An automated insulin delivery app for iOS, built on LoopKit
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- Swift
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diabetes-prediction
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In this project, the objective is to predict whether the person has Diabetes or not based on various features like Number of Pregnancies, Insulin Level, Age, BMI.
heroku flask machine-learning deployment scikit-learn machine-learning-algorithms health-data mini-projects end end-to-end-machine-learning diabetes-prediction mini-project-on-machine-learning machine-learnin-model-deplyment end-end-machine-learning-project machine-learning-mini-project
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Health Check ✔ is a Machine Learning Web Application made using Flask that can predict mainly three diseases i.e. Diabetes, Heart Disease, and Cancer.
css html open-source flask machine-learning cancer jupyter-notebook python3 kaggle diabetes flutter collaborate diseases cancer-detection heart-disease diabetes-prediction heartdisease student-vscode
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Predict Diabetes using Machine Learning.
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Machine learning approach to detect whether patien has the diabetes or not. Data cleaning, visualization, modeling and cross validation applied
machine-learning detection machine-learning-algorithms naive-bayes-classifier accuracy logistic-regression diabetes support-vector-machines health-data knn-classification random-forest-classifier diabetes-detection diabetes-prediction
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Multiple disease prediction such as Diabetes, Heart disease, Kidney disease, Breast cancer, Liver disease, Malaria, and Pneumonia using supervised machine learning and deep learning algorithms.
machine-learning deep-learning cnn neural-networks breast-cancer-prediction classification-model diabetes-prediction heart-disease-prediction malaria-prediction liver-disease-prediction kidney-disease-prediction multiple-disease-prediction pneumonia-prediction
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A ready-to-use framework of the state-of-the-art models for structured (tabular) data learning with PyTorch. Applications include recommendation, CRT prediction, healthcare analytics, anomaly detection, and etc.
tabular-data pytorch attention structured-data movielens-dataset diabetes-prediction healthcare-analysis criteo-dataset avazu-dataset frappe-dataset 121-uci-datasets log-based-anomaly-detection
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This project aims to predict the type 2 diabetes, based on the dataset. It uses machine learning model,which is trained to predict the diabetes mellitus before it hits.
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I used six classification techniques, artificial neural network (ANN), Support Vector Machine (SVM), Decision tree (DT), random forest (RF), Logistics Regression (LR) and Naïve Bayes (NB)
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This website provides a platform for users to predict their likelihood of developing diabetes based on various factors.
javascript css python html flask reactjs linear-regression kaggle hacktoberfest tailwindcss diabetes-prediction gssoc diabetes-dateset-analysis diabetes-prediction-model gssoc24
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This repo contains 4 different projects. Built various machine learning models for Kaggle competitions. Also carried out Exploratory Data Analysis, Data Cleaning, Data Visualization, Data Munging, Feature Selection etc
data-science machine-learning exploratory-data-analysis kaggle kaggle-competition data-analysis house-price-prediction data-munging datavisualization datacleaning datamunging diabetes-prediction creditcardfrauddetection bankloanprediction
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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.
python machine-learning sklearn machinelearning knn-classification diabetes-detection knn-classifier diabetes-prediction pima-indians-diabetes
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💉🤒 The project was built using maven project utilized WEKA in building the decision tree model and JavaFX in building GUI 💉🤒
java maven javafx decision-trees weka-library maven-pom diabetes-prediction artificial-intelligence-courses
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A Machine Learning–based diabetes prediction system built using Python and Logistic Regression. The model analyzes clinical health parameters to classify whether a patient is likely to have diabetes.
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AI Nexus 🌟 is a streamlined suite of AI-powered apps built with Streamlit. It features 👗 StyleScan for fashion classification, 🩺 GlycoTrack for diabetes prediction, 🔢 DigitSense for digit recognition, 🌸 IrisWise for iris species identification, 🎯 ObjexVision for object recognition, and 🎓 GradeCast for GPA prediction with detailed insights.
machine-learning linear-regression python3 datascience mnist-classification keras-classification-models tensorflow-models gradient-boosting knn-classification iris-flower-classification xgboost-algorithm good-first-issue random-forest-regression knn-algorithm diabetes-prediction cifar10-classification streamlit diabetes-risk-prediction mnist-fashion-classification gpa-prediction
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Prediction of diabetes using logistic regression
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This educational repository focuses on working with three types of medical data: tabular data, ECG and EEG signals. It provides implementations of machine learning and deep learning models for processing and analyzing these medical data, with practical projects based on recent research articles.
machine-learning random-forest numpy svm scikit-learn pandas pytorch artificial-intelligence ensemble-learning matplotlib ecg-signal mit-bih-database motor-imagery eegnet diabetes-prediction separable-convolution depthwise-convolutions ecg-arrhytmia-detection 5-finger-classification
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Deployed medical apps on streamlit
data-science machine-learning dna-sequencing deplyment diabetes-prediction heart-disease-prediction streamlit
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A comprehensive project to predict and analyze diabetes health data using advanced machine learning models, including Logistic Regression, Random Forest, and XGBoost. 📊🔍
python data-science machine-learning deep-learning analytics random-forest prediction health data-visualization artificial-intelligence healthcare xgboost classification logistic-regression mlops diabetes-prediction medical-analysis
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