waterQuality1 Dataset
A comprehensive overview of water quality in an urban environment, including chemical and biological parameters that can be analyzed to determine the safety and quality of water.
- Labeling Type: Water Quality
- Data Format: Tabular
- Data Type: Synthetic Data
Main Product
Data Quantity (Samples)
Total Price
$ 4,300
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About Dataset
1) Data Introduction
• The Water Quality Dataset is a synthetic dataset created to represent the quality of water in an urban environment. This dataset includes various parameters such as levels of aluminium, ammonia, arsenic, barium, and other chemicals, as well as the presence of bacteria and viruses. Each parameter is measured in parts per million (ppm) or parts per billion (ppb). The dataset also includes a binary classification indicating whether the water is safe or not (is_safe). This dataset can be used for educational purposes, to practice data analysis, and to develop predictive models for water quality assessment.2) Data Utilization
(1) Water quality data has characteristics that: • It includes numerous chemical and biological parameters that can be analyzed to determine the safety and quality of water. This information is critical for environmental monitoring, public health, and regulatory compliance. (2) Water quality data can be used to: • Environmental Monitoring: Helps in tracking and assessing the quality of water bodies, identifying pollution sources, and implementing corrective actions. • Public Health: Assists in ensuring that drinking water meets safety standards to prevent waterborne diseases and health issues. • Predictive Modeling: Supports the development of models to predict water quality based on chemical and biological 1)parameters, enabling proactive measures for water safety.Meta Data
Perfect
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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