MachineLearning_With_Python

PIMA Indians Diabetes

Background

Diabetes, is a group of metabolic disorders in which there are high blood sugar levels over a prolonged period. Symptoms of high blood sugar include frequent urination, increased thirst, and increased hunger. If left untreated, diabetes can cause many complications. Acute complications can include diabetic ketoacidosis, hyperosmolar hyperglycemic state, or death. Serious long-term complications include cardiovascular disease, stroke, chronic kidney disease, foot ulcers, and damage to the eyes.

This dataset is originally from the National Institute of Diabetes and Digestive and Kidney Diseases. The objective of the dataset is to diagnostically predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the dataset. Several constraints were placed on the selection of these instances from a larger database.

PIMA Dataset

The PIMA dataset is a widely used dataset for Machine Learning tasks, specifically for predicting diabetes. It contains various features such as glucose levels, blood pressure, body mass index (BMI), and age of individuals, along with a binary target variable indicating the presence or absence of diabetes.

Objective :

We will try to build a machine learning model to accurately predict whether or not the patients in the dataset have diabetes or not?

Data :

The datasets consists of several medical predictor variables and one target variable, Outcome. Predictor variables includes the number of pregnancies the patient has had, their BMI, insulin level, age, and so on.

Pregnancies: Number of times pregnant Glucose: Plasma glucose concentration a 2 hours in an oral glucose tolerance test BloodPressure: Diastolic blood pressure (mm Hg) SkinThickness: Triceps skin fold thickness (mm) Insulin: 2-Hour serum insulin (mu U/ml) BMI: Body mass index (weight in kg/(height in m)^2) DiabetesPedigreeFunction: Diabetes pedigree function Age: Age (years) Outcome: Class variable (0 or 1)

Note - You can use your data to predict future events with the help of machine learning.

This project involves creating a machine learning prediction solution with Python, the scikit-learn library, Pandas, NumPy and the Jupyter Notebook environment.