Diabet Feature Engineering - Predict whether people have diabetes when their characteristics are specified

Overview

Diabet_Feature-Engineering

Aim

Predict whether people have diabetes when their characteristics are specified.It is desired to develop a machine learning model that can model data analysis and feature engineering steps required before development you are expected to do.

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. in the USA on Pima Indian women aged 21 and over living in Phoenix, the 5th largest city in the State of Arizona.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 (Small 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 (year) Outcome:Have the disease (1) or not (0)

Owner
Şebnem
Şebnem
Pomodoro timer that acknowledges the inexorable, infinite passage of time

Pomodouroboros Most pomodoro trackers assume you're going to start them. But time and tide wait for no one - the great pomodoro of the cosmos is cold

Glyph 66 Dec 13, 2022
Fast and scalable uncertainty quantification for neural molecular property prediction, accelerated optimization, and guided virtual screening.

Evidential Deep Learning for Guided Molecular Property Prediction and Discovery Ava Soleimany*, Alexander Amini*, Samuel Goldman*, Daniela Rus, Sangee

Alexander Amini 75 Dec 15, 2022
KaziText is a tool for modelling common human errors.

KaziText KaziText is a tool for modelling common human errors. It estimates probabilities of individual error types (so called aspects) from grammatic

ÚFAL 3 Nov 24, 2022
It's like Shape Editor in Maya but works with skeletons (transforms).

Skeleposer What is Skeleposer? Briefly, it's like Shape Editor in Maya, but works with transforms and joints. It can be used to make complex facial ri

Alexander Zagoruyko 1 Nov 11, 2022
Retina blood vessel segmentation with a convolutional neural network

Retina blood vessel segmentation with a convolution neural network (U-net) This repository contains the implementation of a convolutional neural netwo

Orobix 1.2k Jan 06, 2023
Human Activity Recognition example using TensorFlow on smartphone sensors dataset and an LSTM RNN. Classifying the type of movement amongst six activity categories - Guillaume Chevalier

LSTMs for Human Activity Recognition Human Activity Recognition (HAR) using smartphones dataset and an LSTM RNN. Classifying the type of movement amon

Guillaume Chevalier 3.1k Dec 30, 2022
Pytorch implemenation of Stochastic Multi-Label Image-to-image Translation (SMIT)

SMIT: Stochastic Multi-Label Image-to-image Translation This repository provides a PyTorch implementation of SMIT. SMIT can stochastically translate a

Biomedical Computer Vision Group @ Uniandes 37 Mar 01, 2022
Point detection through multi-instance deep heatmap regression for sutures in endoscopy

Suture detection PyTorch This repo contains the reference implementation of suture detection model in PyTorch for the paper Point detection through mu

artificial intelligence in the area of cardiovascular healthcare 3 Jul 16, 2022
Distributed Evolutionary Algorithms in Python

DEAP DEAP is a novel evolutionary computation framework for rapid prototyping and testing of ideas. It seeks to make algorithms explicit and data stru

Distributed Evolutionary Algorithms in Python 4.9k Jan 05, 2023
PyTorch-based framework for Deep Hedging

PFHedge: Deep Hedging in PyTorch PFHedge is a PyTorch-based framework for Deep Hedging. PFHedge Documentation Neural Network Architecture for Efficien

139 Dec 30, 2022
The first public PyTorch implementation of Attentive Recurrent Comparators

arc-pytorch PyTorch implementation of Attentive Recurrent Comparators by Shyam et al. A blog explaining Attentive Recurrent Comparators Visualizing At

Sanyam Agarwal 150 Oct 14, 2022
A short and easy PyTorch implementation of E(n) Equivariant Graph Neural Networks

Simple implementation of Equivariant GNN A short implementation of E(n) Equivariant Graph Neural Networks for HOMO energy prediction. Just 50 lines of

Arsenii Senya Ashukha 97 Dec 23, 2022
Object Tracking and Detection Using OpenCV

Object tracking is one such application of computer vision where an object is detected in a video, otherwise interpreted as a set of frames, and the object’s trajectory is estimated. For instance, yo

Happy N. Monday 4 Aug 21, 2022
Tracking code for the winner of track 1 in the MMP-Tracking Challenge at ICCV 2021 Workshop.

Tracking Code for the winner of track1 in MMP-Trakcing challenge This repository contains our tracking code for the Multi-camera Multiple People Track

DamoCV 29 Nov 13, 2022
Pytorch implementation of PCT: Point Cloud Transformer

PCT: Point Cloud Transformer This is a Pytorch implementation of PCT: Point Cloud Transformer.

Yi_Zhang 265 Dec 22, 2022
DECAF: Deep Extreme Classification with Label Features

DECAF DECAF: Deep Extreme Classification with Label Features @InProceedings{Mittal21, author = "Mittal, A. and Dahiya, K. and Agrawal, S. and Sain

46 Nov 06, 2022
DiSECt: Differentiable Simulator for Robotic Cutting

DiSECt: Differentiable Simulator for Robotic Cutting Website | Paper | Dataset | Video | Blog post DiSECt is a simulator for the cutting of deformable

NVIDIA Research Projects 73 Oct 29, 2022
[ ICCV 2021 Oral ] Our method can estimate camera poses and neural radiance fields jointly when the cameras are initialized at random poses in complex scenarios (outside-in scenes, even with less texture or intense noise )

GNeRF This repository contains official code for the ICCV 2021 paper: GNeRF: GAN-based Neural Radiance Field without Posed Camera. This implementation

Quan Meng 191 Dec 26, 2022
Information Gain Filtration (IGF) is a method for filtering domain-specific data during language model finetuning. IGF shows significant improvements over baseline fine-tuning without data filtration.

Information Gain Filtration Information Gain Filtration (IGF) is a method for filtering domain-specific data during language model finetuning. IGF sho

4 Jul 28, 2022
A colab notebook for training Stylegan2-ada on colab, transfer learning onto your own dataset.

Stylegan2-Ada-Google-Colab-Starter-Notebook A no thrills colab notebook for training Stylegan2-ada on colab. transfer learning onto your own dataset h

Harnick Khera 66 Dec 16, 2022