Personal project using Pima Indians Diabetes to analyse it and make predictions using Machine Learning techniques.

Diabetes Risk Prediction System using Machine Learning and React. The project performs clinical risk assessment based on health and lifestyle inputs. Features include data preprocessing, ML classification, and a React UI with age ranges, tooltips, and risk-based outputs.

Diabetes Prediction is my weekend practice project. In this I used KNN Neighbors Classifier to trained model that is used to predict the positive or negative result. Given set of inputs are BMI(Body Mass Index),BP(Blood Pressure),Glucose Level,Insulin Level based on this features it predict whether you have diabetes or not.

Final Year Project to train the model for Diabetes and Parkinson Prediction showcase with streamlit interface.

🚀 Diabetes Prediction using Machine Learning (KNN Model) This project uses K-Nearest Neighbors (KNN) to predict diabetes based on health parameters. It includes data preprocessing, visualization, model training, hyperparameter tuning, and performance evaluation.

🚀 Diabetes Prediction Machine Learning project using the Pima Indians Diabetes Database. 🩺

Diabetes prediction using data mining functions.Using the Decision Tree, the accuracy obtained is 87.1794% and Average precision-recall score obtained is 0.66. n this project we used different types of machine learning algorithm, and predicted that a person will be having diabetes in the future or not. And how much is it accurate given the responses that the person has diabetes.

This project includes multiple disease prediction models for diabetes, Parkinson's disease, heart disease, and breast cancer.

Diabetes-Prediction is a project aimed at developing a predictive model for diabetes using logistic regression. This repository contains the code and resources needed to create a user-friendly application that predicts the likelihood of an individual having diabetes based on various input features.

Final year major project. AI based Diabetes prediction model using ML algorithms and HTML CSS Flask User Interface.

This project, ‘Heart Stroke Prediction’ is a machine learning based software project to predict whether the person is at risk of getting a heart stroke or not. Heart diseases have become a major concern to deal with as studies show that the number of deaths due to heart diseases has increased significantly over the past few decades in India. World Health Organization has estimated 12 million deaths occur worldwide, every year due to Heart diseases. Half the deaths in the United States and other developed countries are due to cardio vascular diseases. Traditionally, they have relied on standard assessments of cholesterol, blood pressure and health conditions such as diabetes to predict whether a patient is likely to suffer a heart attack.

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This project involves the comparison of performance of ML and DL algorithm for the prediction of diabetes.

Internship / Field attachment: Personal machine learning project on building a Diabetes disease prediction model with implementation of a web application.

Diabetes Platform is a web application for diabetes prediction and analysis. This project aims to predict the risk of diabetes using machine learning techniques in Python and analysis using Power BI. The model development process involved extensive research and work to create an accurate and reliable prediction system.

A diabetes prediction project involves creating a model that can predict the likelihood of an individual developing diabetes based on certain features or parameters. This type of project typically falls under the domain of machine learning and data science

Diabetes Prediction Project Using Machine Learning. This app is a simple web application using the Flask framework, where users can input health data (like glucose levels, BMI, etc.) to predict if they are diabetic or not based on a Logistic Regression model.

This project is a Streamlit-based web application designed to predict the likelihood of various diseases based on user-provided health data. By leveraging machine learning models, the app offers predictions for conditions such as diabetes, heart disease, Parkinson's disease, lung cancer, and hypothyroidism.

This diabetes prediction webapp. In this project, a Kaggle database is used to identify whether someone has diabetes based on data such as BMI, glucose level, blood pressure, etc

HealthOrzo is a Disease Prediction and Information Website. It is user friendly and very dynamic in it's prediction. The Project Predicts 4 diseases that are Diabetes , Kidney Disease , Heart Ailment and Liver Disease . All these 4 Machine Learning Models are integrated in a website using Flask at the backend .

A supervised machine learning project that analyzes the robustness of logistic regression models against adversarial attacks, demonstrated through a Streamlit-based diabetes prediction interface.

Multiple Disease Prediction System using Machine Learning: This project provides a streamlit web application for predicting multiple diseases, including diabetes, Parkinson's disease, and heart disease, using machine learning algorithms. The prediction models are deployed using Streamlit, a Python library for building interactive web applications.

This project involves the prediction of diabetes progression using Ridge Regression in Jupyter Notebook. The dataset contains features such as glucose level, blood pressure, body mass index, and more. Through this analysis, we aim to build a regression model that accurately predicts the progression of diabetes based on the given input features.

This project is a Flask-based web application that predicts the likelihood of three medical conditions: Diabetes, Heart Disease, and Parkinson's Disease. It uses machine learning models to make predictions based on user-provided health parameters.

This python file will compare accuracy of different ML and AI models on the Pima Indian Diabetes dataset and a small GUI to make predictions from the logistic regression model. This project was done as a part of Mini project for my sem 4 Engineering SE- IT

Diabetes Mellitus (TY2) has secured the status of a global pandemic. Thus, the diagnosis of the disease at an early stage plays a very significant role as the early prediction of the disease will minimize the health risks associated with the disease. This projects aims to predict whether a person is diabetic or not via six different classification algorithms namely Support Vector Machine(SVC), K- Nearest Neighbour(Knn), Decision Tree, Naive Bayes, Logistic Regression and Random Forest. Moreover, early prediction of the diabetes disease with a higher accuracy can be found using various machine learning techniques.

Diabetes Prediction Project

A Streamlit ML app to predict diabetes status based on health indicators.

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Machine Learning and AI course by ICT Academy of Kerala include projects like BigMartSalePrediction, Rock_vs_Mine_Prediction, Breast Cancer Classifiation, diabetes prediction, Calories Burnt Prediction, HousePrediction, calorie_Prediction