Analysis of ‘Top 1000 Kaggle Datasets’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/notkrishna/top-1000-kaggle-datasets on 28 January 2022.

--- Dataset description provided by original source is as follows ---

From wiki

Kaggle, a subsidiary of Google LLC, is an online community of data scientists and machine learning practitioners. Kaggle allows users to find and publish data sets, explore and build models in a web-based data-science environment, work with other data scientists and machine learning engineers, and enter competitions to solve data science challenges.

Kaggle got its start in 2010 by offering machine learning competitions and now also offers a public data platform, a cloud-based workbench for data science, and Artificial Intelligence education. Its key personnel were Anthony Goldbloom and Jeremy Howard. Nicholas Gruen was founding chair succeeded by Max Levchin. Equity was raised in 2011 valuing the company at $25 million. On 8 March 2017, Google announced that they were acquiring Kaggle.[1][2]

Source: Kaggle

--- Original source retains full ownership of the source dataset ---

Customer360Insights

The Customer360Insights dataset is a synthetic collection meticulously designed to mirror the multifaceted nature of customer interactions within an e-commerce platform. It encompasses a wide array of variables, each serving as a pillar to support various analytical explorations. Here’s a breakdown of the dataset and the potential analyses it enables:

Dataset Description

• Customer Demographics: Includes FullName, Gender, Age, CreditScore, and MonthlyIncome. These variables provide a demographic snapshot of the customer base, allowing for segmentation and targeted marketing analysis.
• Geographical Data: Comprising Country, State, and City, this section facilitates location-based analytics, market penetration studies, and regional sales performance.
• Product Information: Details like Category, Product, Cost, and Price enable product trend analysis, profitability assessment, and inventory optimization.
• Transactional Data: Captures the customer journey through SessionStart, CartAdditionTime, OrderConfirmation, OrderConfirmationTime, PaymentMethod, and SessionEnd. This rich temporal data can be used for funnel analysis, conversion rate optimization, and customer behavior modeling.
• Post-Purchase Details: With OrderReturn and ReturnReason, analysts can delve into return rate calculations, post-purchase satisfaction, and quality control.

Types of Analysis

• Descriptive Analytics: Understand basic metrics like average monthly income, most common product categories, and typical credit scores.
• Predictive Analytics: Use machine learning to predict credit risk or the likelihood of a purchase based on demographics and session activity.
• Customer Segmentation: Group customers by demographics or purchasing behavior to tailor marketing strategies.
• Geospatial Analysis: Examine sales distribution across different regions and optimize logistics. Time Series Analysis: Study the seasonality of purchases and session activities over time.
• Funnel Analysis: Evaluate the customer journey from session start to order confirmation and identify drop-off points.
• Cohort Analysis: Track customer cohorts over time to understand retention and repeat purchase patterns.
• Market Basket Analysis: Discover product affinities and develop cross-selling strategies.

This dataset is a playground for data enthusiasts to practice cleaning, transforming, visualizing, and modeling data. Whether you’re conducting A/B testing for marketing campaigns, forecasting sales, or building customer profiles, Customer360Insights offers a rich, realistic dataset for honing your data science skills.

Curious about how I created the data? Feel free to click here and take a peek! 😉

📊🔍 Good Luck and Happy Analysing 🔍📊

The community-driven model service platform market is experiencing robust growth, projected to reach $35.14 billion in 2025 and expanding at a compound annual growth rate (CAGR) of 10.1% from 2025 to 2033. This surge is driven by several key factors. The increasing accessibility of machine learning models, fueled by platforms like Kaggle, GitHub, and Hugging Face, is lowering the barrier to entry for developers and researchers. The collaborative nature of these platforms fosters innovation and accelerates model development, leading to a wider adoption of AI solutions across various industries. Furthermore, the growing demand for specialized and customized AI models is pushing businesses to leverage community-driven platforms, where they can find pre-trained models or collaborate on developing tailored solutions, thereby reducing development time and costs. The trend towards open-source models and the rise of model zoos contribute significantly to this market expansion. While challenges exist, such as ensuring model quality, security, and addressing potential biases, the overall market trajectory remains strongly positive. The market's segmentation likely includes various model types (e.g., image recognition, natural language processing, time series analysis), deployment options (cloud-based, on-premise), and target industries (healthcare, finance, retail). Leading players, such as Kaggle, GitHub, Hugging Face, TensorFlow Hub, Model Zoo, DrivenData, and Cortex, are actively shaping the market landscape through continuous innovation and community engagement. The geographical distribution of the market is likely to reflect the global concentration of AI expertise and technological infrastructure, with regions like North America and Europe holding significant market shares initially, followed by rapid expansion in Asia and other developing regions as digital infrastructure improves. Future growth will hinge on continued technological advancements, further integration with cloud platforms, and the development of robust governance frameworks to address ethical concerns surrounding AI model development and deployment.

*Also find Metacritic Movies and Metacritic TV Shows datasets.*

Metacritic Games and Reviews Dataset

This dataset contains a collection of video games and their corresponding reviews from Metacritic, a popular aggregate review site. The data provides insights into various video games across different platforms, including PC, PlayStation, Xbox, and others. Each game entry includes critical reviews, user reviews, ratings, and other relevant information that can be used for analysis, natural language processing, machine learning, and predictive modeling.

Important Note: *The games in this collection are selected from Metacritic's Best Games of All Time list, which only includes titles that have received at least 7 reviews, ensuring a minimum level of critical and user input.*

Up-to-dateness: *This dataset is accurate as of March 14, 2025, and includes the most current rankings and game details available at that time.*

Content

The dataset contains general information and scores of 13K+ games and their corresponding 1.6M+ user/critic reviews collected by sending automated requests to Metacritic's public backend API using Python's requests and pandas libraries.

Potential Uses

• Sentiment analysis and natural language processing on reviews.
• Predictive modeling for predicting user ratings based on features like genre, publisher, and developer.
• Data analysis on trends in game quality, genres, or platform performance.
• Comparing critical reviews and user reviews to understand the divergence in ratings.

This dataset is perfect for researchers, game enthusiasts, and data scientists who are interested in exploring the gaming industry through data analysis.

Acknowledgements

• The datasets are scraped from metacritic.com
  
• The code for scraping the dataset can be found on my GitHub repository

KGTorrent is a dataset of Python Jupyter notebooks from the Kaggle platform.

The dataset is accompanied by a MySQL database containing metadata about the notebooks and the activity of Kaggle users on the platform. The information to build the MySQL database has been derived from Meta Kaggle, a publicly available dataset containing Kaggle metadata.

In this package, we share the complete KGTorrent dataset (consisting of the dataset itself plus its companion database), as well as the specific version of Meta Kaggle used to build the database.

More specifically, the package comprises the following three compressed archives:

KGT_dataset.tar.bz2, the dataset of Jupyter notebooks;

KGTorrent_dump_10-2020.sql.tar.bz2, the dump of the MySQL companion database;

MetaKaggle27Oct2020.tar.bz2, a copy of the Meta Kaggle version used to build the database.

Moreover, we include KGTorrent_logical_schema.pdf, the logical schema of the KGTorrent MySQL database.

GitHub Issues & Kaggle Notebooks

  Description

GitHub Issues & Kaggle Notebooks is a collection of two code datasets intended for language models training, they are sourced from GitHub issues and notebooks in Kaggle platform. These datasets are a modified part of the StarCoder2 model training corpus, precisely the bigcode/StarCoder2-Extras dataset. We reformat the samples to remove StarCoder2's special tokens and use natural text to delimit comments in issues and display… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceTB/issues-kaggle-notebooks.

Dataset

This dataset was created by Jigyashu Singh Lodhi

Released under Other (specified in description)

Contents

Context and Methodology

Research Domain:
The dataset is part of a project focused on retail sales forecasting. Specifically, it is designed to predict daily sales for Rossmann, a chain of over 3,000 drug stores operating across seven European countries. The project falls under the broader domain of time series analysis and machine learning applications for business optimization. The goal is to apply machine learning techniques to forecast future sales based on historical data, which includes factors like promotions, competition, holidays, and seasonal trends.

Purpose:
The primary purpose of this dataset is to help Rossmann store managers predict daily sales for up to six weeks in advance. By making accurate sales predictions, Rossmann can improve inventory management, staffing decisions, and promotional strategies. This dataset serves as a training set for machine learning models aimed at reducing forecasting errors and supporting decision-making processes across the company’s large network of stores.

How the Dataset Was Created:
The dataset was compiled from several sources, including historical sales data from Rossmann stores, promotional calendars, holiday schedules, and external factors such as competition. The data is split into multiple features, such as the store's location, promotion details, whether the store was open or closed, and weather information. The dataset is publicly available on platforms like Kaggle and was initially created for the Kaggle Rossmann Store Sales competition. The data is made accessible via an API for further analysis and modeling, and it is structured to help machine learning models predict future sales based on various input variables.

Technical Details

Dataset Structure:

The dataset consists of three main files, each with its specific role:

1. Train:
   This file contains the historical sales data, which is used to train machine learning models. It includes daily sales information for each store, as well as various features that could influence the sales (e.g., promotions, holidays, store type, etc.).

   https://handle.test.datacite.org/10.82556/yb6j-jw41
   PID: b1c59499-9c6e-42c2-af8f-840181e809db

2. Test2:
   The test dataset mirrors the structure of train.csv but does not include the actual sales values (i.e., the target variable). This file is used for making predictions using the trained machine learning models. It is used to evaluate the accuracy of predictions when the true sales data is unknown.

   https://handle.test.datacite.org/10.82556/jerg-4b84
   PID: 7cbb845c-21dd-4b60-b990-afa8754a0dd9

3. Store:
   This file provides metadata about each store, including information such as the store’s location, type, and assortment level. This data is essential for understanding the context in which the sales data is gathered.

   https://handle.test.datacite.org/10.82556/nqeg-gy34
   PID: 9627ec46-4ee6-4969-b14a-bda555fe34db

Data Fields Description:

• Id: A unique identifier for each (Store, Date) combination within the test set.

• Store: A unique identifier for each store.

• Sales: The daily turnover (target variable) for each store on a specific day (this is what you are predicting).

• Customers: The number of customers visiting the store on a given day.

• Open: An indicator of whether the store was open (1 = open, 0 = closed).

• StateHoliday: Indicates if the day is a state holiday, with values like:

  • 'a' = public holiday,

  • 'b' = Easter holiday,

  • 'c' = Christmas,

  • '0' = no holiday.

• SchoolHoliday: Indicates whether the store is affected by school closures (1 = yes, 0 = no).

• StoreType: Differentiates between four types of stores: 'a', 'b', 'c', 'd'.

• Assortment: Describes the level of product assortment in the store:

  • 'a' = basic,

  • 'b' = extra,

  • 'c' = extended.

• CompetitionDistance: Distance (in meters) to the nearest competitor store.

• CompetitionOpenSince[Month/Year]: The month and year when the nearest competitor store opened.

• Promo: Indicates whether the store is running a promotion on a particular day (1 = yes, 0 = no).

• Promo2: Indicates whether the store is participating in Promo2, a continuing promotion for some stores (1 = participating, 0 = not participating).

• Promo2Since[Year/Week]: The year and calendar week when the store started participating in Promo2.

• PromoInterval: Describes the months when Promo2 is active, e.g., "Feb,May,Aug,Nov" means the promotion starts in February, May, August, and November.

Software Requirements

To work with this dataset, you will need to have specific software installed, including:

• DBRepo Authorization: This is required to access the datasets via the DBRepo API. You may need to authenticate with an API key or login credentials to retrieve the datasets.

• Python Libraries: Key libraries for working with the dataset include:

  • pandas for data manipulation,

  • numpy for numerical operations,

  • matplotlib and seaborn for data visualization,

  • scikit-learn for machine learning algorithms.

Additional Resources

Several additional resources are available for working with the dataset:

1. Presentation:
   A presentation summarizing the exploratory data analysis (EDA), feature engineering process, and key insights from the analysis is provided. This presentation also includes visualizations that help in understanding the dataset’s trends and relationships.

2. Jupyter Notebook:
   A Jupyter notebook, titled Retail_Sales_Prediction_Capstone_Project.ipynb, is provided, which details the entire machine learning pipeline, from data loading and cleaning to model training and evaluation.

3. Model Evaluation Results:
   The project includes a detailed evaluation of various machine learning models, including their performance metrics like training and testing scores, Mean Absolute Percentage Error (MAPE), and Root Mean Squared Error (RMSE). This allows for a comparison of model effectiveness in forecasting sales.

4. Trained Models (.pkl files):
   The models trained during the project are saved as .pkl files. These files contain the trained machine learning models (e.g., Random Forest, Linear Regression, etc.) that can be loaded and used to make predictions without retraining the models from scratch.

5. sample_submission.csv:
   This file is a sample submission file that demonstrates the format of predictions expected when using the trained model. The sample_submission.csv contains predictions made on the test dataset using the trained Random Forest model. It provides an example of how the output should be structured for submission.

These resources provide a comprehensive guide to implementing and analyzing the sales forecasting model, helping you understand the data, methods, and results in greater detail.

Dataset

This dataset was created by Shashi Preetham

Contents

FSDKaggle2018 is an audio dataset containing 11,073 audio files annotated with 41 labels of the AudioSet Ontology. FSDKaggle2018 has been used for the DCASE Challenge 2018 Task 2, which was run as a Kaggle competition titled Freesound General-Purpose Audio Tagging Challenge.

Citation

If you use the FSDKaggle2018 dataset or part of it, please cite our DCASE 2018 paper:

Eduardo Fonseca, Manoj Plakal, Frederic Font, Daniel P. W. Ellis, Xavier Favory, Jordi Pons, Xavier Serra. "General-purpose Tagging of Freesound Audio with AudioSet Labels: Task Description, Dataset, and Baseline". Proceedings of the DCASE 2018 Workshop (2018)

You can also consider citing our ISMIR 2017 paper, which describes how we gathered the manual annotations included in FSDKaggle2018.

Eduardo Fonseca, Jordi Pons, Xavier Favory, Frederic Font, Dmitry Bogdanov, Andres Ferraro, Sergio Oramas, Alastair Porter, and Xavier Serra, "Freesound Datasets: A Platform for the Creation of Open Audio Datasets", In Proceedings of the 18th International Society for Music Information Retrieval Conference, Suzhou, China, 2017

Contact

You are welcome to contact Eduardo Fonseca should you have any questions at eduardo.fonseca@upf.edu.

About this dataset

Freesound Dataset Kaggle 2018 (or FSDKaggle2018 for short) is an audio dataset containing 11,073 audio files annotated with 41 labels of the AudioSet Ontology [1]. FSDKaggle2018 has been used for the Task 2 of the Detection and Classification of Acoustic Scenes and Events (DCASE) Challenge 2018. Please visit the DCASE2018 Challenge Task 2 website for more information. This Task was hosted on the Kaggle platform as a competition titled Freesound General-Purpose Audio Tagging Challenge. It was organized by researchers from the Music Technology Group of Universitat Pompeu Fabra, and from Google Research’s Machine Perception Team.

The goal of this competition was to build an audio tagging system that can categorize an audio clip as belonging to one of a set of 41 diverse categories drawn from the AudioSet Ontology.

All audio samples in this dataset are gathered from Freesound [2] and are provided here as uncompressed PCM 16 bit, 44.1 kHz, mono audio files. Note that because Freesound content is collaboratively contributed, recording quality and techniques can vary widely.

The ground truth data provided in this dataset has been obtained after a data labeling process which is described below in the Data labeling process section. FSDKaggle2018 clips are unequally distributed in the following 41 categories of the AudioSet Ontology:

"Acoustic_guitar", "Applause", "Bark", "Bass_drum", "Burping_or_eructation", "Bus", "Cello", "Chime", "Clarinet", "Computer_keyboard", "Cough", "Cowbell", "Double_bass", "Drawer_open_or_close", "Electric_piano", "Fart", "Finger_snapping", "Fireworks", "Flute", "Glockenspiel", "Gong", "Gunshot_or_gunfire", "Harmonica", "Hi-hat", "Keys_jangling", "Knock", "Laughter", "Meow", "Microwave_oven", "Oboe", "Saxophone", "Scissors", "Shatter", "Snare_drum", "Squeak", "Tambourine", "Tearing", "Telephone", "Trumpet", "Violin_or_fiddle", "Writing".

Some other relevant characteristics of FSDKaggle2018:

• The dataset is split into a train set and a test set.

• The train set is meant to be for system development and includes ~9.5k samples unequally distributed among 41 categories. The minimum number of audio samples per category in the train set is 94, and the maximum 300. The duration of the audio samples ranges from 300ms to 30s due to the diversity of the sound categories and the preferences of Freesound users when recording sounds. The total duration of the train set is roughly 18h.

• Out of the ~9.5k samples from the train set, ~3.7k have manually-verified ground truth annotations and ~5.8k have non-verified annotations. The non-verified annotations of the train set have a quality estimate of at least 65-70% in each category. Checkout the Data labeling process section below for more information about this aspect.

• Non-verified annotations in the train set are properly flagged in train.csv so that participants can opt to use this information during the development of their systems.

• The test set is composed of 1.6k samples with manually-verified annotations and with a similar category distribution than that of the train set. The total duration of the test set is roughly 2h.

• All audio samples in this dataset have a single label (i.e. are only annotated with one label). Checkout the Data labeling process section below for more information about this aspect. A single label should be predicted for each file in the test set.

Data labeling process

The data labeling process started from a manual mapping between Freesound tags and AudioSet Ontology categories (or labels), which was carried out by researchers at the Music Technology Group, Universitat Pompeu Fabra, Barcelona. Using this mapping, a number of Freesound audio samples were automatically annotated with labels from the AudioSet Ontology. These annotations can be understood as weak labels since they express the presence of a sound category in an audio sample.

Then, a data validation process was carried out in which a number of participants did listen to the annotated sounds and manually assessed the presence/absence of an automatically assigned sound category, according to the AudioSet category description.

Audio samples in FSDKaggle2018 are only annotated with a single ground truth label (see train.csv). A total of 3,710 annotations included in the train set of FSDKaggle2018 are annotations that have been manually validated as present and predominant (some with inter-annotator agreement but not all of them). This means that in most cases there is no additional acoustic material other than the labeled category. In few cases there may be some additional sound events, but these additional events won't belong to any of the 41 categories of FSDKaggle2018.

The rest of the annotations have not been manually validated and therefore some of them could be inaccurate. Nonetheless, we have estimated that at least 65-70% of the non-verified annotations per category in the train set are indeed correct. It can happen that some of these non-verified audio samples present several sound sources even though only one label is provided as ground truth. These additional sources are typically out of the set of the 41 categories, but in a few cases they could be within.

More details about the data labeling process can be found in [3].

License

FSDKaggle2018 has licenses at two different levels, as explained next.

All sounds in Freesound are released under Creative Commons (CC) licenses, and each audio clip has its own license as defined by the audio clip uploader in Freesound. For attribution purposes and to facilitate attribution of these files to third parties, we include a relation of the audio clips included in FSDKaggle2018 and their corresponding license. The licenses are specified in the files train_post_competition.csv and test_post_competition_scoring_clips.csv.

In addition, FSDKaggle2018 as a whole is the result of a curation process and it has an additional license. FSDKaggle2018 is released under CC-BY. This license is specified in the LICENSE-DATASET file downloaded with the FSDKaggle2018.doc zip file.

Files

FSDKaggle2018 can be downloaded as a series of zip files with the following directory structure:

root
│

└───FSDKaggle2018.audio_train/            Audio clips in the train set
│

└───FSDKaggle2018.audio_test/            Audio clips in the test set
│

└───FSDKaggle2018.meta/               Files for evaluation setup
│  │

│  └───train_post_competition.csv          Data split and ground truth for the train set
│  │

│  └───test_post_competition_scoring_clips.csv   Ground truth for the test set

│

└───FSDKaggle2018.doc/
  │

  └───README.md                  The dataset description file you are reading
  │

  └───LICENSE-DATASET             

The Data Science Collaboration Platform market is experiencing robust growth, projected to reach $3.901 billion in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 23.5% from 2025 to 2033. This expansion is fueled by several key factors. Firstly, the increasing volume and complexity of data necessitate collaborative data science efforts, driving demand for platforms that streamline workflows and enhance team productivity. Secondly, the rising adoption of cloud-based solutions offers scalability, accessibility, and cost-effectiveness, further accelerating market growth. The shift towards agile methodologies and the need for faster time-to-insights also significantly contribute to the market's dynamism. Large enterprises are currently the leading adopters, driven by their need for advanced analytics and data-driven decision-making. However, increasing digitalization across medium and small enterprises is projected to fuel substantial growth in these segments in the coming years. The market is segmented by deployment type (Cloud-Based and On-Premises), with cloud-based solutions gaining significant traction due to their flexibility and ease of access. The competitive landscape is highly dynamic, with key players such as Databricks, Google, Microsoft, Kaggle, DataRobot, IBM, and Alteryx vying for market share. These companies are constantly innovating to offer enhanced features, improve integration capabilities, and expand their geographical reach. North America currently holds the largest market share, driven by early adoption and technological advancements, followed by Europe and Asia Pacific. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, primarily fueled by expanding digital infrastructure and increasing government investments in data science initiatives within countries like India and China. The market faces certain restraints, such as the need for specialized skills to effectively utilize these platforms and the concerns regarding data security and privacy. However, the overall market outlook remains optimistic, with continuous innovation and rising demand driving sustained growth throughout the forecast period.

The BDG2 open data set consists of 3,053 energy meters from 1,636 non-residential buildings with a range of two full years (2016 and 2017) at an hourly frequency (17,544 measurements per meter resulting in approximately 53.6 million measurements). These meters are collected from 19 sites across North America and Europe, and they measure electrical, heating and cooling water, steam, and solar energy as well as water and irrigation meters. Part of these data was used in the Great Energy Predictor III (GEPIII) competition hosted by the ASHRAE organization in October-December 2019. This subset includes data from 2,380 meters from 1,448 buildings that were used in the GEPIII, a machine learning competition for long-term prediction with an application to measurement and verification. This paper describes the process of data collection, cleaning, and convergence of time-series meter data, the meta-data about the buildings, and complementary weather data. This data set can be used for further prediction benchmarking and prototyping as well as anomaly detection, energy analysis, and building type classification.

Dataset

This dataset was created by Jay2K

Contents

In recent years, with the continuous improvement of the financial system and the rapid development of the banking industry, the competition of the banking industry itself has intensified. At the same time, with the rapid development of information technology and Internet technology, customers’ choice of financial products is becoming more and more diversified, and customers’ dependence and loyalty to banking institutions is becoming less and less, and the problem of customer churn in commercial banks is becoming more and more prominent. How to predict customer behavior and retain existing customers has become a major challenge for banks to solve. Therefore, this study takes a bank’s business data on Kaggle platform as the research object, uses multiple sampling methods to compare the data for balancing, constructs a bank customer churn prediction model for churn identification by GA-XGBoost, and conducts interpretability analysis on the GA-XGBoost model to provide decision support and suggestions for the banking industry to prevent customer churn. The results show that: (1) The applied SMOTEENN is more effective than SMOTE and ADASYN in dealing with the imbalance of banking data. (2) The F1 and AUC values of the model improved and optimized by XGBoost using genetic algorithm can reach 90% and 99%, respectively, which are optimal compared to other six machine learning models. The GA-XGBoost classifier was identified as the best solution for the customer churn problem. (3) Using Shapley values, we explain how each feature affects the model results, and analyze the features that have a high impact on the model prediction, such as the total number of transactions in the past year, the amount of transactions in the past year, the number of products owned by customers, and the total sales balance. The contribution of this paper is mainly in two aspects: (1) this study can provide useful information from the black box model based on the accurate identification of churned customers, which can provide reference for commercial banks to improve their service quality and retain customers; (2) it can provide reference for customer churn early warning models of other related industries, which can help the banking industry to maintain customer stability, maintain market position and reduce corporate losses.

These datasets contain peer-to-peer trades from various recommendation platforms.

Metadata includes

• peer-to-peer trades

• have and want lists

• image data (tradesy)

About sites

All the sites were obtained from different sources.

Analysis of ‘CENSORED WEB-SITES BY ALL COUNTRIES’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/brsdincer/censored-websites-by-all-countries on 28 January 2022.

--- Dataset description provided by original source is as follows ---

CENSORED WEB-SITES BY ALL COUNTRIES

Sites that were or are currently banned.

This data was created by each country's own users.

• Some of the sites you have seen may have been active again.

--- Original source retains full ownership of the source dataset ---

Analysis of ‘Video Game Sales and Ratings’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/kendallgillies/video-game-sales-and-ratings on 13 February 2022.

--- Dataset description provided by original source is as follows ---

Context

This data set contains a list of video games with sales greater than 100,000 copies along with critic and user ratings. It is a combined web scrape from VGChartz and Metacritic along with manually entered year of release values for most games with a missing year of release. The original coding was created by Rush Kirubi and can be found here, but it limited the data to only include a subset of video game platforms. Not all of the listed video games have information on Metacritic, so there data set does have missing values.

Content

The fields include:

• Name - The game's name
• Platform - Platform of the games release
• Year_of_Release - Year of the game's release
• Genre - Genre of the game
• Publisher - Publisher of the game
• NA_Sales - Sales in North America (in millions)
• EU_Sales - Sales in Europe (in millions)
• JP_Sales - Sales in Japan (in millions)
• Other_Sales - Sales in the rest of the world (in millions)
• Global_Sales - Total worldwide sales (in millions)
• Critic_score - Aggregate score compiled by Metacritic staff
• Critic_count - The number of critics used in coming up with the critic score
• User_score - Score by Metacritic's subscribers
• User_count - Number of users who gave the user score
• Rating - The ESRB ratings

Acknowledgements

Again the main credit behind this data set goes to Rush Kirubi. I just commented out two lines of his code.

Also the original inspiration for this data set came from Gregory Smith who originally scraped the data from VGChartz, it can be found here.

--- Original source retains full ownership of the source dataset ---

FSDKaggle2019 is an audio dataset containing 29,266 audio files annotated with 80 labels of the AudioSet Ontology. FSDKaggle2019 has been used for the DCASE Challenge 2019 Task 2, which was run as a Kaggle competition titled Freesound Audio Tagging 2019.

Citation

If you use the FSDKaggle2019 dataset or part of it, please cite our DCASE 2019 paper:

Eduardo Fonseca, Manoj Plakal, Frederic Font, Daniel P. W. Ellis, Xavier Serra. "Audio tagging with noisy labels and minimal supervision". Proceedings of the DCASE 2019 Workshop, NYC, US (2019)

You can also consider citing our ISMIR 2017 paper, which describes how we gathered the manual annotations included in FSDKaggle2019.

Eduardo Fonseca, Jordi Pons, Xavier Favory, Frederic Font, Dmitry Bogdanov, Andres Ferraro, Sergio Oramas, Alastair Porter, and Xavier Serra, "Freesound Datasets: A Platform for the Creation of Open Audio Datasets", In Proceedings of the 18th International Society for Music Information Retrieval Conference, Suzhou, China, 2017

Data curators

Eduardo Fonseca, Manoj Plakal, Xavier Favory, Jordi Pons

Contact

You are welcome to contact Eduardo Fonseca should you have any questions at eduardo.fonseca@upf.edu.

ABOUT FSDKaggle2019

Freesound Dataset Kaggle 2019 (or FSDKaggle2019 for short) is an audio dataset containing 29,266 audio files annotated with 80 labels of the AudioSet Ontology [1]. FSDKaggle2019 has been used for the Task 2 of the Detection and Classification of Acoustic Scenes and Events (DCASE) Challenge 2019. Please visit the DCASE2019 Challenge Task 2 website for more information. This Task was hosted on the Kaggle platform as a competition titled Freesound Audio Tagging 2019. It was organized by researchers from the Music Technology Group (MTG) of Universitat Pompeu Fabra (UPF), and from Sound Understanding team at Google AI Perception. The competition intended to provide insight towards the development of broadly-applicable sound event classifiers able to cope with label noise and minimal supervision conditions.

FSDKaggle2019 employs audio clips from the following sources:

1. Freesound Dataset (FSD): a dataset being collected at the MTG-UPF based on Freesound content organized with the AudioSet Ontology
2. The soundtracks of a pool of Flickr videos taken from the Yahoo Flickr Creative Commons 100M dataset (YFCC)

The audio data is labeled using a vocabulary of 80 labels from Google’s AudioSet Ontology [1], covering diverse topics: Guitar and other Musical Instruments, Percussion, Water, Digestive, Respiratory sounds, Human voice, Human locomotion, Hands, Human group actions, Insect, Domestic animals, Glass, Liquid, Motor vehicle (road), Mechanisms, Doors, and a variety of Domestic sounds. The full list of categories can be inspected in vocabulary.csv (see Files & Download below). The goal of the task was to build a multi-label audio tagging system that can predict appropriate label(s) for each audio clip in a test set.

What follows is a summary of some of the most relevant characteristics of FSDKaggle2019. Nevertheless, it is highly recommended to read our DCASE 2019 paper for a more in-depth description of the dataset and how it was built.

Ground Truth Labels

The ground truth labels are provided at the clip-level, and express the presence of a sound category in the audio clip, hence can be considered weak labels or tags. Audio clips have variable lengths (roughly from 0.3 to 30s).

The audio content from FSD has been manually labeled by humans following a data labeling process using the Freesound Annotator platform. Most labels have inter-annotator agreement but not all of them. More details about the data labeling process and the Freesound Annotator can be found in [2].

The YFCC soundtracks were labeled using automated heuristics applied to the audio content and metadata of the original Flickr clips. Hence, a substantial amount of label noise can be expected. The label noise can vary widely in amount and type depending on the category, including in- and out-of-vocabulary noises. More information about some of the types of label noise that can be encountered is available in [3].

Specifically, FSDKaggle2019 features three types of label quality, one for each set in the dataset:

• curated train set: correct (but potentially incomplete) labels
• noisy train set: noisy labels
• test set: correct and complete labels

Further details can be found below in the sections for each set.

Format

All audio clips are provided as uncompressed PCM 16 bit, 44.1 kHz, mono audio files.

DATA SPLIT

FSDKaggle2019 consists of two train sets and one test set. The idea is to limit the supervision provided for training (i.e., the manually-labeled, hence reliable, data), thus promoting approaches to deal with label noise.

Curated train set

The curated train set consists of manually-labeled data from FSD.

• Number of clips/class: 75 except in a few cases (where there are less)
• Total number of clips: 4970
• Avg number of labels/clip: 1.2
• Total duration: 10.5 hours

The duration of the audio clips ranges from 0.3 to 30s due to the diversity of the sound categories and the preferences of Freesound users when recording/uploading sounds. Labels are correct but potentially incomplete. It can happen that a few of these audio clips present additional acoustic material beyond the provided ground truth label(s).

Noisy train set

The noisy train set is a larger set of noisy web audio data from Flickr videos taken from the YFCC dataset [5].

• Number of clips/class: 300
• Total number of clips: 19,815
• Avg number of labels/clip: 1.2
• Total duration: ~80 hours

The duration of the audio clips ranges from 1s to 15s, with the vast majority lasting 15s. Labels are automatically generated and purposefully noisy. No human validation is involved. The label noise can vary widely in amount and type depending on the category, including in- and out-of-vocabulary noises.

Considering the numbers above, the per-class data distribution available for training is, for most of the classes, 300 clips from the noisy train set and 75 clips from the curated train set. This means 80% noisy / 20% curated at the clip level, while at the duration level the proportion is more extreme considering the variable-length clips.

Test set

The test set is used for system evaluation and consists of manually-labeled data from FSD.

• Number of clips/class: between 50 and 150
• Total number of clips: 4481
• Avg number of labels/clip: 1.4
• Total duration: 12.9 hours

The acoustic material present in the test set clips is labeled exhaustively using the aforementioned vocabulary of 80 classes. Most labels have inter-annotator agreement but not all of them. Except human error, the label(s) are correct and complete considering the target vocabulary; nonetheless, a few clips could still present additional (unlabeled) acoustic content out of the vocabulary.

During the DCASE2019 Challenge Task 2, the test set was split into two subsets, for the public and private leaderboards, and only the data corresponding to the public leaderboard was provided. In this current package you will find the full test set with all the test labels. To allow comparison with previous work, the file test_post_competition.csv includes a flag to determine the corresponding leaderboard (public

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