Predicting diabetes over a five year period using logistic regression and the Pima First-Nation dataset

Last update: Mar 28, 2022

Related tags

Overview

Diabetes

This script uses the Pima First Nations dataset to create a model to predict whether or not an individual will develop Diabetes Mellitus Type 2 within a five year time span

This is a quick little project involving regression analysis and diabetes. I have created this project to better my understanding of not only the content currently being covered in my anatomy and physiology course, but also to practice working with simple regression models and common libraries.

So far, this model is able to predict values with a ~75% accuracy (not bad given the lack of data and size of the model, but not great). There are several ways to optimize this model. A few I can think of off the top of my head would be gathering more data to train it on, and cleaning the data in a different way (ie... not replacing 0 values with the mean value of that column).

Dataset found on kaggle: https://www.kaggle.com/kumargh/pimaindiansdiabetescsv

Owner

GitHub Repository

Climin is a Python package for optimization, heavily biased to machine learning scenarios

climin climin is a Python package for optimization, heavily biased to machine learning scenarios distributed under the BSD 3-clause license. It works

177 Sep 02, 2022

Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.

Prophet: Automatic Forecasting Procedure Prophet is a procedure for forecasting time series data based on an additive model where non-linear trends ar

15.4k Jan 07, 2023

Skforecast is a python library that eases using scikit-learn regressors as multi-step forecasters

Skforecast is a python library that eases using scikit-learn regressors as multi-step forecasters. It also works with any regressor compatible with the scikit-learn API (pipelines, CatBoost, LightGBM

297 Jan 09, 2023

The easy way to combine mlflow, hydra and optuna into one machine learning pipeline.

mlflow_hydra_optuna_the_easy_way The easy way to combine mlflow, hydra and optuna into one machine learning pipeline. Objective TODO Usage 1. build do

9 Sep 09, 2022

A high-performance topological machine learning toolbox in Python

giotto-tda is a high-performance topological machine learning toolbox in Python built on top of scikit-learn and is distributed under the G

632 Dec 29, 2022

A Python package to preprocess time series

Disclaimer: This package is WIP. Do not take any APIs for granted. tspreprocess Time series can contain noise, may be sampled under a non fitting rate

57 Dec 17, 2022

DirectML is a high-performance, hardware-accelerated DirectX 12 library for machine learning.

DirectML is a high-performance, hardware-accelerated DirectX 12 library for machine learning. DirectML provides GPU acceleration for common machine learning tasks across a broad range of supported ha

1.1k Jan 04, 2023

Winning solution for the Galaxy Challenge on Kaggle

483 Jan 02, 2023

mlpack: a scalable C++ machine learning library --

4.2k Jan 01, 2023

Python module for machine learning time series:

seglearn Seglearn is a python package for machine learning time series or sequences. It provides an integrated pipeline for segmentation, feature extr

536 Dec 29, 2022

Responsible Machine Learning with Python

Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.

624 Jan 06, 2023

Neighbourhood Retrieval (Nearest Neighbours) with Distance Correlation.

Neighbourhood Retrieval with Distance Correlation Assign Pseudo class labels to datapoints in the latent space. NNDC is a slim wrapper around FAISS. N

1 Jan 16, 2022

Simple structured learning framework for python

PyStruct PyStruct aims at being an easy-to-use structured learning and prediction library. Currently it implements only max-margin methods and a perce

666 Jan 03, 2023

Kats is a toolkit to analyze time series data, a lightweight, easy-to-use, and generalizable framework to perform time series analysis.

Kats, a kit to analyze time series data, a lightweight, easy-to-use, generalizable, and extendable framework to perform time series analysis, from understanding the key statistics and characteristics

4.1k Dec 29, 2022

Predicting diabetes over a five year period using logistic regression and the Pima First-Nation dataset

Related tags

Overview

Diabetes

This script uses the Pima First Nations dataset to create a model to predict whether or not an individual will develop Diabetes Mellitus Type 2 within a five year time span

Owner

Climin is a Python package for optimization, heavily biased to machine learning scenarios

Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.

Skforecast is a python library that eases using scikit-learn regressors as multi-step forecasters

The easy way to combine mlflow, hydra and optuna into one machine learning pipeline.

A high-performance topological machine learning toolbox in Python

A Python package to preprocess time series

DirectML is a high-performance, hardware-accelerated DirectX 12 library for machine learning.

Winning solution for the Galaxy Challenge on Kaggle

mlpack: a scalable C++ machine learning library --

Python module for machine learning time series:

Responsible Machine Learning with Python

Neighbourhood Retrieval (Nearest Neighbours) with Distance Correlation.

Simple structured learning framework for python

Kats is a toolkit to analyze time series data, a lightweight, easy-to-use, and generalizable framework to perform time series analysis.

Python package for concise, transparent, and accurate predictive modeling

scikit-learn models hyperparameters tuning and feature selection, using evolutionary algorithms.

Azure Cloud Advocates at Microsoft are pleased to offer a 12-week, 24-lesson curriculum all about Machine Learning

To-Be is a machine learning challenge on CodaLab Platform about Mortality Prediction

AutoX是一个高效的自动化机器学习工具，它主要针对于表格类型的数据挖掘竞赛。它的特点包括: 效果出色、简单易用、通用、自动化、灵活。

High performance implementation of Extreme Learning Machines (fast randomized neural networks).

Predicting diabetes over a five year period using logistic regression and the Pima First-Nation dataset

Related tags

Overview

Diabetes

This script uses the Pima First Nations dataset to create a model to predict whether or not an individual will develop Diabetes Mellitus Type 2 within a five year time span

Owner

Climin is a Python package for optimization, heavily biased to machine learning scenarios

Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.

Skforecast is a python library that eases using scikit-learn regressors as multi-step forecasters

The easy way to combine mlflow, hydra and optuna into one machine learning pipeline.

A high-performance topological machine learning toolbox in Python

A Python package to preprocess time series

DirectML is a high-performance, hardware-accelerated DirectX 12 library for machine learning.

Winning solution for the Galaxy Challenge on Kaggle

mlpack: a scalable C++ machine learning library --

Python module for machine learning time series:

Responsible Machine Learning with Python

Neighbourhood Retrieval (Nearest Neighbours) with Distance Correlation.

Simple structured learning framework for python

Kats is a toolkit to analyze time series data, a lightweight, easy-to-use, and generalizable framework to perform time series analysis.

Python package for concise, transparent, and accurate predictive modeling

scikit-learn models hyperparameters tuning and feature selection, using evolutionary algorithms.

Azure Cloud Advocates at Microsoft are pleased to offer a 12-week, 24-lesson curriculum all about Machine Learning

To-Be is a machine learning challenge on CodaLab Platform about Mortality Prediction

AutoX是一个高效的自动化机器学习工具，它主要针对于表格类型的数据挖掘竞赛。 它的特点包括: 效果出色、简单易用、通用、自动化、灵活。

High performance implementation of Extreme Learning Machines (fast randomized neural networks).

AutoX是一个高效的自动化机器学习工具，它主要针对于表格类型的数据挖掘竞赛。它的特点包括: 效果出色、简单易用、通用、自动化、灵活。