https://github.com/ankushmallick1100/diabetes-prediction-using-maching-learning

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.
https://github.com/ankushmallick1100/diabetes-prediction-using-maching-learning

classification cross-validation decision-tree-classifier diabetes diabetes-prediction jupyter jupyter-notebook logistic-regression machine-learning machine-learning-algorithms numpy pandas python random-forest random-forest-classifier regression streamlit support-vector-machine xgboost xgboost-classifier

Last synced: about 2 months ago
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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.

• Host: GitHub
• URL: https://github.com/ankushmallick1100/diabetes-prediction-using-maching-learning
• Owner: ankushmallick1100
• Created: 2023-01-04T14:08:08.000Z (about 2 years ago)
• Default Branch: main
• Last Pushed: 2024-11-04T14:55:35.000Z (2 months ago)
• Last Synced: 2024-11-04T15:32:10.374Z (2 months ago)
• Topics: classification, cross-validation, decision-tree-classifier, diabetes, diabetes-prediction, jupyter, jupyter-notebook, logistic-regression, machine-learning, machine-learning-algorithms, numpy, pandas, python, random-forest, random-forest-classifier, regression, streamlit, support-vector-machine, xgboost, xgboost-classifier
• Language: Jupyter Notebook
• Homepage: https://diabetes-prediction-web-app-ankush-mallick.streamlit.app
• Size: 101 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# 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