PRD: Enhancing Predictive Business Decisions


The purpose of this document is to outline the requirements for implementing a machine learning system to enhance predictive business decisions. The system will utilize historical data to make predictions about future business outcomes, which will be used to inform strategic decision-making.


  • Phase 1: Requirements gathering and project planning – 2 weeks
  • Phase 2: Data collection and preparation – 4 weeks
  • Phase 3: Model development and testing – 8 weeks
  • Phase 4: Deployment and user training – 4 weeks
  • Phase 5: Ongoing maintenance and improvement – ongoing

Estimated Effort

  • Data collection and preparation: 2 FTE
  • Model development and testing: 3 FTE
  • Deployment and user training: 1 FTE
  • Ongoing maintenance and improvement: 1 FTE

Minimum Viable Product (MVP)

The MVP for this project will include:

  • A working machine learning model that can make predictions about future business outcomes
  • A user-friendly interface for inputting data and viewing predictions
  • A set of predefined metrics for evaluating the performance of the model


  • Prediction accuracy: The percentage of predictions that are correct
  • Precision: The percentage of true positive predictions among all positive predictions
  • Recall: The percentage of true positive predictions among all actual positive outcomes
  • F1 Score: The harmonic mean of precision and recall
  • AUC-ROC: Area under the receiver operating characteristic curve


  • A dashboard will be developed to display the performance metrics of the model
  • The dashboard will be accessible to authorized users, and will allow users to view predictions, view data, and interact with the model
  • The dashboard will allow the users to filter the data based on different parameters, such as date, location, product, etc.
  • The dashboard will be updated in real-time and will be accessible via web or mobile.


This project aims to enhance predictive business decisions by developing a machine learning system that can make predictions about future business outcomes. The system will be developed in a phased approach, with an MVP that includes a working model, a user-friendly interface, and predefined metrics for evaluating performance. Ongoing maintenance and improvement will be an important aspect of the project to ensure the accuracy and relevance of the model.