Comprehensive dataset covering Amazon Prime availability across 27 countries, including launch dates, pricing, and regional benefit differences

Context

This data set was created so as to analyze the latest shows available on Amazon Prime as well as the shows with a high rating.

Content

The data set contains the name of the show or title, year of the release which is the year in which the show was released or went on-air, No.of seasons means the number of seasons of the show which are available on Prime, Language is for the audio language of the show and does not take into consideration the language of the subtitles, genre of the show like Kids, Drama, Action and so on, IMDB ratings of the show: though for many tv shows and kid shows the rating was not available, Age of Viewers is to specify the age of the target audience- All in age means that the content is not restricted to any particular age group and all audiences can view it.

Acknowledgements

I have collected this data from Amazon Prime's Website.

Inspiration

Since a lot many TV shows have high IMDB ratings but don't get viewed that much because the audience is not aware of it or it is not advertised much. I have created this data set so as to find out the highest-rated shows in each category or in a particular genre.

Analyzing streaming service prices allows consumers to make informed decisions based on their budget, ensuring they get the best value for their entertainment preferences. This dataset contains price history since 2011 for major streaming services: Netflix, Amazon Prime Video, Hulu, Disney+, HBO Max, Apple TV+, Peacock, Paramount+, Shudder, Crunchyroll.

All prices are for ad-free, lowest-cost monthly subscriptions.

For use case and analysis reference, please take a look at the Streaming Service Prices Study notebook.

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To standardize, all prices follow the below condition. - U.S. price. - Lowest cost. - No ads. - No bundle.

With 92 percent, Netflix had the highest brand awareness among VOD services in the United States, followed by Hulu and Amazon Prime Video, according to a survey from 2025.For this study, brand awareness was surveyed employing the concept of aided brand recognition, showing respondents both the brand's logo and the written brand name.

About Dataset

Edit This dataset contains information about a collection of movies across multiple genres, including Comedy, Romance, Thriller, and Drama. Each record in the dataset includes the following attributes:

Title: The name of the movie. IMDb Rating: The IMDb rating of the movie, a measure of its popularity and quality, based on user reviews. Release Year: The year the movie was released. Duration: The length of the movie in minutes. Genre: The genre or category of the movie, such as Comedy, Drama, Thriller, or Romance. The dataset covers movies spanning various genres and time periods, offering insights into movie ratings, durations, and genres. The data could be used for analysis in areas such as movie recommendations, trends in genre popularity, or the correlation between movie length and user ratings.

Here’s a detailed description of each column in your dataset:

Title: Description: This column contains the name of the movie. It serves as a unique identifier for each movie in the dataset. The titles represent a wide range of films from various genres and periods.

IMDb Rating: Description: This column represents the IMDb rating of each movie, which is a score given by users on the IMDb platform. The rating is typically out of 10 and reflects the overall user perception of the movie, including aspects such as storytelling, acting, direction, and entertainment value. Higher ratings generally indicate better reception by audiences.

Release Year: Description: This column indicates the year when the movie was officially released. It provides a temporal context for each movie, helping users understand when the movie was made and the era it belongs to. This can be useful for analyzing trends in the movie industry over time.

Duration: Description: This column contains the duration of each movie, measured in minutes. It indicates how long the movie runs from start to finish. This data is important for understanding the length of films, which can be a factor in viewers' preferences and movie industry trends.

Genre: Description: This column categorizes each movie based on its genre, such as Comedy, Drama, Kids Movies, or Romance. The genre provides insights into the movie's thematic focus and target audience. Genres help classify movies into broad categories, allowing for analysis of trends in different movie types over time.

Content

Amazon Prime Video is an OTT platform streaming movies and TV shows. This dataset contains a list of all the movies streaming on the platform in India.

Columns are as follows

• Movie Name: Name of the Movie
• Language: Audio language of the Movie (some movies are dubbed in multiple languages)
• IMDb Rating: The rating a movie received in the IMDb Platform
• Running Time: Total running time of the movie in hours and minutes
• Year of Release: The year in which a movie was released in the theatres or on Amazon Prime for Amazon Prime Originals Movies
• Maturing Rating: Specifies the target audience.
  • All: Appropriate for Kids
  • 7+: Appropriate for Older Kids
  • 13+: Appropriate for teens
  • 16+: Appropriate for Young Adults
  • 18+: Appropriate for Adults
  • NR: Unrated
• Plot: This column contains a short summary of the movie's plot.

Acknowledgements

This dataset has been scraped from the Amazon Prime Video Website. For movies that don't have IMDb Rating on the website, the rating has been collected using the IMDbPY python package.

Inspiration

Amazon Prime Video is one of the largest OTT platforms. Using this dataset we can find answers to some interesting questions like, 1. Understanding the quality of the movies streaming on the platform 2. Identifying similar plot of the movies 3. Finding out why movies are rated high or low based on the plot 4. Analysing how the running time of movies have changed over the years in different languages

Other Platform's Datasets (Click on the logos to view)

![alt text][1] ![alt text][3] ![alt text][5] ![alt text][7] [1]: https://i.imgur.com/As0PMcL.jpg =75x20

[3]: https://i.imgur.com/r5t3MpQ.jpg =75x20

[5]: https://i.imgur.com/4a4ZMuy.png =75x30

[7]: https://i.imgur.com/nCL8Skc.png?1 =75x32

• Amazon Prime Video Movies and TV Shows
• Disney+ Movies and TV Shows
• Netflix Prime Video Movies and TV Shows
• Hulu Movies and TV Shows

Netflix Movies and TV Shows

About this Dataset: Netflix is one of the most popular media and video streaming platforms. They have over 8000 movies or tv shows available on their platform, as of mid-2021, they have over 200M Subscribers globally. This tabular dataset consists of listings of all the movies and tv shows available on Netflix, along with details such as - cast, directors, ratings, release year, duration, etc.

Featured Notebooks: Click Here to View Featured Notebooks Milestone: Oct 18th, 2021: Most Upvoted Dataset on Kaggle by an Individual Contributor

Interesting Task Ideas

1. Understanding what content is available in different countries
2. Identifying similar content by matching text-based features
3. Network analysis of Actors / Directors and find interesting insights
4. Does Netflix has more focus on TV Shows than movies in recent years.

Check my Other Datasets

When it comes to the most well-known digital music providers in the U.S., Spotify leads the list ahead of Pandora. Both these platforms are recognized by 89 percent and 86 percent of the respondents in the United States respectively. Third on this list comes YouTube Music, followed by iTunes and Amazon Music.

For this study, brand awareness was surveyed employing the concept of aided brand recognition, showing respondents both the brand's logo and the written brand name.

Interested in more detailed results covering all brands of this ranking and many more? Explore GCS Brand Profiles. These statistics show results of the Brand KPI survey.

Video live streaming is gaining prevalence among video streaming services, especially for the delivery of popular sporting events. The quality of these live streaming videos can be adversely affected by any of a wide variety of events,including poor network connections, capture artifacts, and distortions incurred during coding and transmission. Because of this, the development of objective Video Quality Assessment (VQA) algorithms that can predict the perceptual quality of videos have become important sources of feedback, monitoring, and control of video streaming. Important resources for developing these algorithms are appropriate databases that exemplify the kinds of live streaming video distortions encountered in practice. Towards making progress in this direction, we built a video quality database specifically designed for live streaming VQA research. The new video database is called the Laboratory for Image and Video Engineering - Amazon Prime Video (APV) Live Video Streaming Database (LIVE-APV). We envision that researchers will find the dataset to be useful for the development, testing, and comparison of future VQA models.

Representative applications that can directly collect 5G da-tasets from mobile terminals without using specialized equipment include G-NetTrack Pro and PCAPdroid. The for-mer allows for the monitoring and logging of the header and payload information of the medium access control (MAC) frame passing through the 5G air interface. The latter is an open-source network capture and monitoring tool that works without root privileges, analyzing connections made by ap-plications installed on the user's mobile device. The latter can also dump mobile traffic to PCAP (also known as libpcap) and send it to the well-known Wireshark for further analysis. We created 5G datasets by measuring 5G traffic directly from a major mobile operator in South Korea. The model name of the mobile terminal used for traffic measurement is the Samsung Galaxy A90 5G, and it was equipped with a Qualcomm Snapdragon X50 5G modem. The packet sniffer software used for traffic measurement, PCAPdroid, was in-stalled in the terminal through Google play. Traffic was measured sequentially per application on two stationary ter-minals (only one terminal was used for non-interactive ser-vices) with no background traffic. The collected dataset is representative resource-intensive video traffic that has the greatest impact on 5G network planning and provisioning, and background traffic was not mixed to measure the unique characteristics of each type of traffic. The video streaming dataset includes data directly meas-ured while watching Netflix and Amazon Prime, which are representative over-the-top (OTT) services, on mobile devic-es. The live streaming dataset was measured while watching YouTube Live and South Korea's representative live broad-casts (Naver NOW and Afreeca TV). Video conferencing data were measured by holding an actual meeting on the widely used Zoom, MS Teams, and Google Meet platform. Two types of metaverse traffic were acquired: Zepeto and Roblox. Zepeto traffic was collected while staying in the 'camping world' for 15 hours. Roblox traffic was collected over 25 hours of playing the 'Collect All Pets' game using an auto clicker. We collected two types of mobile network gaming traffic. The first was cloud gaming, an online game setup that runs video games on remote servers and streams them direct-ly to the user's device. The second was a traditional mobile game connected to the Internet. The dataset was collected from May to October 2022, is a massive 328 hours in total, and is provided in the csv file format. The dataset we collected is a timestamp-mapped time series dataset with packet header information, and traffic analysis by application is possible because it includes source and destination addresses. To make it more usable as a traffic source model, Section III describes how to use it as a training dataset for the traffic simulator platform's source generator.

A 5G traffic dataset measured by PCAPdroid has been re-leased and can be used as a training dataset for various ML models. However, since the size of this dataset is very large, it is inconvenient to handle, and additional data preprocessing is required to use it for its intended purpose.

This data set can be used to learn GANs, time-series forcasting deep learning models.

Our implementation is given on GitHub. https://github.com/0913ktg/5G-Traffic-Generator

Context

Recommendation systems are used everywhere now a days. Netflix , Amazon Prime , YouTube , Online shopping sites etc. Datasets like this are great way to start working on Recommendation system. The Dataset was created from the official API provied by TMDB

Content

What's inside is more than just rows and columns. This is the dataset for 10000 Popular movies based on the TMDB ratings. Ideal database to start off with Recommendation algorithms.

Origin

The code which was used to extract this dataset can be found here - Creating Dataset of top 10000 popular movies

Update

Added Overview , Revenue , Runtime, tagline column for each movie.

Context

Recommendation systems are used everywhere nowadays. Netflix, Amazon Prime, YouTube, Online shopping sites, etc. Datasets like this are a great way to start working on a Recommendation system. The Dataset was created from the official API provided by TMDB.

Content

What's inside is more than just rows and columns. This is the dataset for 10,000 Popular movies based on the TMDB ratings. Ideal database to start off with Recommendation algorithms.

Columns in the file include :

• id
• original_language
• original_title
• overview
• popularity
• release_date
• title
• vote_average
• vote_count # Inspiration This dataset was assembled as part of a semester Project in Data Science Career Track. I wanted to perform an extensive EDA on Movie Data to narrate the history and the story of Cinema and use this metadata to build various types of Recommender Systems.

Some of the things you can do with this dataset: Predicting movie revenue and/or movie success based on a certain metric. What movies tend to get higher vote counts and vote averages on TMDB? Building Content-Based and Collaborative Filtering Based Recommendation Engines.

Acknowledgments

This dataset was generated from The Movie Database API. This product uses the TMDb API but is not endorsed or certified by TMDb. Their API also provides access to data on many additional movies, actors and actresses, crew members, and TV shows. You can try it for yourself here.