A machine learning project to predict diabetes using a Support Vector Machine (SVM) model. The project utilizes the Pima Indians Diabetes Database to train and evaluate the model, providing performance metrics such as accuracy, precision, recall, and F1-score.

The primary aim of this project is to accurately identify individuals at risk of diabetes based on different features.

In this project, different machine learning approaches are used to detect the diabetes in patients using the PIMA Indians diabetes dataset.

Ensemble Learning

We run the dataset of Pima indians through different learnt Machine Learning techniques using R and then interpreting the results in terms of our research questions and purpose. From this, we were able to deduce the best algorithm as well as the most influential variables for the onset of diabetes with proper mathematical reasoning provided.

This is a Machine learning project trained for Diabetes Prediction using Multiple Ensemble models like Random Forest, Ada boost, cat boost and a few more. It is trained on the Pima Indian Diabetes Dataset.

Machine Learning model for Early Stage Diabetes Prediction

This project focuses on developing a predictive system for analyzing the Pima Indians Diabetes dataset and predicting the likelihood of individuals having diabetes

The objective of the project is to diagnostically predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the dataset. Several constraints were placed on the selection of these instances from a larger database. In particular, all patients here are females at least 21 years old of Pima Indian heritage.

Pima Diabetes Outcome

Boosting Implementation (Ada-Boosting, Gradient Boosting, XGB)

Implement a feedforward neural network for Pima Indians onset of diabetes dataset using TensorFlow & Keras

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.

Machine Learning Basics

I made some projects using PyTorch

Repo which includes the medical data sets used in a feature selection paper proposed by OASYS group

This project uses a Machine learning approach to detect whether the patient has diabetes or not using different machine learning algorithms.

Pima Indians Diabetes Classification with custom Naive Bayes

Predict diabetes onset for women of Pima Indian Heritage (age 21+) using data from the National Institute of Diabetes and Digestive and Kidney Diseases.