Best Selling Steam Games of All Time Dataset
A comprehensive overview of the best-selling Steam games of all time, including game names, review ratings, release dates, developer names, genre tags, support operating systems, support languages, price, play times, age limits, user ratings, difficulty levels, and download counts.
- Labeling Type: supported_os
- Data Format: Tabular
- Data Type: Synthetic Data
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Data Quantity (Samples)
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$ 5,600
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About Dataset
1) Data Introduction
• The Best-Selling Steam Games of All Time Dataset contains 2,380 of the world's best-selling Steam games, collected from the 'Best sellers' page of the Steam official store as of June 1, 2025.2) Data Utilization
(1) Best-Selling Steam Games of All Time Dataset has characteristics that: • This dataset incorporates data collected from three locations: Steam, GameFAQs, and SteamDB, and consists of 15 columns: game name, review rating, release date, developer, genre tag, support operating system, support language, price, play support, age limit, user rating, difficulty, play time, and expected downloads. • Each game's genre and tag has been consistently refined using only 42 standardized representative tags, and all data is completely available with no missing values. (2) Best-Selling Steam Games of All Time Dataset can be used to: • Game Market Analysis: Based on various information such as sales volume, price, genre, user evaluation, support platform, etc., it can be used to analyze trends, popular genres, price policies, and success factors in the Steam game market. • Recommendation system and game development: You can build a personalized game recommendation system using various characteristics such as user rating, tag, difficulty, and play time, or use it as benchmarking data when planning new games.Meta Data
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Outstanding
Data Samples
Utility
| Downstream Classification (▲) | Entropy (▲) | MMD (▼) | 2D Correlation Similarity (▼) | One Class Classification (▼) | Duplication Rate (▼) | |
|---|---|---|---|---|---|---|
| Total | 0 | 0 | 0 | 0 | 0 | 0 |
| Suitability | OK | OK | OK | OK | OK | OK |
The higher the value, the better (▲)
Model Performance
Downstream classification accuracy is an indicator used to evaluate the usefulness of synthetic data. It measures whether synthetic data performs similarly to real data. The method involves training the same model separately on real data and synthetic data, and then comparing the accuracies of the two models. Interpretation: A high accuracy rate means that the model trained on synthetic data performs similarly to the one trained on real data, indicating that the synthetic data is of high quality and well represents the real data.
The closer the value is to 0 or 1, or the lower the number, the better (▼)
Quality
MMD (Maximum Mean Discrepancy) is a metric used to assess the similarity between two probability distributions. It is commonly used to compare generated data with real data. High MMD score: A score above 0.05 indicates that the two distributions may differ. Low MMD score: Indicates that the generated data is similar to the real data. A score close to 0 is preferable, and a score below 0.01 suggests that the two data distributions are nearly indistinguishable.
Quality
2D Relationship Similarity measures the similarity in correlation structures between two datasets by comparing the correlation coefficients of columns in the original and generated data. High value (0.05 or above): Suggests differences in correlation structures, indicating the generated data may differ from the original. Low value: Indicates that the correlation structure of the generated data is similar to the original data. For instance, a 2D Relationship Similarity below 0.01 suggests the datasets are very similar.
Duplication Rate
Duplication Rate represents the proportion of identical or nearly identical items within a dataset. It is calculated by dividing the number of duplicate items by the total number of items. High Duplication Rate: Indicates lower data diversity and potential quality issues, which can reduce the reliability of analysis and models. Low Duplication Rate: Suggests higher data diversity and better quality.
Privacy
| Identification Risk (▼) | Linkage Risk (▼) | Inference Risk (▼) (Adjust by subtracting 0.5) | |
|---|---|---|---|
| Total | 0 | 0 | 0 |
| Suitability | OK | OK | OK |
The closer to zero or the lower the value, the better (▼)
Structural Similarity
Identification risk assesses how well synthetic data protects the privacy of the original data. It measures the likelihood that synthetic data can match records from the original data, thereby evaluating the potential for identifying specific individuals. Interpretation: A value closer to 0 indicates that the synthetic data is effectively protecting personal information. The level of risk considered safe can vary depending on the nature and sensitivity of the information contained in the data.
Perceptual Similarity
Linkage risk assesses the risk of inferring sensitive information from the original data using synthetic data. It measures the proportion of quasi-identifier values in the synthetic data that match those in the original data when an attacker knows quasi-identifier information from the original data. High Duplication Rate: Indicates lower data diversity and potential quality issues, which can reduce the reliability of analysis and models. Interpretation: A lower risk indicates that the data is safer, meaning there is a reduced likelihood of inferring sensitive information.
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