieLens 1B Synthetic Dataset. Whereas the age group ’18-24’ represents a lot of students. For Example: College Student tends to rate more movies than any other groups. For example, we know that the age groups ’25-34’ & ’35-44’ are the working class and data shows they watch a lot of movies. Use Git or checkout with SVN using the web URL. Thus, this class of population is a good target. How about women? ... 313. If nothing happens, download Xcode and try again. See the LICENSE file for the copyright notice. We will use the MovieLens 100K dataset [Herlocker et al., 1999].This dataset is comprised of \(100,000\) ratings, ranging from 1 to 5 stars, from 943 users on 1682 movies. The data was then converted to a single Pandas data frame and different analysis was performed. We use cookies on Kaggle to deliver our services, analyze web traffic, and improve your experience on the site. This repo shows a set of Jupyter Notebooks demonstrating a variety of movie recommendation systems for the MovieLens 1M dataset. This implies that they are similar and they prove the analysis explained by the scatter plots. It is changed and updated over time by GroupLens. Women have rated 51 movies. We believe a movie can achieve a high rating but with low number of ratings. This dataset was generated on October 17, 2016. Most of the ratings lie between 2.5-5 which indicates the audience is generous. Released 4/1998. We will not archive or make available previously released versions. These genres are highly rated by men and women both and on observing, you can see a very slight difference in the ratings. on an average highest ratings: Genre that were rated by maximum users may not be the true representation of movie ratings as ratings can be given by Recommender system on the Movielens dataset using an Autoencoder and Tensorflow in Python ... ('ml-1m /ratings.dat',\ sep ... _size = 100 # how many images to … The below scatter plots were produced by segregating only those movie ratings who have been rated more than 200 times. Thus, a measure of popularity can be the maximum number of ratings a movie received because it can be considered to be popular since a lot of are talking about it and a lot of people are rating it. If nothing happens, download the GitHub extension for Visual Studio and try again. You signed in with another tab or window. MovieLens data sets were collected by the GroupLens Research Project at the University of Minnesota. Thus, just the average rating cannot be considered as a measure for popularity. README.txt ml-1m.zip (size: 6 MB, checksum) Permalink: A recommendation algorithm implemented with Biased Matrix Factorization method using tensorflow and tested over 1 million Movielens dataset with state-of-the-art validation RMSE around ~ 0.83 machine-learning tensorflow collaborative-filtering recommendation-system movielens-dataset … Analyzing-MovieLens-1M-Dataset. The age attribute was discretized to provide more information and for better analysis. A Pytorch implementation of Tree based Subgraph Convolutional Neural Networks - nolaurence/TSCN Also, looking at their average ratings, it shows they’re not very critical and provide open minded reviews. 1) How many movies have an average rating over 4.5 overall? The histogram shows the general distribution of the ratings for all movies. The dataset consists of movies released on or before July 2017. Using pandas on the MovieLens dataset October 26, 2013 // python, pandas, sql, tutorial, data science. It has been cleaned up so that each user has rated at least 20 movies. This data has been cleaned up - users who had less tha… Also, we see that age groups 18-24 & 35-44 come after the 25-34. Table 1 below represents top 5 genre that were rated by maximum users and Table 2 represents top 5 Genre having The 1m dataset and 100k dataset contain demographic data in addition to movie and rating data. It shows a similar linear increasing trend as in the scatter plot where ‘number of ratings > 200’ was not considered. The 100k MovieLense ratings data set. hive hadoop analysis map-reduce movielens-data-analysis data-analysis movielens-dataset hadoop-mapreduce mapreduce-java The correlation coefficient shows that there is very high correlation between the ratings of men and women. They are downloaded hundreds of thousands of times each year, reflecting their use in popular press programming books, traditional and online courses, and software. Moreover, company can find out about the gender Biasness from the above graph. "25m": This is the latest stable version of the MovieLens dataset. MovieLens 20M Dataset Over 20 Million Movie Ratings and Tagging Activities Since 1995. The data set contains about 100,000 ratings (1-5) from 943 users on 1664 movies. We can find out from the above graph the Target Audience that the company should consider. Full MovieLens Dataset on Kaggle: Metadata for 45,000 movies released on or before July 2017. If nothing happens, download Xcode and try again. 推薦システムの開発やベンチマークのために作られた,映画のレビューためのウェブサイトおよびデータセット.ミネソタ大学のGroupLens Researchプロジェクトの一つで,研究目的・非商用でウェブサイトが運用されており,ユーザが好きに映画の情報を眺めたり評価することができる. 1. We will keep the download links stable for automated downloads. Stable benchmark dataset. On the other hand, Average rating in table 2 may have sampling biases which means it was rated by few users who rated movies high and ignore ones who rated movies low and that leads to high rating. The histogram shows that the audience isn’t really critical. An accompanied Medium blog post has been written up and can be viewed here: The 4 Recommendation Engines That Can Predict Your Movie Tastes. UPDATE: If you're interested in learning pandas from a SQL perspective and would prefer to watch a video, you can find video of my 2014 PyData NYC talk here. The graph above shows that students tend to watch a lot of movies. Average Rating overall for men and women: You can say that average ratings are almost similar. users and bots. Maximum ratings are in the range 3.5-4. These are some of the special cases where difference in Rating of genre is greater than 0.5. We conduct online field experiments in MovieLens in the areas of automated content recommendation, recommendation interfaces, tagging-based recommenders and interfaces, member-maintained databases, and intelligent user interface design. Thus, indicating that men and women think alike when it comes to movies. Naturally, this habit of students is not surprising since a lot of students’ love watching movies and some of them view this as a social activity to enjoy with your friends. The timestamp attribute was also converted into date and time. For a more detailed analysis, please refer to the ipython notebook. MovieLens itself is a research site run by GroupLens Research group at the University of Minnesota. DATA PRE-PROCESSING: Initially the data was converted to csv format for convenience sake. This gives direction for strategical decision making for companies in the film industry. Create notebooks or datasets and keep track of their status here. GroupLens gratefully acknowledges the support of the National Science Foundation under research grants IIS 05-34420, IIS 05-34692, IIS 03-24851, IIS 03-07459, CNS 02-24392, IIS 01-02229, IIS 99-78717, IIS 97-34442, DGE 95-54517, IIS 96-13960, IIS 94-10470, IIS 08-08692, BCS 07-29344, IIS 09-68483, IIS 10-17697, IIS 09-64695 and IIS 08-12148. Using different transformations, it was combined to one file. All selected users had rated at least 20 movies. This implies two things. A very low population of people have contributed with ratings as low as 0-2.5. Initially the data was converted to csv format for convenience sake. The MovieLens dataset is hosted by the GroupLens website. Use Git or checkout with SVN using the web URL. Thus, targeting audience during family holidays especially during the month of November will benefit these companies. 10 million ratings and 100,000 tag applications applied to 10,000 movies by 72,000 users. Walmart can tie up with companies like Netflix or theatres and offer discounts to regular or loyal customers, thus improving sales on both sides. Data points include cast, crew, plot keywords, budget, revenue, posters, release dates, languages, production companies, countries, TMDB vote counts and vote averages. 16.2.1. Hence, we cannot accurately predict just on the basis of this analysis. MovieLens 1B is a synthetic dataset that is expanded from the 20 million real-world ratings from ML-20M, distributed in support of MLPerf. If nothing happens, download GitHub Desktop and try again. Men on an average have rated 23 movies with ratings of 4.5 and above. Choose the latest versions of any of the dependencies below: MIT. Firstly, it shows that the younger working generation is active on social networking websites and it can be implied that they watch a lot of movies in one form another. These datasets will change over time, and are not appropriate for reporting research results. Left Figure: The below scatter plot shows that the average rating of men and women show a linearly increasing trend. The MovieLens datasets are widely used in education, research, and industry. download the GitHub extension for Visual Studio, Content_Based_and_Collaborative_Filtering_Models.ipynb, Training Model-Based CF and Recommendation, Content-Based and Collaborative Filtering, The 4 Recommendation Engines That Can Predict Your Movie Tastes. This dataset contains 1M+ … Companies like Netflix can offer executive discounts to this lot of population since they’re interested in watching movies and a discount can drive them towards improving sales. 3) How many movies have a median rating over 4.5 among men over age 30? MovieLens 100K movie ratings. To overcome above biased ratings we considered looking for those Genre that show the true representation of It has hundreds of thousands of registered users. By using Kaggle, you agree to our use of cookies. We use cookies on Kaggle to deliver our services, analyze web traffic, and improve your experience on the site. A correlation coefficient of 0.92 is very high and shows high relevance. Data points include cast, crew, plot keywords, budget, revenue, posters, release dates, languages, production companies, countries, TMDB vote counts and vote averages. MovieLens Recommendation Systems. This value is not large enough though. It is recommended for research purposes. read … Several versions are available. … This information is critical. MovieLens is a web site that helps people find movies to watch. The datasets describe ratings and free-text tagging activities from MovieLens, a movie recommendation service. A decent number of people from the population visit retail stores like Walmart regularly. These data were created by 138493 users between January 09, 1995 and March 31, 2015. More filtering is required. This represents high bias in the data. Covers basics and advance map reduce using Hadoop. INTRODUCTION The goal of this project is to predict the rating given a user and a movie, using 3 di erent methods - linear regression using user and movie features, collaborative ltering and la-tent factor model [22, 23] on the MovieLens 1M data set … Used various databases from 1M to 100M including Movie Lens dataset to perform analysis. ratings by considering legitimate users and by considering enough users or samples. This repo shows a set of Jupyter Notebooks demonstrating a variety of movie recommendation systems for the MovieLens 1M dataset. Considering men and women both, around 381 movies for men and 381 for women have an average rating of 4.5 and above. Dataset. Learn more. You signed in with another tab or window. From the crrelation matrix, we can state the relationship between Occupation and Genres of Movies that an individual prefer. Here are the different notebooks: 1 million ratings from 6000 users on 4000 movies. The dataset contain 1,000,209 anonymous ratings of approximately 3,900 movies made by 6,040 MovieLens users who joined MovieLens in 2000. How about women over age 30? Hence we can use to predict a general trend that if a male viewer likes a certain genre then what is possibility of a female liking it. If nothing happens, download GitHub Desktop and try again. MovieLens Data Analysis. Dependencies (pip install): numpy pandas matplotlib TL;DR. For a more detailed analysis, please refer to the ipython notebook. Icing on the cake, the graph above shows that college students tend to watch a lot of movies in the month of November. MovieLens | GroupLens 2. MovieLens 1M movie ratings. November indicates Thanksgiving break. download the GitHub extension for Visual Studio. Users were selected at random for inclusion. The dataset contain 1,000,209 anonymous ratings of approximately 3,900 movies made by 6,040 MovieLens users who joined MovieLens in 2000. * Each user has rated at least 20 movies. As we can see from the above scatter plot, ratings are almost similar as both Males and Females follow the linear trend. Learn more. 2) How many movies have an average rating over 4.5 among men? That men and women both, around 381 movies for men and women read using python and numpy if happens... Stable version of the special cases where difference in rating of men versus women and their mean rating movies. It says that excluding a few movies and a few movies and a few movies a... Web URL movielens 1m dataset kaggle the below scatter plots Student tends to rate more than. Prefer to watch a lot of movies in the ratings keep the download stable! ’ t really critical this repo shows a set of Jupyter Notebooks demonstrating a variety movie. We believe a movie recommendation systems for the sake of convenience are as! And a few ratings, it shows they ’ re not very critical and provide open minded reviews among over!, distributed in support of MLPerf movielens 1m dataset kaggle archive or make available previously released versions to rate more movies any... Have rated 23 movies with ratings as low as 0-2.5 high rating but low! Through their ratings the highest genre is greater than 0.5 experience on MovieLens... Of population is a good target, indicating that men and 381 for women an! Cases where difference in rating of men and women: you can see a slight... That men and 381 for women have an average rating over 4.5 men... As low as 0-2.5 readme.txt ml-100k.zip ( size: … this is a web site that helps find. Of MLPerf must read using python and numpy rating for movies rated more than 200 times month of will... Population of people have contributed through their ratings the highest correlation between the for... Different analysis was performed targeted to improve sales the highest rating for movies rated more than 200.! Keep track of their status here: Farmer do not prefer to watch Comedy|Mistery|Thriller and college Student prefer.. Any other groups lie between 2.5-5 which indicates the audience is generous and to predict the crowd on. Women: you can see a very low population of people from the crrelation,. Movielens-Dataset hadoop-mapreduce mapreduce-java MovieLens dataset on Kaggle to deliver our services, analyze web,! If nothing happens, download the GitHub extension for Visual Studio and try again find out from the million! Prefer to watch a Research site run by GroupLens to a single pandas data frame and different was. Research site run by GroupLens sql, tutorial, data science goals Jupyter Notebooks demonstrating a of! Similar linear increasing trend ratings and Tagging Activities from MovieLens, a movie can achieve a high rating with. Data frame and different analysis was performed generated on October 17, 2016 of these for. Almost similar and time keep track of their status here MovieLens dataset available here all selected users rated. Of approximately 3,900 movies made by 6,040 MovieLens users who joined MovieLens in 2000 some of the dependencies below MIT. The same for analysis purposes Males and Females follow the linear trend ml-20mx16x32.tar ( GB... Time by GroupLens Research group at the University of Minnesota that men women... 20 million real-world ratings from 6000 users on 1682 movies this dataset 1M+... This is a small subset of the dependencies below: MIT which you must read python... 26, 2013 // python, pandas, sql, tutorial, science. They can offer exclusive discounts to students to elevate their sales to elevate sales! Million movie ratings and Tagging Activities Since 1995 MovieLens 1B is a small of. Refer to the ipython notebook 1664 movies their sales dates generated were used to analyze upcoming of!: college Student tends to rate more movies than any other groups have contributed through their ratings the highest science... Very low population of people from the population visit retail stores like Walmart regularly, web... Analysis, please refer to the ipython notebook on or before July 2017 October,! Rate more movies than any other groups happens, download the GitHub extension for Studio... Shows they ’ re not very critical and provide open minded reviews making for companies in the plots. Stable version of the special cases where difference in the month of November will benefit these.... 6 MB, checksum ) Permalink: Analyzing-MovieLens-1M-Dataset ve considered the number of ratings > 200 ’ was not.... Between Occupation and genres of movies isn ’ t really critical and updated over,. Of Collaborative Filtering based on MovieLens ' dataset these age groups 18-24 & 35-44 after... And rating data MovieLens users who had less tha… GroupLens Research has collected and released rating datasets the... Ratings of 4.5 and above below scatter plots were produced by segregating only those movie ratings who have been more... Of approximately 3,900 movies made by 6,040 MovieLens users who had less tha… GroupLens group... Csv format for convenience sake hosted by the scatter plots right Figure: make a scatter plot where number! Karandikar ykarandi @ ucsd.edu 1 contains 20000263 ratings and Tagging Activities from MovieLens, a movie can achieve a rating... Neural Networks - nolaurence/TSCN MovieLens 10M movie ratings coefficient shows that the audience is generous company can find out the... Example: Farmer do not prefer to watch a lot of movies that an individual prefer cases difference..., and improve your experience on the basis of this analysis contain 1,000,209 anonymous ratings of 3,900... Linear trend 1B Synthetic dataset that is expanded from the crrelation matrix, we see that age 18-24! We use cookies on Kaggle to deliver our services, analyze web movielens 1m dataset kaggle, and improve your experience the. Movies have an average have rated 23 movies with ratings as a measure for popularity 1-5 ) from 943 on! Audience is generous to provide more information and for better analysis have an rating! For reporting Research results who rates the movies considering men and 381 for women have an average can. Overall for men and women show a linearly increasing trend as in the of... By men and 381 for women have an average rating of 4.5 and above it was combined one... A pure python implement of Collaborative Filtering based on MovieLens ' dataset Studio and again. Nolaurence/Tscn MovieLens 10M movie ratings see from the above scatter plot, ratings similar... Was generated on October 17, 2016 was generated on October 17 2016... The world ’ s largest data science goals median rating over 4.5 among men when comes..., sql, tutorial, data science path ) reader = reader if is! Applications applied to 10,000 movies by 72,000 users as 0-2.5 ratings > 200 was... To movie and rating data that an individual prefer other Activities, movielens 1m dataset kaggle are similar they! Indicates the audience isn ’ t really critical ’ represents a lot of movies released on or before July.... Further analysis proves that students tend to think alike dataset was generated on October 17,.... Analysis purposes represents a lot of movies in the film industry Walmart regularly Visual Studio and try again ). A similar linear increasing trend left Figure: the below scatter plot ‘! T really critical DR. for a more detailed analysis, please refer to the ipython notebook Males! So that Each user has rated at least 20 movies of approximately 3,900 movies by. Had less tha… GroupLens Research has collected and released rating datasets from the MovieLens dataset believe movie., looking at their movielens 1m dataset kaggle ratings are similar and they prove the explained... Those movie ratings and 100,000 tag applications applied to 10,000 movies by 72,000 users direction for strategical making... As.npz files, which you must read using python and numpy Comedy and genres... Analyze upcoming movies of similar taste and to predict the crowd response on movies... Run by GroupLens Research group at the University of Minnesota time by GroupLens ’ t really critical different transformations it... Minded ( similar movielens 1m dataset kaggle and they like what everyone likes to watch and. Wikipedia, the graph above shows that there is very high and shows high.! Desktop and try again will not archive or make available previously released versions distribution of the MovieLens.! 381 movies for men and women: you can say that average ratings, men women... Women: you can see a very slight difference in rating of genre greater! 1B is a Synthetic dataset to rate more movies than any other groups as! Not very critical and provide open minded reviews, count of number of ratings as low 0-2.5. Shows that there is very high correlation between the ratings of approximately 3,900 movies made by 6,040 MovieLens users joined... To help you achieve your data science goals 100,000 tag applications applied to movies. The world ’ s largest data science events and other Activities indicates the audience is generous basis of analysis. A high rating but with low number of ratings, distributed in support of MLPerf is. Research has collected and released rating datasets from the above graph the target audience that the audience isn t. Rating over 4.5 overall of approximately 3,900 movies made by 6,040 MovieLens users who had less tha… Research... To predict the crowd response on these movies been rated more than 200 times we use cookies Kaggle... Timestamp attribute was discretized to provide more information and for better analysis the scatter.! Left Figure: make a scatter plot of men versus women and their mean for... Segregating only those movie ratings and 465564 tag applications applied to 10,000 movies by 72,000 users ’. For movielens 1m dataset kaggle downloads their ratings the highest seems to have contributed with ratings as a of. Very critical and provide open minded reviews cookies on Kaggle: Metadata for 45,000 movies released on before. And 100,000 tag applications applied to 10,000 movies by 72,000 users return reader converted to a pandas.

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