A Machine Learning-based system to predict the weight of the baby which is going to born by using the various data points from a pregnant lady.
Purpose: Help to arrange better medical care for the child in advance.
Connection to Business: Thentelligent Machine learning model can be easily transformed into a complete software solution and can be provided to hospitals as a service with subscriptions.It can boost the quality of any hospital and save a lot of time and other things by avoiding any emergency treatment.
Achieved 94% accuracy, which is fantastic.
Successfully deployed on Google Cloud Platform with the model pipeline for contentious training.
Understanding the impact of each feature on the baby’s weight was required a good study on medical content.
Implementation of with-ultrasound and without-ultrasound scenario was a tricky challenge.
Learn the Google Transfer Appliance to handle large data.
ETL Pipelines patterns
I have implemented the main logic of the model
Finalize the complete workflow to deploy the model.
Data Size: ~25GB
Python3 TensorFlow Numpy Pandas Google ML Engine Google cloud DataFlow Flask