Decision tree is the most powerful and popular tool for classification and prediction

Last update: Jan 23, 2022

Overview

Diabetes Prediction Using Decision Tree

Introduction

Decision tree is the most powerful and popular tool for classification and prediction. A Decision tree is a flowchart like tree structure, where each internal node denotes a test on an attribute, each branch represents an outcome of the test, and each leaf node (terminal node) holds a class label.

In this project we build a decsion tree to predict diabetes for Pima Indians dataset with variables such as age, blood, pressure etc

Major Steps

Load the required libraries
Load the data sets using Pandas
Divide the columns to two types of variables dependent and independent variables
Bulding Decision Tree using scikit-learn
Evaluvating the model or classifier
Creating a visual Decision Tree

Group Members

Reference

Decision Tree Classification on Diabetes-Dataset using Python : https://medium.com/@ananya_bt18/decision-tree-classification-on-diabetes-dataset-using-python-scikit-learn-package-f7be624c344e

Decision tree is the most powerful and popular tool for classification and prediction

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Overview

Diabetes Prediction Using Decision Tree

Introduction

Major Steps

Group Members

Reference

Owner

Arjun U

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