Diabetes-Feature-Engineering - A machine learning model that can predict whether people have diabetes when their characteristics are specified

Last update: Feb 23, 2022

Overview

Diabetes-Feature-Engineering

Aim

Developing a machine learning model that can predict whether people have diabetes when their characteristics are specified. Perform the necessary data analysis and feature engineering steps before developing the model.

Information about the dataset

The dataset is part of the large dataset held at the National Institutes of Diabetes-Digestive-Kidney Diseases in the USA. On Pima Indian women aged 21 and over living in Phoenix, the 5th largest city in the State of Arizona in the USA. Data used for diabetes research. The target variable is specified as "outcome"; 1 indicates positive diabetes test result, 0 indicates negative.

Variables

Pregnancies: Number of pregnancies Glucose Oral: 2-hour plasma glucose concentration in glucose tolerance test Blood Pressure: Blood Pressure (mm Hg) SkinThickness : Skin Thickness Insulin: 2-hour serum insulin (mu U/ml) DiabetesPedigreeFunction: Function (2 hour plasma glucose concentration in oral glucose tolerance test) BMI : Body mass index Age : Age (years) Outcome: Have the disease (1) or not (0)

Diabetes-Feature-Engineering - A machine learning model that can predict whether people have diabetes when their characteristics are specified

Related tags

Overview

Diabetes-Feature-Engineering

Aim

Information about the dataset

Variables

Owner

Özlem Taşkın

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Diabetes-Feature-Engineering - A machine learning model that can predict whether people have diabetes when their characteristics are specified

Related tags

Overview

Diabetes-Feature-Engineering

Aim

Information about the dataset

Variables

Owner

Özlem Taşkın

Code for EmBERT, a transformer model for embodied, language-guided visual task completion.

Memoized coduals - Shows that it is possible to implement reverse mode autodiff using a variation on the dual numbers called the codual numbers

TraND: Transferable Neighborhood Discovery for Unsupervised Cross-domain Gait Recognition.

POPPY (Physical Optics Propagation in Python) is a Python package that simulates physical optical propagation including diffraction

The personal repository of the work: *DanceNet3D: Music Based Dance Generation with Parametric Motion Transformer*.

Deep Learning (with PyTorch)

Semantic Segmentation of images using PixelLib with help of Pascalvoc dataset trained with Deeplabv3+ framework.

A tensorflow implementation of Fully Convolutional Networks For Semantic Segmentation

Framework that uses artificial intelligence applied to mathematical models to make predictions

Official PyTorch implementation of the ICRA 2021 paper: Adversarial Differentiable Data Augmentation for Autonomous Systems.

Source code for Transformer-based Multi-task Learning for Disaster Tweet Categorisation (UCD's participation in TREC-IS 2020A, 2020B and 2021A).

Demonstration of the Model Training as a CI/CD System in Vertex AI

Phonetic PosteriorGram (PPG)-Based Voice Conversion (VC)

Code for Overinterpretation paper Overinterpretation reveals image classification model pathologies

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