According to the most recent data, U.S. viewers aged 15 years and older spent on average *** hours and ** minutes watching TV per day in 2024. Adults aged 75 and above spent the most time watching television at over **** hours, whilst 20 to 24-year-olds watched TV for less than *** hours each day. The dynamic TV landscape The way people consume video entertainment platforms has significantly changed in the past decade, with a forecast suggesting that the time spent watching traditional TV in the U.S. will probably decline in the years ahead, while digital video will gain in popularity. Younger age groups in particular tend to cut the cord and subscribe to video streaming services, such as Netflix, Hulu, and Amazon Prime Video. TV advertising in a transition period Similarly, the TV advertising market made a development away from traditional linear TV towards online media. While the ad spending on traditional TV in the U.S. generally increased until the end of the 2010s, this value is projected to decline to below ** billion U.S. dollars in the next few years. By contrast, investments in connected TV advertising are expected to steadily grow, despite the amount being just over half of the traditional TV ad spend by 2025.

This table contains 156 series, with data for years 1998 - 2004 (not all combinations necessarily have data for all years), and is no longer being released. This table contains data described by the following dimensions (Not all combinations are available): Geography (13 items: Canada;Newfoundland and Labrador;Prince Edward Island;Nova Scotia; ...), Sex (2 items: Males;Females), Age group (6 items: 18 years and over;18 to 24 years;25 to 34 years;35 to 49 years; ...).

In an era dominated by streaming services, Netflix stands as one of the undisputed giants in the world of entertainment. With its vast library of movies, TV shows, documentaries, and more, it has become a household name for millions of viewers worldwide. But what keeps us glued to the screens? These questions, among others, have prompted my classmates and me to delve deep into the world of Netflix.

Hence, this dataset showcases a diverse range of preferences and opinions, offering insights into the habits and experiences of Netflix users across various demographics.

Demographic: - Gender - Age

Netflix user's behaviour: - What types of content do you most often watch on Netflix? - How often do you watch Netflix? - How do you typically discover new content to watch on Netflix? - What devices do you primarily use to access Netflix? - How satisfied are you with the overall Netflix user interface and browsing experience? - Why is this rating?

Description:
This dataset contains detailed information about Netflix users, including their subscription behaviors, engagement metrics, and demographic details. It encompasses various attributes such as subscription length, customer satisfaction scores, daily watch time, preferred genres, devices used, regional distribution, payment history, and churn status. The data can be used to analyze user retention, identify factors influencing customer satisfaction, and explore trends in viewing habits across different regions and demographics. Key features include:

Customer ID: Unique identifier for each user.
Subscription Length (Months): Duration of the user's subscription.
Customer Satisfaction Score (110): Selfreported satisfaction level.
Daily Watch Time (Hours): Average hours spent watching content daily.
Engagement Rate (110): Metric indicating user interaction with the platform.
Device Used Most Often: Primary device for streaming (e.g., Smart TV, Mobile, Laptop).
Genre Preference: Favorite content genre (e.g., Action, Drama, Comedy).
Region: Geographic location of the user.
Payment History: Ontime or delayed payments.
Subscription Plan: Tier of service (Basic, Standard, Premium).
Churn Status (Yes/No): Whether the user has canceled their subscription.
Support Queries Logged: Number of customer support interactions.
Demographics: Age and monthly income.
Promotional Offers Used: Count of promotional offers utilized.
Number of Profiles Created: Profiles set up under the subscription.

This dataset is ideal for predictive modeling (e.g., churn prediction), customer segmentation, and market research to enhance user experience and business strategies for streaming platforms.

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Context

Anime is a popular form of entertainment originating from Japan. It encompasses a wide range of animated TV series, movies, and OVAs (original video animations) that cater to various genres and target audiences. Anime is known for its distinctive art style, compelling storytelling, and diverse themes. Anime covers a vast array of genres, including action, adventure, comedy, drama, romance, fantasy, sci-fi, and many more. Each genre offers unique storytelling elements and appeals to different preferences and interests among anime enthusiasts. It has gained significant popularity worldwide and has developed a dedicated and passionate fanbase. Fans of anime often engage in discussions, reviews, and rankings, contributing to the vibrant community surrounding this form of entertainment. Due to the vast number of anime titles available, recommendations play a crucial role in helping enthusiasts discover new shows that align with their interests. Recommendation systems leverage user ratings, genres, and other factors to suggest anime series that users might enjoy based on their preferences.

Content

------------------------------------------ "anime-dataset-2023.csv" ----------------------------------------------

• anime_id: Unique ID for each anime.
• Name: The name of the anime in its original language.
• English name: The English name of the anime.
• Other name: Native name or title of the anime(can be in Japanese, Chinese or Korean).
• Score: The score or rating given to the anime.
• Genres: The genres of the anime, separated by commas.
• Synopsis: A brief description or summary of the anime's plot.
• Type: The type of the anime (e.g., TV series, movie, OVA, etc.).
• Episodes: The number of episodes in the anime.
• Aired: The dates when the anime was aired.
• Premiered: The season and year when the anime premiered.
• Status: The status of the anime (e.g., Finished Airing, Currently Airing, etc.).
• Producers: The production companies or producers of the anime.
• Licensors: The licensors of the anime (e.g., streaming platforms).
• Studios: The animation studios that worked on the anime.
• Source: The source material of the anime (e.g., manga, light novel, original).
• Duration: The duration of each episode.
• Rating: The age rating of the anime.
• Rank: The rank of the anime based on popularity or other criteria.
• Popularity: The popularity rank of the anime.
• Favorites: The number of times the anime was marked as a favorite by users.
• Scored By: The number of users who scored the anime.
• Members: The number of members who have added the anime to their list on the platform.
• Image URL: The URL of the anime's image or poster.

The dataset offers valuable information for analyzing and comprehending the characteristics, ratings, popularity, and viewership of various anime shows. By utilizing this dataset, one can conduct a wide range of analyses, including identifying the highest-rated anime, exploring the most popular genres, examining the distribution of ratings, and gaining insights into viewer preferences and trends. Additionally, the dataset facilitates the creation of recommendation systems, time series analysis, and clustering to delve deeper into anime trends and user behavior.

--------------------------------------------- "users-details-2023.csv" ------------------------------------------------

• Mal ID: Unique ID for each user.
• Username: The username of the user.
• Gender: The gender of the user.
• Birthday: The birthday of the user (in ISO format).
• Location: The location or country of the user.
• Joined: The date when the user joined the platform (in ISO format).
• Days Watched: The total number of days the user has spent watching anime.
• Mean Score: The average score given by the user to the anime they have watched.
• Watching: The number of anime currently being watched by the user.
• Completed: The number of anime completed by the user.
• On Hold: The number of anime on hold by the user.
• Dropped: The number of anime dropped by the user.
• Plan to Watch: The number of anime the user plans to watch in the future.
• Total Entries: The total number of anime entries in the user's list.
• Rewatched: The number of anime rewatched by the user.
• Episodes Watched: The total number of episodes watched by the user.

The User Details Dataset provides valuable information for analyzing user behavior and preferences on the anime platform. By examining mean scores and anime genres, you can gain insights into user preferences. Users can be segmented into different gro...