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diabetes-detection

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This project aims to analyze diabetes data using data management, captivating visualizations, and cutting-edge machine learning techniques to predict the presence of diabetes in individuals. Our robust dataset includes comprehensive health exam results and family history.

  • Updated Jun 30, 2023
  • Jupyter Notebook

This project is using machine learning to predict the likelihood of a person having diabetes. The dataset used in this project is the "diabetes.csv" file, which contains information on various health factors such as glucose levels, blood pressure, BMI, and age, among others. The goal is to use this data to train a machine learning model, specifical

  • Updated Apr 23, 2023
  • Jupyter Notebook

In this case, we train our model with several medical informations such as the blood glucose level, insulin level of patients along with whether the person has diabetes or not so this act as labels whether that person is diabetic or non-diabetic so this will be label for this case.

  • Updated Sep 16, 2022
  • Jupyter Notebook

This project focuses on predicting the likelihood of diabetes in individuals using logistic regression, a powerful machine learning algorithm. Additionally, we have developed a user-friendly web application that allows users to input relevant health metrics and receive instant predictions regarding their risk of diabetes.

  • Updated Mar 4, 2024
  • Jupyter Notebook

This repository contains the code and resources for a machine learning project aimed at diabetes detection. We have implemented multiple machine learning models and data visualization techniques to build an accurate diabetes detection system.

  • Updated Nov 1, 2023
  • Jupyter Notebook

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