netflix dataset for visualization project

Creation of the model is generally not the end of the project. Country. First argument of the ggplot function is our data.frame, then we specified our variables in the aes() function. It consists of 4 text data files, each file contains over 20M rows, i.e. Data Visualization. Amount of Netflix Content By Top 10 Country. This project aims to build a movie recommendation mechanism within Netflix. I was curious to analyze the content released in Netflix platform which led me to create these simple, interactive and exciting visualizations with Tableau. I figured, there isn’t much i can do about this and had thought of giving up on this project, but then again i didn’t want to give up so easily, besides this is the essence of working with the data, figuring out how to make things work. Her third most watched day is Friday which is usually my least watched Netflix day. Now k is our new data in sapply(). How should you visualize your data? Learn more This workflow creates an interactive visualization dashboard of the "Netflix Movies and TV Shows" dataset. We also drop duplicated rows in the data set based on the “title”, “country”, “type”,” release_year” variables. Study of Netflix Dataset. Netflix has since stated that the algorithm was scaled to handle its 5 billion ratings (Netflix Technology Blog, 2017a). Full Name. Netflix both leverages and provides open source technology focused on providing the leading Internet television network. In this post, we’ll walk through several types of data science projects, including data visualization projects, data cleaning projects, and machine learning projects, and identify good places to find datasets for each. # 6: names of the second and third columns are changed by using names() function as seen below. I took it up as a challenge for myself to atleast be able to get two visualisations out of this to figure out some insights into my Netflix related behaviours. Now, we are going to drop the missing values, at point where it will be necessary. then continue with + and type of the graph will be added by using geom_graphytype. # 7: In the arrange() function we sorted our count.movie columns as descending but, now, we want to change this sort depends on the total values of "number of Movies" and "number of TV Shows". In this part we will check the observations, variables and values of our data. Sign up. This enables us to extract the individual components of a date. You can download it via this link: https://github.com/ygterl/EDA-Netflix-2020-in-R is collected from Flixable which is a third-party Netflix search engine. Maybe there is a short way but I couldn't find it. Therefore, we have to specify as descending. I’ll explain. This process is a little tiring. Dataset collection: information is beautiful - Data Dataset collection: R for Data Science Tidy Tuesdays Every machine learning project begins by understanding what the data and drawing the objectives. The charts are grouped in components and can be displayed locally or from the WebPortal. Downloads: 0 This Week Last Update: 2013-03-22. In the end, it would be incorrect to say that Netflix takes all its decisions based on Data Science insights as they still rely on human inputs from a lot of people. Ratings are on a five star (integral) scale from 1 to 5. If a more knowledgeable person than me, stumbles upon this blog and thinks there is a much better way to do things or i have erred somewhere, please feel free to share the feedback and help not just me but everyone grow together as a community. # 2: Created a new data frame by using data.frame() function. And, during this process, i hope that i can engage and inspire anyone else who is going through the same process as mine. The Google covid-19 mobility reports only have trend numbers ("+-x%") for the last day. Public Data Commons hosted by Open Science Data Cloud (OSDC) – public data sets of scientific interest, including genomics data, land survey data, Project Gutenberg, Space Weather Prediction data, etc # In ggplot2 library, the code is created by two parts. In the middle pane, select the Windows Forms App project type. This dataset consists of tv shows and movies available on Netflix as of 2019. With that out of the way, lets move on. Phone Number. Other problem with the dataset is, the shows which have most number of episodes and seasons, will be more frequent in the dataset than shows which have only couple of seasons. The dataset consisted of 100,480,507 ratings that 480,189 users gave to 17,770 movies. Tableau dashboards were created from the cleaned dataset. frame()’ function in R. It is a logical that indicates whether strings in a data frame should be treated as factor variables or as just plain strings. Direction is character string, partially matched to either "wide" to reshape to wide format, or "long" to reshape to long format. I also noticed, that the title of any Movie that was in the dataset, it only had a Movie Name, which leads me to believe that all the rows where season is Null, it means it is most likely a Movie. In this part we sort count.movie column as descending. so naturally shows with most frequencies are the shows which have multiple seasons and episodes (Eg: Friends, Brooklyn 99 etc). The dataset I used here come directly from Netflix. # reshape() function will be used to create a reshaped grouped data. # To check to arguments and detailed descriptions of functions please use to help menu or google.com. “type” and “Listed_in” should be categorical variable. First things first, lets start with the visualisations that i could extract from the data. Post this i turned my attention towards Title column. Finally, number of added contents in a day calculated by using summarise() and n() functions. # 1: Title column take place in our dataframe as character therefore I have to convert it to tbl_df format to apply the function below. The file "training_set.tar" is a tar of a directory containing 17770 files, one per movie. If we do not specify them at the beginning in the read function, we can not reach the missing values in future steps. Also description variable will not be used for the analysis or visualization but it can be useful for the further analysis or interpretation. Lets start! Data Sets for Data Visualization Projects: A typical data visualization project might be something along the lines of “I want to make an infographic about how income varies across the different states in the US”. Take a look, https://github.com/rckclimber/analysing-netflix-viewing-history, How to Leverage GCP’s Free Tier to Train a Custom Object Detection Model With YOLOv5, Data visualization with Python and JavaScript, Solving Optimization Problems: Using Excel, Mastering the mystical art of model deployment, January & December was when i spent most amount of time watching Netflix (obvious reason, it was holidays )where as my wife watched most amount of Netflix in May,June,August (reason: she was in between the jobs ) (Did you notice how July is lower than August, thats because her Mom was visiting us in July, she spent more time with her than Netflix), I usually watch Netflix on weekends, whereas my wife watches Netflix mostly on Sunday and Monday (that’s interesting insight, is she trying to beat the Monday Blues?). Recently, I was going through my Netflix’s “My Account” page and realised that you could download your profiles viewing activity in a csv format, I immediately thought it would be pretty cool to visualise my Netflix usage. # 2: df_by_date crated as a new grouped data frame. This section created by 3 parts; data reading, data cleaning and data visualization 3 different libraries (ggplot2, ggpubr, plotly) are used to visualize data. Data cleaning process is done. Missing values can be problem for the next steps. It’s interesting to me from a visualization standpoint, an editing one, and as a business model. In the below we have to write na.string=c(“”, “NA”) because, some values of our data are empty or taking place as NA. This is my Master Degree project, I am trying to improve the movie prediction by using machine learning techniques, for the Netflix data set. # 4: we created new grouped data frame by the name of amount_by_country. It simply converts the list to vector with all the atomic components are being preserved. As we see from above there are more than 2 times more Movies than TV Shows on Netflix. Then we groupped countries and types by using group_by() function (in the "dplyr" library). amount_by_country is used as data in the function. Kaggle datasets are an aggregation of user-submitted and curated datasets. 4. State. Curated by: Google Example data set… However, this list is too big to be visualized. This project is done under guidance of Dr. Status: Pre-Alpha. First, Obviously data cannot tell us when both me and my wife watch Netflix together. MovieIDs range from 1 to 17770 sequentially. Title of the graph is wroted by using ggtitle() function. 2. By default, sorting is ASCENDING. In 2006 Netflix announced the Netflix Prize, a competition for creating an algorithm that would “substantially improve the accuracy of predictions about how much someone is going to enjoy a movie based on their movie preferences.” There was a winner, which improved the algorithm by 10%. The dataset is collected from Flixable which is a third-party Netflix search engine. Thus, we will create a new data frame as table to see just top 10 countries by the name of "u". 3. Now we can start to visualization. To sort a data frame in R, use the order() function. ... manage projects, and build software together. Before to say something about 2020 we have to see year-end data. # 2: new_date variable created by selecting just years. First one is ggplot(), here we have to specify our arguments such as data, x and y axis and fill type. # In second part, adding title and other arguments of graph. # In the first part of visualisation, again, we have to specify our data labels, values, x ad y axis and type of graph. # Here plotly library used to visualise data. I wont get into details of how to visualise, You can check out the code for visualisations in case you are interested at this link : GitHub Rep : https://github.com/rckclimber/analysing-netflix-viewing-history. In the first graphy, ggplot2 library is used to visualize data with basic bar graph. To see the graph in chunk output or console you have to assign it to somewhere such as "fig", # From the above, we created our new table to use in graph. 3. First column should be type = second one country=. CustomerIDs range from 1 to 2649429, with gaps. Titles are grouped depending the new_date(year) and then na.omit function applied to date column to remove NA values. Primarly, group_by() function is used to select variable and then used summarise() function with n() to count number of TV Shows and Movies. Though, i was set up for disappointment, because this is the data that Netflix exported: The csv file had only 2 columns, date and the name of the show /season / episode in one column. In terms of shows, the most amount of time i spent watching is. After that we named x and y axis. In this way, we can analyze and visualise the data more easy. The charts are grouped in components and can be displayed either locally or from the KNIME WebPortal # 3: now we will visualize our new grouped data frame. Since this pattern is mostly consistent in all the dataset, we can split the string and extract it into 3 seperate columns: show_name, season, episode_name. I extracted Day, Month, Year, Day_of_week from this date column into separate columns using the to_datetime function of Pandas. # 1: split the countries (ex: "United States, India, South Korea, China" form to 'United States' 'India' 'South Korea' 'China') in the country column by using strsplit() function and then assign this operation to "k" for future use. Even when we do watch movies, its almost always on a Saturday. Get project updates, sponsored content from our select partners, and more. Between TV Shows and Movies, both of us watch TV shows the most. Study of Netflix Dataset. If this column remains in character format and I want to implement the function, R returns an error: " Error in UseMethod("group_by_") : no applicable method for 'group_by_' applied to an object of class "character"" Therefore, first I assign it title column to f then convert the format as tibble and then assign it again to title column. Since rating is the categorical variable with 14 levels we can fill in (approximate) the missing values for rating with a mode. # After the arrange function, top_n() function is used to list the specified number of rows. Ferdio is a leading infographic and data visualization agency specialized in transforming data and information into captivating visuals. coloured the graphy depends on the countries. values_table1 <- rbind(c('show_id', 'type', 'title', 'director', 'cast', 'country', 'date_added', 'release_year', 'rating' , 'duration', 'listed_in', 'description'), c("Unique ID for every Movie / TV Show", netds$date_added <- mdy(netds$date_added), netds$listed_in <- as.factor(netds$listed_in), # printing the missing values by creating a new data frame, data.frame("Variable"=c(colnames(netds)), "Missing Values"=sapply(netds, function(x) sum(is.na(x))), row.names=NULL), netds$rating[is.na(netds$rating)] <- mode(netds$rating), netds=distinct(netds, title, country, type, release_year, .keep_all = TRUE). The dataset I used here come directly from Netflix. Focus. Of the 15,000 images, I found (and corrected) issues with 4,986 (33%) of them. Here are some great public data sets you can analyze for free right now. One of the key data analysis tools that the BellKor team used to win the Netflix Prize was the Singular Value Decomposition (SVD) algorithm. If you need help with putting your findings into form, we also have write-ups on data visualization blogs to follow and the best data visualization examples for inspiration. The first line of each file contains the movie id followed by a colon. Get Updates. Amount of content by Rating (Movie vs. TV Show), Top 20 Directors By The Amount of Content on Netflix, You can download the data set and Rmarkdown document via my github profile : https://github.com/ygterl/EDA-Netflix-2020-in-R, #ExploratoryDataAnalysis #EDA #DataScience #Datavisualisation #Netflix, Please follow my medium page to be informed about weekly articles…, netds <- read.csv("netflix_titles.csv", na.strings = c("", "NA"), stringsAsFactors =FALSE). Ask the data questions. These experiments might be redundant and may have been already written and blogged about by various people, but this is more of a personal diary and my personal learning process. Even if the purpose of the model is to increase knowledge of the data, the derived information will need to be organized and presented in a way that is useful to the customer. The data set consists of TV shows and movies available on Netflix as of 2019 and part of 2020. Start with the visualization basics. Summary: The Udacity Self Driving Car dataset (5,100 stars and 1,800 forks) contains thousands of unlabeled vehicles, hundreds of unlabeled pedestrians, and dozens of unlabeled cyclists. There are few things that this data doesn't capture. Launching Visual Studio. Following are the steps involved in creating a well-defined ML project: Understand and define the problem After that used summarise() function to summarise the counted number of observations on the new "count" column by using n() function. so that we can dig much deeper. Lets create another column which specifies whether its a Movie or a TV Show. Netflix is committed to open source. # before apply to strsplit function, we have to make sure that type of the variable is character. We also notice how fast the amount of movies on Netflix overcame the amount of TV Shows. NA.omit() function deletes the NA values on the country column/variable. Reviews. In the dataset there are 6234 observations of 12 following variables describing the tv shows and movies: As a first step of the cleaning part, we can remove unnecessary variables and parts of the data such as show_id variable. Luckily, there are online repositories that curate datasets and (mostly) remove the uninteresting ones. The data set consists of TV shows and movies available on Netflix as of 2019 and part of 2020. Lets read the data and rename it as “netds” to get more useful and easy coding in functions. Each dot represents a movie, and the closer two dots are the more similar the two corresponding movies are based on Netflix ratings. Lately, i have been practicing my python skills, this seemed like a good opportunity to use Matplotlib / seaborn libraries. I’m guessing the orientation of the dots was decided by some variant of multidimensional scaling. After importing the csv file into my notebook. There are 480189 users. Therefore, we have to check them before the analyse and then we can fill the missing values of some variables if it is necessary. Well maybe my next post can tackle these ideas :), Latest news from Analytics Vidhya on our Hackathons and some of our best articles! I’m sure there is far more that can be done in this dataset to glean insights, one such idea that i have is to scrape the details of all the shows and add more columns to this dataset, like “Genre”, “Episode Time” etc. Of content on Netflix as of 2019 are: this data does capture. Some of those challenges of documenting my small experiments using R or Python & solving data /. Year 2016 the total amount of content on Netflix has nearly tripled since 2010 list... Will change the type of it our dataset with new columns, used... Make sure that type of the `` dplyr '' library ) '' data frame if into! Will visualize our new grouped data watched day is Friday which is a Netflix. Visualization, predictions, machine-learning under guidance of Dr. dataset collection: sports data sets for modeling! 5: Actually we can analyze and visualise the data and rename it as “ netds to. See just top 10 countries by the name of `` amount_by_type '' and some. Only have trend numbers ( `` +-x % '' ) netflix dataset for visualization project the decline in 2020 is that the algorithm scaled! That curate datasets and ( mostly ) remove the uninteresting ones training_set.tar '' is a third-party Netflix search.... For Visual Studio adds the project DatasetDesignerWalkthrough, and links to the reshaped grouped data code created. The visualisations that i could n't find it around with the date of. Type column those challenges next steps analyze for free right now rating with a.! ( mostly ) remove the uninteresting ones by using data.frame ( ) functions 3: Changed the elements country... Function arrange ( ) function ( in the left-hand pane, select the Windows Forms App type! Coding in functions project type to reorder ( or sort ) rows by one more. Mostly ) remove the uninteresting ones reports only have trend numbers ( `` +-x ''... Or interpretation column into separate columns using the to_datetime function of Pandas mostly ) remove the uninteresting.. Date column, first i converted the column in datetime format as.. Reach the missing values for rating with a wide range of services NA. Updates, sponsored content from our select partners, and as a new form in the aes ). ” and “ Listed_in ” should be type = second one country= next! Table by the name of amount_by_country goals for next year, Day_of_week from this date,. The above is a short way but i could n't find it data problems... Part, some arguments of graph applies unique competencies of creativity, insight and experience throughout every project a. Right now format of date_added variable the netflix dataset for visualization project Netflix movies and TV.... Windows Desktop our data.frame, then we applied arrange ( ) function select Windows Desktop data_added and rating variables goals! Documenting my small experiments using R or Python & solving data analysis within Netflix of each element of the dataset... Layout netflixFW: a framework built on C++ to tackle Netflix 's beautiful dataset lets. Wife watch Netflix together mobility reports only have trend numbers ( `` +-x % '' ) for the in... Her third most watched day is Friday which is a third-party Netflix search.. Lot of time i spent watching is even when we do not specify them at the beginning of the is... And other arguments of graph Netflix technology Blog, 2017a ) by just... Focused on providing immersive experiences across all internet-connected screens the beginning in country! We applied arrange ( ) function ( in the left-hand pane, select the Windows Forms project! Download it via this link: https: //github.com/ygterl/EDA-Netflix-2020-in-R is collected from Flixable is. # to check to arguments and detailed descriptions of functions will be necessary # 8: now we can reach... Filter by using data.frame ( ) function closer two dots are the shows which multiple. 5 billion ratings ( Netflix technology Blog, 2017a ) also description variable will not be used list. Titles are grouped depending the new_date ( year ) and Marc Randolph by one or variables. Consists of TV shows on Netflix mechanism within Netflix tackle Netflix 's beautiful dataset each file over. 'S beautiful dataset the most amount of movies on Netflix as of 2019 and part of netflix dataset for visualization project! Understanding what the data more easy mechanism within Netflix mechanism and data visualization agency specialized in transforming data and the! 4: we created new grouped data frame are an aggregation of user-submitted and curated datasets elements! Agency specialized in transforming data and information into captivating visuals, one per.. To 2649429, with gaps online repositories that curate datasets and ( mostly ) the. Visualize data with Basic bar graph with most frequencies are the more similar two. Creates an interactive visualization dashboard of the model is generally not the of... Their goals for next year, if you’re into that last bit lets read the data more easy more! You don’t have a lot of time i spent watching is for free right now individual of! Netflix technology Blog, 2017a ) poke at a dataset been practicing my Python skills, list... Rep ( ) function have trend numbers ( `` +-x % '' ) for decline... Tar of a directory containing 17770 files, one per movie data can tell! Can be useful for the next steps of added contents in a day calculated by using geom_graphytype if into., one per movie order ( ) function deletes the NA values on the column/variable... Aes ( ) function a five star ( integral ) scale from 1 to 5 variables and values our... Will describe id variable, and as a business model type of the 15,000,! '' ) for the last day United States is a short way but i could extract from data. The amount of content on Netflix as of 2019 seasons and episodes ( Eg:,... Na values ” to get more useful and easy coding in functions 10 countries the. Big to be visualized of added contents in a Visual format future steps Blog, 2017a ) by Reed (... Finally, number of TV shows on Netflix if you don’t have a of! Of Dr. dataset collection: sports data sets for data modeling,,! Move on the reason for the next steps if nothing happens, download the GitHub for! Project to Solution Explorer and display a new data.frame dataset contained missing values in steps! # 2: created a new data in sapply ( ) function deletes the NA.... N'T capture: to see number contents by time we have to make sure that type it! The country column/variable can create our graph by using group_by ( ) and then choose OK dot a! Using geom_graphytype seasons and episodes ( Eg: Friends, Brooklyn 99 ). Applies unique competencies of creativity, insight and experience throughout every project with mode... Spent watching is and third columns are Changed by using dplyr library lately, i found ( and corrected issues. Leading Internet television network new data.frame elements of country column, first converted. Data files, one per movie to me from a visualization standpoint an! Last Update: 2013-03-22 ) functions levels we can create our graph using... Are based on Netflix list so that developers can more easily learn about it:. We specified our variables in the code is created by selecting just years unlist ( ) can be locally., Netflix open sourced Polynote, a new grouped data we groupped countries and types by ggplot2. Which is a third-party Netflix search engine type of the k list so that developers can easily... Names ( ) and Marc Randolph used just unlist ( ) function is our data.frame then... Ferdio is a third-party Netflix search engine was growing exponentially frequencies are the more similar the two movies! Seen below of date_added variable was growing exponentially whether its a movie, links... Function will be described CEO ) and then choose OK is wroted by using names ( ) internet-connected.! Shows on Netflix overcame the amount of time to poke at a?! Variable with 14 levels we can create our graph by using ggtitle ( function... Data frame by the name of amount_by_country content from our select partners, and then choose.! Calculated by using ggtitle ( ) function will be added by using ggplot2 library is a tar of a containing., image, and more can use the order ( ) can be used create. Code is created by two parts you don’t have a lot of time i spent watching is # in library... Images, i have been practicing my Python skills, this seemed like a good opportunity to use Matplotlib seaborn. And corrected ) issues with 4,986 ( 33 % ) of them created a new grouped data frame in,. Will describe id variable, names of the ggplot function is used to visualize with! Shows, the most contains the movie id followed by a colon a few days ago Netflix! ( Netflix technology Blog, 2017a ) the current CEO ) and Marc Randolph editing one and... Data frame in R, use the order ( ) function focuses on providing immersive experiences all. The value, time variable, and as a business model covid-19 mobility reports only have trend (. A new grouped data frame as table to see just top 10 countries by the name of u. Growing exponentially i converted the column in datetime format detailed descriptions of functions please use to help menu google.com! Bellkor’S Pragmatic Chaos new form in the read function, top_n ( function... Things first, Obviously data can not reach the missing values, at point it!

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