Francis Odo
This project is focused on demonstrating and articulating how Machine Learning applications can be applied to the Healthy Living Partner mobile app, which is a product solution for achieving a healthy lifestyle through controlled and monitored habits.
Part I
As a component within a larger solution, the machine learning aspect of the product is the core decision support unit. It takes in user data, analyze, train, test using classification model in supervised learning.
The following files are provided:
- ML Application In HLP Project Proposal.pdf - A proposal that articulates the product solution
- healthy-living-partner.ipynb - code in Jupyter Notebook
- ML Application In Healthy Living Partner Project Report.pdf - Project report
Machine Learning algorithm is the main driver behind decisions made in terms of what actions to take, as well as, recommend to the user. It collects and analyzes data generated by the user for guidance on maintaining a healthy lifestyle. For these reasons it is absolutely necessary for the Machine learning algorithm to continue to perfect itself in learning, training and making predictions in support of decisions.
Part II (Work in progress)
The development of the server side of the application in Python 3, hosted on Flask Framework, which consists of the main product features
Product Features
Health Status Check (Health Status)
Provides analytical report of user health condition
Glucose/Sugar-Level Monitor (Glucose Monitor)
Provides the user with measures of Blood Glucose level
Performance Monitor (Performance Monitor)
Provides the user with an analysis and percentage rating of completion of tasks in a routine.
Application Programming Interface (API)
Application Programing Interface for access to other data collecting and monitoring devices
See the Prototype section of the report for a preview.