Context

This datasets was created as a result of web scrapping using python tool called scrapy.

Content

This contains the user interacting with light novel sharing forum. Subject column represent subject of the forum post and almost every subject is a name of light novel they are sharing. Second column represent who created it. Third column shows that how many views got that light novel. Next column shows how many users have replied for that post. Next 2 columns show who post last on that post and when that last post made.

Inspiration

I hope this will unlock hidden secrets of light novel reading communities.

Context

The dataset is a collection of question-answer pairs gathered from game development forums. All data was web scraped using Scrapy framework and preprocessed by Apache Spark. All intents (questions) variations are synthetically generated using a paraphrasing AI.

The dataset does not contain any personal information about users on a forum. User IDs, post IDs, and mentions were anonymized by a preprocessing pipeline.

Content

The dataset is separated into two sections: - raw.json - unprocessed web scraped forum posts. Use this dataframe for an EDA researches. - intents.json - a cleaned and labeled collection of intents set and their corresponding answer. Use this dataframe for build domain-oriented chatbot.

Acknowledgements

The dataset was collected from Unity Answers Forum.

Inspiration

Use this dataset to train a domain-oriented chatbot, that provides answers to gaming-related questions. Combine it with dialogues dataframe to add a "chit-chat" intent to the bot. You can also perform other NLP/NLU related tasks, such as multilabel text classification of forum tags from the question input.

This file is for my postgraduate study. The data is concerned with the Coursera forum data of R Programming. All data has been anonymized for the purpose of data privacy.

The data scaped is dated from September 2018 to September 2019.

The above dataset consists of 20 rows and 5 columns that represent sales data for a company. Each row in the dataset represents a single sale, and the columns provide information about the sale, including:

Order ID: A unique identifier for each sale, assigned by the company. Product Name: The name of the product that was sold. Price: The price of the product, represented in dollars. Quantity: The number of units of the product that were sold. Sales Amount: The total sales amount for the sale, calculated as the product of the price and the quantity. This dataset can be used to analyze various aspects of the company's sales performance, such as the total revenue generated, the most popular products, and the average order size. The data could be further analyzed using various statistical techniques and visualization tools to gain insights into the company's sales trends and performance.

Context

LetsRun is a popular running forum, but topics range widely. Some posts garner more engagement than others, and I want to explore how the title of the post influences its engagement with other users on the site.

Content

The title and number of child posts of over 10,000 threads from LetsRun

Acknowledgements

Thanks to LetsRun.com for the data: https://www.letsrun.com/ How I scrapped the data: https://github.com/jbraun8/Kaggle_Dataset_Tools

Dataset

This dataset was created by LK

Released under Data files © Original Authors

Contents

This dataset was modified from @iafoss 's notebook to create full sized 512x512px images. It has been derived from HuBMAP's competition data. By using this dataset, you acknowledge and accept the rules of the competition, which is non-exhaustively summarized below:

DATA ACCESS AND USE: Open Source

Competitions are open to residents of the United States and worldwide, except that if you are a resident of Crimea, Cuba, Iran, Syria, North Korea, Sudan, or are subject to U.S. export controls or sanctions, you may not enter the Competition. Other local rules and regulations may apply to you, so please check your local laws to ensure that you are eligible to participate in skills-based competitions. The Competition Sponsor reserves the right to award alternative Prizes where needed to comply with local laws.

1. COMPETITION DATA.

"Competition Data" means the data or datasets available from the Competition Website for the purpose of use in the Competition, including any prototype or executable code provided on the Competition Website. The Competition Data will contain private and public test sets. Which data belongs to which set will not be made available to participants.

A. Data Access and Use. You may access and use the Competition Data for any purpose, whether commercial or non-commercial, including for participating in the Competition and on Kaggle.com forums, and for academic research and education. The Competition Sponsor reserves the right to disqualify any participant who uses the Competition Data other than as permitted by the Competition Website and these Rules.

B. Data Security. You agree to use reasonable and suitable measures to prevent persons who have not formally agreed to these Rules from gaining access to the Competition Data. You agree not to transmit, duplicate, publish, redistribute or otherwise provide or make available the Competition Data to any party not participating in the Competition. You agree to notify Kaggle immediately upon learning of any possible unauthorized transmission of or unauthorized access to the Competition Data and agree to work with Kaggle to rectify any unauthorized transmission or access.

C. External Data. You may use data other than the Competition Data (“External Data”) to develop and test your models and Submissions. However, you will (i) ensure the External Data is available to use by all participants of the competition for purposes of the competition at no cost to the other participants and (ii) post such access to the External Data for the participants to the official competition forum prior to the Entry Deadline.

Discussion Forum data scraped from Reddit- 18,000 comments about Opioids with some lightweight text analytics used to create additional attributes, beyond the standard meta data that Reddit provides (e.g., 'points', 'comment counts', etc)

Dataset

This dataset was created by Arpan Tarkas

Released under CC0: Public Domain

Contents

It contains the following files:

Dataset

This dataset was created by Ragul Ram J

Released under CC0: Public Domain

Contents

The Health Insurance Marketplace Public Use Files contain data on health and dental plans offered to individuals and small businesses through the US Health Insurance Marketplace.

Exploration Ideas

To help get you started, here are some data exploration ideas:

• How do plan rates and benefits vary across states?
• How do plan benefits relate to plan rates?
• How do plan rates vary by age?
• How do plans vary across insurance network providers?

See this forum thread for more ideas, and post there if you want to add your own ideas or answer some of the open questions!

Data Description

This data was originally prepared and released by the Centers for Medicare & Medicaid Services (CMS). Please read the CMS Disclaimer-User Agreement before using this data.

Here, we've processed the data to facilitate analytics. This processed version has three components:

1. Original versions of the data

The original versions of the 2014, 2015, 2016 data are available in the "raw" directory of the download and "../input/raw" on Kaggle Scripts. Search for "dictionaries" on this page to find the data dictionaries describing the individual raw files.

2. Combined CSV files that contain

In the top level directory of the download ("../input" on Kaggle Scripts), there are six CSV files that contain the combined at across all years:

• BenefitsCostSharing.csv
• BusinessRules.csv
• Network.csv
• PlanAttributes.csv
• Rate.csv
• ServiceArea.csv

Additionally, there are two CSV files that facilitate joining data across years:

• Crosswalk2015.csv - joining 2014 and 2015 data
• Crosswalk2016.csv - joining 2015 and 2016 data

3. SQLite database

The "database.sqlite" file contains tables corresponding to each of the processed CSV files.

The code to create the processed version of this data is available on GitHub.

Context

RedFlagDeals is a forum where users can post retail sales and deals that they come across. The "All Hot Deals" section of the forum was crawled for relevant information on July 12, 2020, and made public.

Content

Each row corresponds to a specific post and contains several relevant pieces of information. A description of each column is given in the table below.

Inspiration

After data-wrangling, the data-set should be fairly simple to analyze and may contain some interesting deals. Since the data includes links to the sales, you may come across offerings that interest you.

Happy sales hunting!

Some questions you may want to consider answering: * Which users generate the most discussed posts or highest number of upvotes? * What are the products that top-users post? * Which sales offer the biggest savings in terms of dollars or percentage? * What are the most popular product categories?

Dataset

This dataset was created by Firas Baba

Contents

Context Imagine you have to process bug reports about an application your company is developing, which is available for both Android and iOS. Could you find a way to automatically classify them so you can send them to the right support team?

Content The dataset contains data from two StackExchange forums: Android Enthusiasts and Ask Differently (Apple). I pre-processed both datasets from the raw XML files retrieved from Internet Archive in order to only contain useful information for building Machine Learning classifiers. In the case of the Apple forum, I narrowed down to the subset of questions that have one of the following tags: "iOS", "iPhone", "iPad".

Think of this as a fun way to learn to build ML classifiers! The training, validation and test sets are all available, but in order to build robust models please try to use the test set as little as possible (only as a last validation for your models).

Acknowledgements The image was retrieved from unsplash and made by @thenewmalcolm. Link to image here.

The data was made available for free under a CC-BY-SA 4.0 license by StackExchange and hosted by Internet Archive. Find it here.

License

CC-BY-SA

Original Data Source: Question Classification: Android or iOS?

Dataset

This dataset was created by Kiim

Released under CC0: Public Domain

Contents

Dataset

This dataset was created by tudor9

Contents

Content

Contains features for the indoor location and navigation competition They were generated using only wifi's that had 500 or more bssid in the training dataset. . See this forum post for discussion on why these features are useful and ways to improve solution.

Acknowledgements

Thanks to @devinanzelmo

This contains data relevant for the 2016 US Presidential Election, including up-to-date primary results.

Exploration Ideas

• What candidates within the Republican party have results that are the most anti-correlated?
• Which Republican candidate is Hillary Clinton most correlated with based on county voting patterns? What about Bernie Sanders?
• What insights can you discover by mapping this data?

Do you have answers or other exploration ideas? Add your ideas to this forum post and share your insights through Kaggle Scripts!

Do you think that we should augment this dataset with more data sources? Let us know here!

Data Description

The 2016 US Election dataset contains several main files and folders at the moment. You may download the entire archive via the "Download Data" link at the top of the page, or interact with the data in Kaggle Scripts through the ../input directory.

• PrimaryResults.csv: main primary results file
  • State: state where the primary or caucus was held
  • StateAbbreviation: two letter state abbreviation
  • County: county where the results come from
  • Party: Democrat or Republican
  • Candidate: name of the candidate
  • Votes: number of votes the candidate received in the corresponding state and county (may be missing)
  • FractionVotes: fraction of votes the president received in the corresponding state, county, and primary
• database.sqlite: SQLite database containing the PrimaryResults table with identical data and schema
• county_shapefiles: directory containing county shapefiles at three different resolutions for mapping

Original Data Sources

• Iowa County-Level Results
• New Hampshire County-Level Results
• County Shapefiles

Slightly modified version of the Iron March data dump that only includes the relevant original files:

• core_members.csv
• orig_topics.csv
• orig_members.csv
• forums_posts_edited.csv
• orig_message_topics.csv

as well as slightly modified versions of the posts and messages files. The modification was converting the post and msg_post in the respective files from HTML to text, using the html2text package. This takes on the order of a minute or two, so I figured I'd include the modified files for convenience, so users don't have to run the command every time they use the notebook.

• message_posts_edited.csv
• core_message_topics.csv

DNS vitiated H2-air Freely Propagating Flame 17 (Ka_u= 2.4, U_in/S_L = 5.51). Part of https://blastnet.github.io/. For config. info look at https://doi.org/10.1016/j.combustflame.2019.07.020 or the file "info.json" in this repo.
A notebook for browsing and reading this data has been attached: https://www.kaggle.com/code/waitongchung/vith2case17-browsedata

Please cite the following when publishing based on this dataset:

1. Appl. Energy in Combust. Sci. Paper: https://doi.org/10.1016/j.jaecs.2022.100087
2. ICML W. Paper: https://openreview.net/forum?id=LxGTZM7L6qn
3. Dataset citation: https://doi.org/10.5281/zenodo.7242864