Vehicle Sales Dataset
Detailed information on the sales transactions of various vehicles, allowing for the analysis of trends in the automotive market.
- Labeling Type: sellingprice
- Data Format: Tabular
- Data Type: Synthetic Data
Main Product
Data Quantity (Samples)
Total Price
$ 5,600
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About Dataset
1) Data Introduction
• The Vehicle Sales Data dataset contains information related to the sales transactions of various vehicles, making it useful for analyzing trends in the automotive market. • Key features include the vehicle's manufacturing year, brand, model, trim, body type, transmission type, Vehicle Identification Number (VIN), registered state, vehicle condition rating, mileage, exterior and interior colors, seller information, Manheim Market Report (MMR) values, selling prices, and sale dates.2) Data Utilization
(1) Characteristics of the Vehicle Sales Data • This dataset provides detailed vehicle information and sales transaction data, which is useful for analyzing trends in the automotive market. • It allows for the prediction of each vehicle's market value, helping to analyze market trends and price fluctuations. • It provides essential data for studying the relationship between the physical condition of vehicles and their prices. (2) Applications of the Vehicle Sales Data • Market Analysis: It is useful for studying trends in the automotive market, analyzing vehicle price fluctuations, and assessing market demand. • Predictive Modeling: This dataset can be used to develop models for predicting vehicle sale prices. • Business Insights: Automotive industry professionals can use this data to analyze consumer preferences, market demand, and pricing strategies to develop business strategies. ? Marketing Strategy Development: Automotive dealers and industry professionals can use this data to understand consumer preferences and develop targeted marketing strategies.Meta Data
Normal
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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