262 Resources

Open Dataset

Showing 209-216 of 262 results
Video Game Sales

Video Game Sales

Stefan
Open Dataset
Creators: Smith, Gregory
Publication Date: 2016
Creators: Smith, Gregory

This dataset contains a list of video games with sales greater than 100,000 copies. It was generated by a scrape of vgchartz.com. The dataset has a size of 1,36 MB and includes games released up to the year 2016, offering a historical perspective on video game sales over several decades. It allows for in-depth analysis of sales trends across different regions, platforms, and genres, making it a valuable resource for market analysis and strategic planning within the video game industry. Each entry in the dataset includes the following attributes:​

  • Rank: Overall sales ranking of the game.​
  • Name: Title of the game.​
  • Platform: The platform on which the game was released (e.g., PC, PS4).​
  • Year: Year of the game’s release.​
  • Genre: Genre classification of the game.​
  • Publisher: Company that published the game.​
  • NA_Sales: Sales figures in North America (in millions).​
  • EU_Sales: Sales figures in Europe (in millions).​
  • JP_Sales: Sales figures in Japan (in millions).​
  • Other_Sales: Sales figures in the rest of the world (in millions).​
  • Global_Sales: Total worldwide sales (in millions).
World Happiness Report

World Happiness Report

Stefan
Open Dataset
Creators: F. Helliwell, John; Layard, Richard; Sachs, Jeffrey D. ; De Neve, Jan-Emmanuel; Aknin, Lara B.; Wang, Shun
Publication Date: 2012
Creators: F. Helliwell, John; Layard, Richard; Sachs, Jeffrey D. ; De Neve, Jan-Emmanuel; Aknin, Lara B.; Wang, Shun

The happiness scores and rankings use data from the Gallup World Poll. The scores are based on answers to the main life evaluation question asked in the poll. This question, known as the Cantril ladder, asks respondents to think of a ladder with the best possible life for them being a 10 and the worst possible life being a 0 and to rate their own current lives on that scale. The scores are from nationally representative samples for the years 2013-2016 and use the Gallup weights to make the estimates representative. The columns following the happiness score estimate the extent to which each of six factors – economic production, social support, life expectancy, freedom, absence of corruption, and generosity – contribute to making life evaluations higher in each country than they are in Dystopia, a hypothetical country that has values equal to the world’s lowest national averages for each of the six factors. They have no impact on the total score reported for each country, but they do explain why some countries rank higher than others. The dataset has a size of 80,86 kB.

Popular Movies of TMDb

Popular Movies of TMDb

Stefan
Open Dataset
Creators: Mondal, Sankha Subhra
Publication Date: 2020
Creators: Mondal, Sankha Subhra

This dataset of the 10,000 most popular movies across the world has been fetched through the read API.
TMDB’s free API provides for developers and their team to programmatically fetch and use TMDb’s data.
Their API is to use as long as you attribute TMDb as the source of the data and/or images. Also, they update their API from time to time. The data set is 3.2 MB large. It offers valuable insights into global cinematic trends and preferences.

Each movie entry in the dataset includes the following attributes:​

  • title: The name of the movie.​
  • overview: A brief summary of the movie’s plot.​
  • original_language: The language in which the movie was originally produced.​
  • vote_average: The average user rating of the movie on TMDb.​
goodbooks-10k

goodbooks-10k

Stefan
Open Dataset
Creators: Zając, Zygmunt
Publication Date: 2017
Creators: Zając, Zygmunt

The dataset contains six million ratings for ten thousand most popular books (with most ratings). It offers a rich resource for analyzing reading habits, book popularity, and user engagement within the literary community. There are also books marked to read by the users, book metadata (author, year, etc.) and tags/shelves/genres.

ratings contains ratings sorted by time. Ratings go from one to five. Both book IDs and user IDs are contiguous. For books, they are 1-10000, for users, 1-53424.

to_read  provides IDs of the books marked “to read” by each user, as user_id,book_id pairs, sorted by time. There are close to a million pairs.

books has metadata for each book (goodreads IDs, authors, title, average rating, etc.). The metadata have been extracted from goodreads XML files.

book_tags contains tags/shelves/genres assigned by users to books. Tags in this file are represented by their IDs. They are sorted by goodreads_book_id  ascending and count descending.

The date set is 68.8 MB large.

European Funds dataset from Morningstar

European Funds dataset from Morningstar

Stefan
Open Dataset
Creators: (Leone, Stefano)
Publication Date: 2019
Creators: (Leone, Stefano)

The file contains 57,603 Mutual Funds and 9,495 ETFs with general aspects (as Total Net Assets, management company and size), portfolio indicators (as cash, stocks, bonds, and sectors), returns (as yeartodate, 2020-11) and financial ratios (as price/earning, Treynor and Sharpe ratios, alpha, and beta).
Additional data in terms of sustainability is also available. A key feature of this dataset is the inclusion of detailed Morningstar ratings, which are widely used in the financial industry to assess fund quality based on past performance, risk-adjusted returns, and analyst evaluations. Additionally, it offers categorization of funds, allowing for segmentation by investment type, sector, region, and fund style (e.g., growth vs. value investing). The dataset has a total size of approximately 103.88 MB.

Overall, the dataset is structured into the following variables:​

  • ticker: Fund ticker code.​
  • isin: Fund ISIN code.​
  • fund_name: Extended name of the fund.​
  • inception_date: Date of the fund’s inception.​
  • category: Fund category.​
  • rating: Morningstar rating.​
  • analyst_rating: Morningstar analyst rating.​
  • risk_rating: Morningstar risk rating.​
  • performance_rating: Morningstar performance rating.​
TripAdvisor European restaurants

TripAdvisor European restaurants

Stefan
Open Dataset
Creators: (Leone, Stefano)
Publication Date: 2021
Creators: (Leone, Stefano)

TripAdvisor is the most popular travel website and it stores data for almost all restaurants, showing locations (even latitude and longitude coordinates), restaurant descriptions, user ratings and reviews, and many more aspects. The dataset is 0.68 GB large.

The TripAdvisor dataset includes 1,083,397 restaurants with attributes such as location data, average rating, number of reviews, open hours, cuisine types, awards, etc.

The dataset combines the restaurants from the main European countries, the data has been scraped in early May 2021.

The dataset is structured with various variables for each restaurant, such as:​

  • restaurant_link: Unique TripAdvisor restaurant link.​
  • restaurant_name: Name of the restaurant on TripAdvisor.​
  • original_location: Original location displayed on TripAdvisor.​
  • country: Country name retrieved from original_location.​
  • region: Region name retrieved from original_location.​
  • province: Province name retrieved from original_location.​
  • city: City name retrieved from original_location.​
  • address: Address displayed on TripAdvisor.​
  • latitude: Latitude coordinate.​
  • longitude: Longitude coordinate.​
  • claimed: Indicates if the restaurant business is claimed on TripAdvisor.​
  • awards: Award names.​
  • popularity_detailed: Detailed popularity ranking.​
  • popularity_generic: Generic popularity ranking (among all places to eat in the area).
Credit Card Fraud Detection

Credit Card Fraud Detection

Stefan
Open Dataset
Creators: Worldline and the Machine Learning Group of ULB ((Universite Libre de Bruxelles)
Publication Date: 2016
Creators: Worldline and the Machine Learning Group of ULB ((Universite Libre de Bruxelles)

The Credit Card Fraud Detection dataset is a rich collection of credit card transactions made by European cardholders in September 2013. It presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions. The dataset is 0.15 GB large.

The data has been collected and analysed during a research collaboration of Worldline and the Machine Learning Group (http://mlg.ulb.ac.be) of ULB (Université Libre de Bruxelles) on big data mining and fraud detection.

Each transaction record in the dataset includes several features:​

  • Time: The number of seconds elapsed between this transaction and the first transaction in the dataset.​

  • V1 to V28: These are the result of a Principal Component Analysis (PCA) transformation applied to the original features to protect sensitive information.​

  • Amount: The monetary value of the transaction.​

  • Class: A binary indicator where ‘1’ signifies a fraudulent transaction and ‘0’ denotes a legitimate one.​

Heart Disease Data Set

Heart Disease Data Set

Stefan
Open Dataset
Creators: Janosi, Andras; Steinbrunn, William; Pfisterer, Matthias; Detrano, Robert
Publication Date: 1988
Creators: Janosi, Andras; Steinbrunn, William; Pfisterer, Matthias; Detrano, Robert

The Heart Disease database is a well-regarded resource in the medical research community, particularly for studies related to cardiovascular conditions. It comprises data from four distinct databases: the Cleveland Clinic Foundation, the Hungarian Institute of Cardiology in Budapest, the V.A. Medical Center in Long Beach, California, and the University Hospital in Zurich, Switzerland. Each of these databases contains patient records with various medical attributes, totaling 76 features. However, most research has focused on a subset of 14 key attributes to diagnose the presence of heart disease. he dataset is relatively small, with each database containing a few hundred records. For example, the Cleveland database includes 303 instances. Given the number of attributes and instances, the dataset’s size is minimal, making it easily manageable for analysis without requiring significant storage resources. The data was collected over several years, primarily during the 1980s.

Each patient record in the dataset includes the following 14 attributes commonly used in research:​

  • Age: Age of the patient in years.​
  • Sex: Gender of the patient (1 = male; 0 = female).​
  • Chest Pain Type (cp): Categorical variable indicating the type of chest pain experienced, with values ranging from 0 to 3.​
  • Resting Blood Pressure (trestbps): Resting blood pressure in mm Hg upon hospital admission.​
  • Serum Cholesterol (chol): Serum cholesterol level in mg/dl.​
  • Fasting Blood Sugar (fbs): Binary variable indicating if fasting blood sugar is greater than 120 mg/dl (1 = true; 0 = false).​
  • Resting Electrocardiographic Results (restecg): Categorical variable with values 0 to 2 indicating ECG results.​
  • Maximum Heart Rate Achieved (thalach): Maximum heart rate achieved during exercise.​
  • Exercise-Induced Angina (exang): Binary variable indicating if exercise-induced angina occurred (1 = yes; 0 = no).​
  • ST Depression (oldpeak): ST depression induced by exercise relative to rest.​
  • Slope of the Peak Exercise ST Segment (slope): Categorical variable with values 0 to 2.​
  • Number of Major Vessels Colored by Fluoroscopy (ca): Integer value ranging from 0 to 3.​
  • Thalassemia (thal): Categorical variable indicating blood disorder status (3 = normal; 6 = fixed defect; 7 = reversible defect).​
  • Diagnosis of Heart Disease (target): Integer value ranging from 0 to 4, indicating the presence and severity of heart disease.

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