Diabetes Prediction Bot

Overview:
The “Diabetes Prediction Bot” is a machine learning-based software application designed to predict whether an individual is likely to have diabetes. Users can provide relevant medical and lifestyle data, and the bot analyzes this information to provide a predictive outcome. This tool is useful for early detection and risk assessment of diabetes, allowing individuals to take preventive measures and seek medical advice when necessary.

Files:

  • trained_model.sav: This file contains the trained machine learning model for diabetes prediction.
  • Diabetes-Prediction-bot deployment in Spyder.py: This Python script is used for deploying the Diabetes Prediction Bot in the Spyder IDE and running it using ‘Streamlit’.
  • diabetes.csv: This data file contains the dataset used to train and test the diabetes prediction model.
  • diabetes_prediction.py: This Python script is designated for use in Google Colab.

Usage:

  • Ensure you have the necessary dependencies and libraries installed (e.g., NumPy, Pandas, Scikit-Learn).
  • Run the provided Python script (Spyder multiple disease prediction(combo of heart & diabetes prediction bot).py) in the Spyder IDE to deploy the bot.
  • Provide the required medical and lifestyle data as input.
  • The bot will analyze the data and provide a prediction regarding the likelihood of diabetes.

GitHub Repository: Diabetes Prediction Bot
Deployed Model: Diabetes Prediction Bot

This repository has garnered 2 stars, 5 clones, and 50 views, making it a popular tool for health-conscious individuals seeking quick and reliable body metrics assessment. Give it a try and stay informed about your body composition!

Check out the other posts related to this one:-


Discover more from Coursity

Subscribe to get the latest posts sent to your email.

Leave a Comment

Your email address will not be published. Required fields are marked *

Discover more from Coursity

Subscribe now to keep reading and get access to the full archive.

Continue reading

Scroll to Top