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

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

Group Members

• @Arjun U
• @Sidharth S Kumar
• @Avadhuta Sai Varun
• @Karan-Rajesh-Nair

Reference

1. 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