Skip to content

This project based on focusing data analysis and feature engineering steps to develop a machine learning model that can predict whether people have diabetes when their characteristics are specified.

Notifications You must be signed in to change notification settings

cagladenizdoruk/Diabete-Feature-Engineering

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

4 Commits
 
 
 
 

Repository files navigation

Diabete Feature Engineering

DiabeteFeatureEngineering

Business Problem

It is desired to develop a machine learning model that can predict whether people have diabetes when their characteristics are specified. You are expected to perform the necessary data analysis and feature engineering steps before developing the model.

Dataset

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

9 Features | 768 Observations | 24 KB

Feature Definition
Pregnancies Number of pregnancies
Glucose 2-hour plasma glucose concentration in the oral glucose tolerance test
BloodPressure  Blood pressure (Diastolic(Small Blood Pressure))
SkinThickness Skin Thickness
Insulin Insulin
BMI Body Mass Index
DiabetesPedigreeFunction A function that calculates the probability of having diabetes based on people in the lineage.
Age Age (year)
Outcome Information whether the person has diabetes or not. Have the disease (1) or not (0)

Requirements

matplotlib==3.4.3
numpy==1.20.3
pandas==1.3.4
seaborn==0.11.2
session_info==1.0.0
sklearn==0.24.2

Author

Çağla Deniz Doruk

About

This project based on focusing data analysis and feature engineering steps to develop a machine learning model that can predict whether people have diabetes when their characteristics are specified.

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published