The primary goal of this software is to provide a diagnostic prediction of whether a patient has diabetes. It achieves this through a robust machine learning model that has been meticulously trained to accurately discern the presence or absence of diabetes in a patient.
The underlying dataset originates from the National Institute of Diabetes and Digestive and Kidney Diseases. This dataset encompasses crucial diagnostic measurements that have been instrumental in training the machine learning model. These measurements include various medical predictor variables such as the number of pregnancies a patient has had, their BMI (Body Mass Index), insulin levels, age, and more.
In essence, the dataset comprises several medical predictor variables, with the primary target being the variable labeled as outcome. The culmination of these variables contributes to the efficacy of the machine learning model in making informed predictions regarding a patient’s diabetic status.
Resources used in Development
to accomplish this task we used the following stack:
- Python
- Jupyter NoteBook
- Scikit-Learn
- Docker
- Flask API
- React Js
- github
Project link
Github Repository link