https://github.com/anas436/patient-class-and-drug-prediction-using-decision-tree-with-python

https://github.com/anas436/patient-class-and-drug-prediction-using-decision-tree-with-python

decision-tree-classifier matplotlib numpy pandas python3 scikit-learn sklearn sklearn-metrics train-test-split

Last synced: 2 months ago
JSON representation

• Host: GitHub
• URL: https://github.com/anas436/patient-class-and-drug-prediction-using-decision-tree-with-python
• Owner: Anas436
• Created: 2022-09-03T17:06:46.000Z (almost 4 years ago)
• Default Branch: main
• Last Pushed: 2022-09-03T17:08:50.000Z (almost 4 years ago)
• Last Synced: 2025-02-01T15:30:34.471Z (over 1 year ago)
• Topics: decision-tree-classifier, matplotlib, numpy, pandas, python3, scikit-learn, sklearn, sklearn-metrics, train-test-split
• Language: Jupyter Notebook
• Homepage:
• Size: 25.4 KB
• Stars: 0
• Watchers: 2
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

          
# Patient-Class-and-Drug-Prediction-Using-Decision-Tree-with-Python

## Objectives

After completing this lab you will be able to:

*   Develop a classification model using Decision Tree Algorithm

In this lab exercise, you will learn a popular machine learning algorithm, Decision Trees. You will use this classification algorithm to build a model from the historical data of patients, and their response to different medications. Then you will use the trained decision tree to predict the class of an unknown patient, or to find a proper drug for a new patient.

Table of contents




    


        
About the dataset

        
Downloading the Data

        
Pre-processing

        
Setting up the Decision Tree

        
Modeling

        
Prediction

        
Evaluation

        
Visualization

    










    
About the dataset

    Imagine that you are a medical researcher compiling data for a study. You have collected data about a set of patients, all of whom suffered from the same illness. During their course of treatment, each patient responded to one of 5 medications, Drug A, Drug B, Drug c, Drug x and y. 

    


    


    Part of your job is to build a model to find out which drug might be appropriate for a future patient with the same illness. The features of this dataset are Age, Sex, Blood Pressure, and the Cholesterol of the patients, and the target is the drug that each patient responded to.

    


    


    It is a sample of multiclass classifier, and you can use the training part of the dataset 

    to build a decision tree, and then use it to predict the class of an unknown patient, or to prescribe a drug to a new patient.