This project intends to use SVM supervised learning to determine whether or not an individual is diabetic given certain attributes.

Overview

Diabetes Prediction Using SVM

I explore a diabetes prediction algorithm using a Diabetes dataset. Using a Support Vector Machine for my prediction algorithm, I intend on predicting whether an individual has diabetes or not based on certain attributes provided within the dataset headers.

This table is taken from kaggle and contains data specifying diabetes data from a particular group, which may create difficulty in generalization.

"This dataset is originally from the National Institute of Diabetes and Digestive and Kidney Diseases. The objective is to predict based on diagnostic measurements whether a patient has diabetes. 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."

Overview

With Diabetes becoming a growing health issue in society, I would like to be able to predict to a certain accuracy the probability of an individual being diagnosed with diabetes given certain health attributes. The objective of this project aims to predict whether the person in question has Diabetes or not based on the below elements:

  • Pregnancies
  • Glucose
  • Blood Pressure
  • Skin Thickness
  • Insulin Level
  • BMI
  • Diabetes Pedigree
  • Age

Workflow

For the purposes of this notebook, I will use a subset of the Diabetes dataset provided by the following.

  1. Begin by collecting Diabetes data and understanding the content of the dataset
  2. Preprocess data
  3. Train - Test data split
  4. Feed a SVM model after determining data subset to be satisfactory

App Loading

  1. Run app.py
  2. Click on local host
Owner
Jeff Shen
Data Science @ UCSB
Jeff Shen
A note taker for NVDA. Allows the user to create, edit, view, manage and export notes to different formats.

Quick Notetaker add-on for NVDA The Quick Notetaker add-on is a wonderful tool which allows writing notes quickly and easily anytime and from any app

5 Dec 06, 2022
Source code for "FastBERT: a Self-distilling BERT with Adaptive Inference Time".

FastBERT Source code for "FastBERT: a Self-distilling BERT with Adaptive Inference Time". Good News 2021/10/29 - Code: Code of FastPLM is released on

Weijie Liu 584 Jan 02, 2023
Online Pseudo Label Generation by Hierarchical Cluster Dynamics for Adaptive Person Re-identification

Online Pseudo Label Generation by Hierarchical Cluster Dynamics for Adaptive Person Re-identification

TANG, shixiang 6 Nov 25, 2022
Official code repository for the EMNLP 2021 paper

Integrating Visuospatial, Linguistic and Commonsense Structure into Story Visualization PyTorch code for the EMNLP 2021 paper "Integrating Visuospatia

Adyasha Maharana 23 Dec 19, 2022
Taichi Course Homework Template

太极图形课S1-标题部分 这个作业未来或将是你的开源项目,标题的内容可以来自作业中的核心关键词,让读者一眼看出你所完成的工作/做出的好玩demo 如果暂时未想好,起名时可以参考“太极图形课S1-xxx作业” 如下是作业(项目)展开说明的方法,可以帮大家理清思路,并且也对读者非常友好,请小伙伴们多多参

TaichiCourse 30 Nov 19, 2022
A Loss Function for Generative Neural Networks Based on Watson’s Perceptual Model

This repository contains the similarity metrics designed and evaluated in the paper, and instructions and code to re-run the experiments. Implementation in the deep-learning framework PyTorch

Steffen 86 Dec 27, 2022
The PyTorch implementation of DiscoBox: Weakly Supervised Instance Segmentation and Semantic Correspondence from Box Supervision.

DiscoBox: Weakly Supervised Instance Segmentation and Semantic Correspondence from Box Supervision The PyTorch implementation of DiscoBox: Weakly Supe

Shiyi Lan 1 Oct 23, 2021
PyTorch implementations of Top-N recommendation, collaborative filtering recommenders.

PyTorch implementations of Top-N recommendation, collaborative filtering recommenders.

Yoonki Jeong 129 Dec 22, 2022
Camera calibration & 3D pose estimation tools for AcinoSet

AcinoSet: A 3D Pose Estimation Dataset and Baseline Models for Cheetahs in the Wild Daniel Joska, Liam Clark, Naoya Muramatsu, Ricardo Jericevich, Fre

African Robotics Unit 42 Nov 16, 2022
The implementation for "Comprehensive Knowledge Distillation with Causal Intervention".

Comprehensive Knowledge Distillation with Causal Intervention This repository is a PyTorch implementation of "Comprehensive Knowledge Distillation wit

Xiang Deng 10 Nov 03, 2022
The official implementation of Autoregressive Image Generation using Residual Quantization (CVPR '22)

Autoregressive Image Generation using Residual Quantization (CVPR 2022) The official implementation of "Autoregressive Image Generation using Residual

Kakao Brain 529 Dec 30, 2022
Hierarchical Memory Matching Network for Video Object Segmentation (ICCV 2021)

Hierarchical Memory Matching Network for Video Object Segmentation Hongje Seong, Seoung Wug Oh, Joon-Young Lee, Seongwon Lee, Suhyeon Lee, Euntai Kim

Hongje Seong 72 Dec 14, 2022
Source code for paper "Deep Superpixel-based Network for Blind Image Quality Assessment"

DSN-IQA Source code for paper "Deep Superpixel-based Network for Blind Image Quality Assessment" Requirements Python =3.8.0 Pytorch =1.7.1 Usage wit

7 Oct 13, 2022
ruptures: change point detection in Python

Welcome to ruptures ruptures is a Python library for off-line change point detection. This package provides methods for the analysis and segmentation

Charles T. 1.1k Jan 03, 2023
A method that utilized Generative Adversarial Network (GAN) to interpret the black-box deep image classifier models by PyTorch.

A method that utilized Generative Adversarial Network (GAN) to interpret the black-box deep image classifier models by PyTorch.

Yunxia Zhao 3 Dec 29, 2022
Weighted K Nearest Neighbors (kNN) algorithm implemented on python from scratch.

kNN_From_Scratch I implemented the k nearest neighbors (kNN) classification algorithm on python. This algorithm is used to predict the classes of new

1 Dec 14, 2021
Code for models used in Bashiri et al., "A Flow-based latent state generative model of neural population responses to natural images".

A Flow-based latent state generative model of neural population responses to natural images Code for "A Flow-based latent state generative model of ne

Sinz Lab 5 Aug 26, 2022
Mixed Transformer UNet for Medical Image Segmentation

MT-UNet Update 2022/01/05 By another round of training based on previous weights, our model also achieved a better performance on ACDC (91.61% DSC). W

dotman 92 Dec 25, 2022
Anti-Adversarially Manipulated Attributions for Weakly and Semi-Supervised Semantic Segmentation (CVPR 2021)

Anti-Adversarially Manipulated Attributions for Weakly and Semi-Supervised Semantic Segmentation Input Image Initial CAM Successive Maps with adversar

Jungbeom Lee 110 Dec 07, 2022
Code examples and benchmarks from the paper "Understanding Entropy Coding With Asymmetric Numeral Systems (ANS): a Statistician's Perspective"

Code For the Paper "Understanding Entropy Coding With Asymmetric Numeral Systems (ANS): a Statistician's Perspective" Author: Robert Bamler Date: 22 D

4 Nov 02, 2022