Entities and Attributes in Predictive Analytics Systems
Entities in a Predictive Analytics System represent various data sources, features, models, predictions, and evaluations, while attributes describe their characteristics. Common entities and their attributes include:
1. Data Source
- DataSourceID (Primary Key): Unique identifier for each data source.
- Name: Name or description of the data source.
- Type: Type of data source (e.g., database, file, API).
2. Feature
- FeatureID (Primary Key): Unique identifier for each feature.
- Name: Name or description of the feature.
- Type: Type of feature (e.g., numerical, categorical).
3. Model
- ModelID (Primary Key): Unique identifier for each predictive model.
- Name: Name or description of the model.
- Algorithm: Algorithm used to build the model (e.g., linear regression, decision tree, neural network).
4. Prediction
- PredictionID (Primary Key): Unique identifier for each prediction.
- ModelID (Foreign Key): Reference to the predictive model used for the prediction.
- Timestamp: Date and time of the prediction.
- Predicted Value: Predicted outcome or target variable.
5. Evaluation
- EvaluationID (Primary Key): Unique identifier for each model evaluation.
- ModelID (Foreign Key): Reference to the predictive model evaluated.
- Metric: Evaluation metric used (e.g., accuracy, precision, recall).
- Value: Value of the evaluation metric.
How to Design Database for Predictive Analytics
Predictive analytics is a powerful tool used across various industries to forecast future trends, behaviors, and outcomes based on historical data and statistical algorithms. A well-designed database architecture forms the foundation for storing, processing, and analyzing large amounts of data to generate predictive insights.
In this article, we will learn about How Database Design for Predictive Analytics by understanding various aspects of the article in detail.
Contact Us