Dataset

This dataset was created by Alireza Ahmadihesar

Released under CC0: Public Domain

Contents

This project focuses on exploring and analyzing the most popular datasets available on Kaggle. By delving into these datasets, we aim to identify key trends, understand user preferences, and highlight the topics that drive engagement within the data science and machine learning communities

Also there are interesting charts and analytics in the attached notebook

Dataset

This dataset was created by valerie lucro

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Dataset

This dataset was created by v1nor1

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Dataset

This dataset was created by M Shaheena

Released under Apache 2.0

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Dataset

This dataset was created by Masab001

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Dataset

This dataset was created by Abbad Alam

Released under Apache 2.0

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Dataset

This dataset was created by Zheung Yik2024

Released under Apache 2.0

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Dataset

This dataset was created by Danh_Anh

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Dataset

This dataset was created by Chekhova Nadezhda

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Dataset

This dataset was created by Mohammad Mehdi

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Dataset

This dataset was created by Rohan Bhopale

Released under Apache 2.0

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This dataset contains a snapshot of the official Kaggle datasets leaderboard (taken between October 21st and October 24th 2024). For every user, the dataframe contains all their datasets with information sourced through the Kaggle API. Currently, the dataset only contains the top 250, but I have a larger snapshot of the leaderboard. I aim to expand the dataset to include the top 1000 dataset contributors.

Dataset

This dataset was created by Marcos Faria

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This Data has been fetched from the LinkedIn API.

The 'post.csv' file provides all the details about a post made on LinkedIn by a certain user, *All details in file.

The 'comments.csv' file describes all the information about comments on the post and other details.

This is my first time making a dataset, so please ignore any discrepancies and please suggest on how I can improve on it.

Thanks, and happy kaggling.

Dataset

This dataset was created by Thanh Minh Nguyễn Lê

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Cleaned Meta-Kaggle Dataset

The Original Dataset - Meta-Kaggle

Explore our public data on competitions, datasets, kernels (code / notebooks) and more Meta Kaggle may not be the Rosetta Stone of data science, but we do think there's a lot to learn (and plenty of fun to be had) from this collection of rich data about Kaggle’s community and activity.

Strategizing to become a Competitions Grandmaster? Wondering who, where, and what goes into a winning team? Choosing evaluation metrics for your next data science project? The kernels published using this data can help. We also hope they'll spark some lively Kaggler conversations and be a useful resource for the larger data science community.

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This dataset is made available as CSV files through Kaggle Kernels. It contains tables on public activity from Competitions, Datasets, Kernels, Discussions, and more. The tables are updated daily.

Please note: This data is not a complete dump of our database. Rows, columns, and tables have been filtered out and transformed.

August 2023 update

In August 2023, we released Meta Kaggle for Code, a companion to Meta Kaggle containing public, Apache 2.0 licensed notebook data. View the dataset and instructions for how to join it with Meta Kaggle here

We also updated the license on Meta Kaggle from CC-BY-NC-SA to Apache 2.0.

The Problems with the Original Dataset

• The original dataset is 32 CSV files, with 268 colums and 7GB of compressed data. Having so many tables and columns makes it hard to understand the data.
• The data is not normalized, so when you join tables you get a lot of errors.
• Some values refer to non-existing values in other tables. For example, the UserId column in the ForumMessages table has values that do not exist in the Users table.
• There are missing values.
• There are duplicate values.
• There are values that are not valid. For example, Ids that are not positive integers.
• The date and time columns are not in the right format.
• Some columns only have the same value for all rows, so they are not useful.
• The boolean columns have string values True or False.
• Incorrect values for the Total columns. For example, the DatasetCount is not the total number of datasets with the Tag according to the DatasetTags table.
• Users upvote their own messages.

The Solution

• To handle so many tables and columns I use a relational database. I use MySQL, but you can use any relational database.
• The steps to create the database are:
• Creating the database tables with the right data types and constraints. I do that by running the db_abd_create_tables.sql script.
• Downloading the CSV files from Kaggle using the Kaggle API.
• Cleaning the data using pandas. I do that by running the clean_data.py script. The script does the following steps for each table:
  • Drops the columns that are not needed.
  • Converts each column to the right data type.
  • Replaces foreign keys that do not exist with NULL.
  • Replaces some of the missing values with default values.
  • Removes rows where there are missing values in the primary key/not null columns.
  • Removes duplicate rows.
• Loading the data into the database using the LOAD DATA INFILE command.
• Checks that the number of rows in the database tables is the same as the number of rows in the CSV files.
• Adds foreign key constraints to the database tables. I do that by running the add_foreign_keys.sql script.
• Update the Total columns in the database tables. I do that by running the update_totals.sql script.
• Backup the database.

Dataset

This dataset was created by Kunal Kumar Sahoo

Released under CC0: Public Domain

Contents

This dataset provides a curated collection of freely available APIs covering a wide range of categories. Whether you are a developer, data enthusiast, or just someone interested in exploring various API services, this dataset offers a valuable resource to help you discover, access, and understand these APIs.

Each entry in the dataset includes essential information about the APIs, such as the API name, a brief description of its functionality, authentication requirements, HTTPS support, and a link to the API's documentation or endpoint. The dataset is categorized to facilitate easy exploration and access to APIs across different domains.

Example entries:

"AdoptAPet": A resource to help get pets adopted, requiring an API key for access. "Axolotl": A collection of axolotl pictures and facts, with HTTPS support and no authentication required. "Cat Facts": Providing daily cat facts, with HTTPS support and no authentication needed.

Columns: API: This column provides the name or title of the API

Description: In this column, you'll find a brief description of the API's functionality and what it offers.

Auth (Authentication): This column indicates whether the API requires authentication for access. If it specifies "apiKey" or any other form of authentication, users need to provide valid credentials or keys to utilize the API.

HTTPS: This column indicates whether the API supports secure communication over HTTPS

Link: This column provides a URL or link to the API's documentation

Category: The "Category" column categorizes the API into a relevant domain or topic.

MyAnimeList API Download

This is the dataset that I created as part of the Google Data Analytics Professional Certificate capstone project. The MyAnimeList website has a vast repository of ratings and rankings of viewership data that could be used for various methods. I extracted several datasets from the detail API from MyAnimeList (MAL) https://myanimelist.net/apiconfig/references/api/v2 and plan to potentially update data every two weeks.

Many possible uses for this data could be tracking what anime viewers are watching most within a particular time period, what's being scored (out of 10) well and what isn't.

My viz for this data will be part of a tableau dashboard located here. This dashboard allows fans to explore the dataset and locate top scored or popular titles by genre, time period, and demographic (although this field isn't always entered)

Documentation

The extraction and cleaning process is outlined on github here.

Frequency of Updates

I plan on updating this potentially every 2 weeks, this depends on my availability and the interest in this dataset.

Caveats

Extracting and loading this data involved some transformations that should be noted:

• This data only includes titles that correspond with the "tv" ranking category. This was in an effort to streamline extraction and fine tune the analysis. If you would like to see other categories you are welcome to suggest it as an enhancement or use the code create your own dataset. As a result of subsetting on "tv", the dataset excludes the following ranking categories:
  1. All
  2. airing
  3. upcoming
  4. ova
  5. movie
  6. special
  7. bypopularity
  8. favorite
• Adult content - This extract excludes all adult content (r+).
• Note: The previous two points are valid for all tables with the exception of the rank_table. This is the table that was used as a starting point to obtain all MAL ids that were associated with "tv". Because this is a fast download, all categories are included in this table.
• The creation of the alternative_title field in the anime_table. This uses the english version of the name unless it is null, if the value is null, it uses the default name. This was in an effort to make the title accessible to english speakers. The original title field can be used if desired.
• The extraction of the demographic information from the genres field. MyAnimeList includes demographic information (shounen, seinen etc.) in the genres field. I've extracted it so that it could be used as its own field. However, many of those fields are null making it somewhat difficult to use.
• Cleaning processes of data. Various methods of cleaning data have been carried out and are noted on github.
• start_season.year - this field in the anime_table has been modified for null values. If there are null values, the first four characters from the start_date have been used. I will continue to use this method as long as it is viable.

Table Structure

The primary keys in all of the tables (with the exclusion of the tm_ky table) are foreign keys to other tables. As a result, the tables have 2 or more primary keys.

1. anime_demo_table

Field	Type	Primary Key
tm_ky	int	PK
mal_id	int	PK
demo_id	int

1. anime_genres_table

Field	Type	Primary Key
tm_ky	int	PK
mal_id	int	PK
genres_id	int	PK

1. anime_ranking_table

Field	Type	Primary Key
tm_ky	int	PK
mal_id	int	PK
mean	dbl
rank	int
popularity	int
num_scoring_users	int
statistics.watching	int
statistics.completed	int
statistics.on_hold	int
statistics.dropped	int
statistics.plan_to_watch	int
statistics.num_scoring_users	int

1. anime_studios_table

Field	Type	Primary Key
tm_ky	int	PK
mal_id	int	PK
studio_id	int	PK

1. anime_syn_table

Field	Type	Primary Key
tm_ky	int	PK
mal_id	int	PK
synonyms	chr

1. anime_table

Field	Type	Primary Key
tm_ky	int	PK
mal_id	int	PK
title	chr
main_picture.medium	chr
main_picture.large	chr
alternative_titles.en	chr
alternative_titles.ja	chr
start_date	chr
end_date	chr
synopsis	chr
media_type	chr
status	chr
num_episodes	int
start_season.year	int
start_season.season	chr
rating	chr
nsfw	chr
demo_de	chr ...