Design a Predictive Analytics System

Functional Requirements:

• Data Ingestion: Ability to collect and process data from multiple sources in real-time or batch mode.
• Data Preprocessing: Clean, transform, and normalize data to ensure quality and consistency.
• Model Training: Develop and train machine learning models using historical data.
• Prediction Generation: Generate predictions based on new input data using trained models.
• Result Visualization: Provide visual representation of predictions and insights for end-users.
• User Interface: Develop a user-friendly interface for interacting with the system.
• Feedback Loop: Implement a mechanism for users to provide feedback on predictions to improve model accuracy.
• Alert System: Notify users of significant predictions or anomalies.

Non-Functional Requirements:

• Scalability: System should handle increasing amounts of data and users without performance degradation.
• Performance: Ensure low latency in data processing and prediction generation.
• Reliability: System should be highly available with minimal downtime.
• Security: Protect data integrity and ensure secure access to the system.
• Compliance: Adhere to relevant data protection regulations and standards.
• Maintainability: Codebase should be easy to update and maintain.
• Interoperability: Ability to integrate with existing systems and third-party services.
• Usability: Ensure the system is intuitive and easy to use for non-technical users.

Constraints:

• Budget: Limited financial resources for development and maintenance.
• Time: Project must be completed within a specified timeframe.
• Data Availability: Dependence on the availability and quality of external data sources.
• Resource Limitations: Limited computational resources for model training and deployment.
• Regulatory Constraints: Compliance with industry-specific regulations and standards.