Diabetes Prediction with Logistic Regression

Overview

Diabetes Prediction with Logistic Regression

  1. Exploratory Data Analysis
  2. Data Preprocessing
  3. Model & Prediction
  4. Model Evaluation
  5. Model Validation: Holdout
  6. Model Validation: 10-Fold Cross Validation
  7. Prediction for A New Observation

Business Problem

Characteristics of people with diabetes will be able to predict whether they have a patient or not it is desirable to develop a machine learning model.

Dataset Story

The data set is part of a large data set maintained at the National Institutes of Diabetes-dIgestive-Kidney Diseases in the United States. this data used for a diabetes study conducted on Pima Indian women aged 21 years and older living in the city of Phoenix, which is their city. The data consists of 768 observations and 8 numerical independent variables. The target variable is specified as "output";

1 diabetes test result is positive, 0 indicates that it is negative.

Variables

  • Pregnancies: Number of pregnancies
  • Glucose: 2 Hours plasma glucose concentration in the oral glucose tolerance test
  • Blood Pressure: mm Hg
  • SkinThickness:
  • Insulin: 2 Hours serum insulin (mu U/ml)
  • DiabetesPedigreeFunction
  • Age: years
  • Outcome: Having diabete (1) or not (0)

In this study, the diabetes data set was reviewed and it was tried to predict whether a person has diabetes with a Logistic Regression model. Firstly, the dependent variable "outcome" was reviewed in the study. In the last step, new variables were produced and the success of the model was tried to be increased. The accuracy rate and F1 score of the established model were determined as 0.63 and the AUC value was determined as 0.84. Finally, it was estimated by the established model whether a randomly selected person has diabetes or not.

Owner
AZİZE SULTAN PALALI
Doping Hafıza | Data Analyst | Data Science and Machine Learning Bootcamp Participant at Veri Bilimi Okulu
AZİZE SULTAN PALALI
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

Microsoft 1.1k Jan 04, 2023
Lightning ⚡️ fast forecasting with statistical and econometric models.

Nixtla Statistical ⚡️ Forecast Lightning fast forecasting with statistical and econometric models StatsForecast offers a collection of widely used uni

Nixtla 2.1k Dec 29, 2022
Apache (Py)Spark type annotations (stub files).

PySpark Stubs A collection of the Apache Spark stub files. These files were generated by stubgen and manually edited to include accurate type hints. T

Maciej 114 Nov 22, 2022
A statistical library designed to fill the void in Python's time series analysis capabilities, including the equivalent of R's auto.arima function.

pmdarima Pmdarima (originally pyramid-arima, for the anagram of 'py' + 'arima') is a statistical library designed to fill the void in Python's time se

alkaline-ml 1.3k Jan 06, 2023
Repository for DCA0305, an undergraduate course about Machine Learning Workflows and Pipelines

Federal University of Rio Grande do Norte Technology Center Department of Computer Engineering and Automation Machine Learning Based Systems Design Re

Ivanovitch Silva 81 Oct 18, 2022
Penguins species predictor app is used to classify penguins species created using python's scikit-learn, fastapi, numpy and joblib packages.

Penguins Classification App Penguins species predictor app is used to classify penguins species using their island, sex, bill length (mm), bill depth

Siva Prakash 3 Apr 05, 2022
LightGBM + Optuna: no brainer

AutoLGBM LightGBM + Optuna: no brainer auto train lightgbm directly from CSV files auto tune lightgbm using optuna auto serve best lightgbm model usin

Rishiraj Acharya 22 Dec 15, 2022
A modular active learning framework for Python

Modular Active Learning framework for Python3 Page contents Introduction Active learning from bird's-eye view modAL in action From zero to one in a fe

modAL 1.9k Dec 31, 2022
WAGMA-SGD is a decentralized asynchronous SGD for distributed deep learning training based on model averaging.

WAGMA-SGD is a decentralized asynchronous SGD based on wait-avoiding group model averaging. The synchronization is relaxed by making the collectives externally-triggerable, namely, a collective can b

Shigang Li 6 Jun 18, 2022
Python-based implementations of algorithms for learning on imbalanced data.

ND DIAL: Imbalanced Algorithms Minimalist Python-based implementations of algorithms for imbalanced learning. Includes deep and representational learn

DIAL | Notre Dame 220 Dec 13, 2022
Fundamentals of Machine Learning

Fundamentals-of-Machine-Learning This repository introduces the basics of machine learning algorithms for preprocessing, regression and classification

Happy N. Monday 3 Feb 15, 2022
database for artificial intelligence/machine learning data

AIDB v0.0.1 database for artificial intelligence/machine learning data Overview aidb is a database designed for large dataset for machine learning pro

Aarush Gupta 1 Oct 24, 2021
A machine learning web application for binary classification using streamlit

Machine Learning web App This is a machine learning web application for binary classification using streamlit options this application contains 3 clas

abdelhak mokri 1 Dec 20, 2021
Kaggle Competition using 15 numerical predictors to predict a continuous outcome.

Kaggle-Comp.-Data-Mining Kaggle Competition using 15 numerical predictors to predict a continuous outcome as part of a final project for a stats data

moisey alaev 1 Dec 28, 2021
Mortality risk prediction for COVID-19 patients using XGBoost models

Mortality risk prediction for COVID-19 patients using XGBoost models Using demographic and lab test data received from the HM Hospitales in Spain, I b

1 Jan 19, 2022
Price Prediction model is used to develop an LSTM model to predict the future market price of Bitcoin and Ethereum.

Price Prediction model is used to develop an LSTM model to predict the future market price of Bitcoin and Ethereum.

2 Jun 14, 2022
scikit-learn: machine learning in Python

scikit-learn is a Python module for machine learning built on top of SciPy and is distributed under the 3-Clause BSD license. The project was started

neurodata 3 Dec 16, 2022
This is the code repository for Interpretable Machine Learning with Python, published by Packt.

Interpretable Machine Learning with Python, published by Packt

Packt 299 Jan 02, 2023
Summer: compartmental disease modelling in Python

Summer: compartmental disease modelling in Python Summer is a Python-based framework for the creation and execution of compartmental (or "state-based"

6 May 13, 2022
Scikit-Garden or skgarden is a garden for Scikit-Learn compatible decision trees and forests.

Scikit-Garden or skgarden (pronounced as skarden) is a garden for Scikit-Learn compatible decision trees and forests.

260 Dec 21, 2022