This dataset contains information about top 1000 IMDB movies, including their titles, certificates, durations, genres, IMDb ratings, Metascores, directors, cast members, the number of votes they received, grossed earnings, and plot summaries. The data is a curated list of highly acclaimed and popular movies.

Columns/Variables:

Movie Name: The title of the movie. Certificate: The certificate or rating assigned to the movie. Duration: The duration of the movie in minutes. Genre: The genre(s) to which the movie belongs. IMDb Rating: The IMDb rating of the movie. Metascore: The Metascore rating of the movie. Director: The director of the movie. Stars: The main cast members of the movie. Votes: The number of user votes/ratings the movie has received. Grossed in $: The gross earnings in dollars (if available). Plot: A brief summary or plot description of the movie. Size: The dataset contains 1000 rows and 11 columns.

Data Quality: The dataset appears to be well-structured and complete. There are no missing values, and it seems to be ready for analysis.

Use Cases: This dataset can be used for various analyses, such as exploring the relationship between IMDb ratings and Metascores, identifying top-rated directors, or understanding the distribution of movie ratings across genres.

Cloud Database and DBaaS Market size was valued at USD 18.28 Billion in 2024 and is projected to reach USD 83.95 Billion by 2031, growing at a CAGR of 20.99% during the forecasted period 2024 to 2031.

The Cloud Database and Database as a Service (DBaaS) market is driven by the increasing adoption of cloud computing and big data analytics, as organizations seek scalable, flexible, and cost-effective data management solutions. The growing volume of unstructured data and the need for real-time data processing and analytics propel demand for cloud databases. Businesses' emphasis on reducing operational complexities and costs associated with traditional on-premise databases, coupled with the need for enhanced data security, disaster recovery, and compliance, further fuels market growth. The proliferation of IoT devices and the rise of AI and machine learning applications also contribute to the demand for robust cloud database solutions. Additionally, the trend towards digital transformation and the increasing reliance on remote work environments accentuate the need for reliable, accessible, and scalable database solutions.

Top rated movies

In this dataset there are almost 1980 rows of data that are highly rated movies and this data is collected and added some relational informative columns out of number of key, value pairs of data which got from the specific API endpoint about movies from tmdb api

BoxofficeMojo Alltime Domestic Data

Data scraped from BoxofficeMojo's listing of the lifetime gross, ranking and production year of hollywood movies. All is based on domestic gross (does NOT account for inflation).

Interactive dashboard to explore the data:

https://www.dashboardom.com/boxofficemojo

About the dashboard: https://www.slideshare.net/eliasdabbas/boxofficemojo-data-interactive-dashboard

Script to scrape the data and analyze words (absolute frequency vs weighted frequency) on DataCamp: https://www.datacamp.com/community/tutorials/absolute-weighted-word-frequency

Quick result of the analysis (April 2018): http://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1524577430/output_27_0_kujy3w.png" alt="">

Official Website of this dataset: https://betweenourworlds.org/

These Datasets are released in 2023. To find more older Datasets visit https://data.world/betweenourworlds

ABOUT Between Our Worlds provides a monthly Linked Open Dataset about anime series and movies. The dataset does not only incorporate over 14.000 Animes, but also their seasons, episodes, trailers (e.g., on YouTube), streams (e.g., from Netflix), and characters.

DETAILS Between Our Worlds is an initiative to provide metadata information about anime as Linked Open Data. Wait, what is this Linked Data thingy you speak of? Linked Data is a method of publishing structured data so that it can be interlinked and become more useful through semantic queries. This is something you can't do if you would, for example, store your data in a relational database. Furthermore, the word 'open' refers to the fact that the data is available under a free license.

The core contribution of our initiative is a Linked Open Dataset. It is currently available by downloading the RDF datadump or by quering our Triple Pattern Fragments server.

We cannot create our own tags in Kaggle. so, here are more detailed tags. TAGS: anime, episode, trailer, series, movie, character, stream, season

This dataset contains information about the age, height, weight, gender, and likeness of a group of people. The likeness column indicates whether the person likes biryani or samosa.

age: The age of the person in years. height: The height of the person in inches. weight: The weight of the person in pounds. gender: The gender of the person (male or female). likeness: The person's likeness of biryani or samosa (biryani or samosa). The dataset was created for the purpose of studying the relationship between age, height, weight, gender, and likeness of biryani or samosa. It can be used by researchers and data scientists to investigate these relationships and to develop models that can predict a person's likeness of biryani or samosa based on their age, height, weight, and gender.

The dataset is well-documented and easy to use. It is available for download on Kaggle.