Context

This dataset belongs to the app Clash Of Clans available on the Google Play Store. The Dataset mostly has user reviews and the various comments made by the users.

Content

The content of the various columns is listed below. Please find the description for each column.

userName: Name of a User, userImage: Profile Image that a user has content: This represents the comments made by a user score: 5, thumbsUpCount: Number of Thumbs up received by a person reviewCreatedVersion: Version number on which the review is created at: Created At replyContent: Reply to the comment by the Company repliedAt: Date and time of the above reply reviewId: unique identifier

Acknowledgements

Banner image - Supercell

Context

I've been recently exploring Microsoft Azure and have been playing this game for the past 4 or so years. I am also a software developer by profession. I did a simple pipeline that gets data from the official Clash Royale API using (Python) Jupyter Notebooks and Azure VMs. I tried searching for public Clash Royale datasets, but the ones I saw don't quite have that much data from my perspective, so I decided to create one for the whole community.

I started pulling in the data at the beginning of the month of December until season 18 ended. This covers the season reset last December 07, and the latest balance changes last December 09. This dataset also contains ladder data for the new Legendary card Mother Witch.

The amount of data that I have, with the latest dataset, has ballooned to around 37.9 M distinct/ unique ladder matches that were (pseudo) randomly being pulled from a pool of 300k+ clans. If you think that this is A LOT, this could only be a percent of a percent (even lower) of the real amount of ladder battle data. It still may not reflect the whole population, also, the majority of my data are matches between players of 4000 trophies or more.

I don't see any reason for me not to share this to the public as the data is now considerably large that working on it and producing insights will take more than just a few hours of "hobby" time to do.

Feel free to use it on your own research and analysis, but don't forget to credit me.

Also, please don't monetize this dataset.

Stay safe. Stay healthy.

Happy holidays!

Content

Card Ids Master List is in the discussion, I also created a simple notebook to load the data and made a sample n=20 rows, so you can get an idea on what the fields are.

Inspiration

With this data, the following can possibly be answered 1. Which cards are the strongest? The weakest? 2. Which win-con is the most winning? 3. Which cards are always with a specific win-con? 4. When 2 opposing players are using maxed decks, which win-con is the most winning? 5. Most widely used cards? Win-Cons? 6. What are the different metas in different arenas and trophy ranges? 7. Is ladder matchmaking algorithm rigged? (MOST CONTROVERSIAL)

(and many more)

Implementation

I have 2 VMs running a total of 14 processes, and for each of these processes, I've divided a pool of 300k+ clans into the same number of groups. This went on 24/7, non-stop for the whole season. Each process will then randomize the list of clans it is assigned to and will iterate through each clan, and get that clan's members' ladder data. It is important to note that I also have a pool of 470 hand-picked clans that I always get data from, as these clans were the starting point that eventually enabled me to get the 300k+ clans. There are clans who have minimal ladder data, there are some clans who have A LOT.

To prevent out of memory exceptions, as my VMs are not really that powerful (I'm using Azure free credits), I've put on a time and limit of battles extracted per member.

My Clan and Handle

My account: https://royaleapi.com/player/89L2CLRP My clan: https://royaleapi.com/clan/J898GQ

Acknowledgements

Thank you to SUPERCELL for creating this FREEMIUM game that has tested countless people's patience, as well as the durability of countless mobile devices after being smashed against a wall, and thrown on the floor.

Thank you to Microsoft for Azure and free monthly credits

Thank you to Python and Jupyter notebooks.

Thank you Kaggle for hosting this dataset.

The TMDb (The Movie Database) is a comprehensive movie database that provides information about movies, including details like titles, ratings, release dates, revenue, genres, and much more.

This dataset contains a collection of 1,000,000 movies from the TMDB database.

Dataset is updated daily. If you find this dataset valuable, don't forget to hit the upvote button! 😊💝

Interesting Task Ideas:

1. Predict movie ratings based on features such as revenue, popularity, genre, and runtime.
2. Identify trends in movie release dates and analyze their impact on revenue.
3. Analyze the relationship between budget, revenue, and popularity to determine factors that contribute to a movie's success.
4. Build a recommendation system that suggests similar movies based on genres, production companies, and language.
5. Perform sentiment analysis on movie reviews to understand audience reactions.
6. Explore the impact of movie genres on popularity and revenue.
7. Investigate the correlation between runtime and audience engagement.
8. Identify successful production companies and analyze their strategies.
9. Utilize natural language processing techniques to extract meaningful insights from movie overviews.
10. Visualize movie popularity over time and identify popular genres in different periods.

Checkout my other datasets

Clash of Clans Clans Dataset 2023 (3.5M Clans)

Black-White Wage Gap in the USA Dataset

130K Kindle Books

USA Unemployment Rates by Demographics & Race

150K TMDb TV Shows

Photo by Onur Binay on Unsplash

Overview

The mobile games industry is worth billions of dollars, with companies spending vast amounts of money on the development and marketing of these games to an equally large market. Using this data set, insights can be gained into a sub-market of this market, strategy games. This sub-market includes titles such as Clash of Clans, Plants vs Zombies and Pokemon GO.

Background

This is the data of 17007 strategy games on the Apple App Store. It was collected on the 3rd of August 2019, using the iTunes API and the App Store sitemap.

Some ideas

You could use the number of ratings as a proxy indicator for the overall success of a game, and then work out what factors make a successful game. Or you could measure the state of the market over time and try predict where it is headed. And I think an analysis of the icons of the apps would be pretty cool.