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This is a machine learning project that uses various machine learning algorithms to predict whether a patient is diabetic or not. Here various machine learning algorithms like SVM, RF Classifier, DT Classifier, KNN, LR , LRwith CV, NB Classifier, and XGB are used. For this work, a website is made with Python Streamlit library. Paper is ongoing.

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ankushmallick1100/Diabetes-prediction-using-maching-learning

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Diabetes Prediction using Maching Learning

Description

This is a machine learning work that uses various machine learning algorithms to predict whether a patient is diabetic or not (non-diabetic). Here, various type of machine learning algorithms like Support Vector Machine Classifier (SVM), Random Forest Classifier (RF), Decision Tree Classifier (DT), K-Nearest Neighbours (KNN), Logistic Regression (LR), Logistic Regression (LR) with Cross-Validation (CV), Naive Bayes Classifier (NB), and XGBoost Classifier (XGB) are used for this. A paper is written in this topic which is under ongoing.

Logistic Regression gives 83.62% testing accuracy which is the best testing accuracy among other machine learning models

Dataset Link

Dataset is present in Kaggle
Link: https://www.kaggle.com/datasets/uciml/pima-indians-diabetes-database?resource=download

Paper Link

Link: Paper writing is in under ongoing

Deployed Web App Link in public

Link: https://diabetes-prediction-web-app-ankush-mallick.streamlit.app

About

This is a machine learning project that uses various machine learning algorithms to predict whether a patient is diabetic or not. Here various machine learning algorithms like SVM, RF Classifier, DT Classifier, KNN, LR , LRwith CV, NB Classifier, and XGB are used. For this work, a website is made with Python Streamlit library. Paper is ongoing.

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