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
- 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.
AJ is a highly experienced web developer with over 25 years of experience and a proven track record of delivering successful projects on time and within budget. He is skilled in the full stack of web development and has the ability to lead and optimize agile teams. In addition, AJ has a deep interest in AI development and blockchain technologies, with a solid understanding of the fundamental concepts and technologies behind them. His experience and knowledge make him an excellent candidate for any organization looking to integrate these cutting-edge technologies into their products and services.