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Decision-Tree-for-Drug-Selection

Dataset Description :

This dataset contains information about a set of patients who suffered from the same illness and were treated with one of five medications: Drug A, Drug B, Drug C, Drug X, and Drug Y. The goal is to build a model to predict which drug might be appropriate for future patients based on their characteristics.

Features

Age: The age of the patient. Sex: The sex of the patient (Male or Female). Blood Pressure (BP): The blood pressure level of the patient, categorized as LOW, NORMAL, or HIGH. Cholesterol: The cholesterol level of the patient, categorized as NORMAL or HIGH. Na_to_K: Sodium to Potassium ratio in the patient's blood.

Target

The target variable is the drug that each patient responded to, represented as:

Drug A Drug B Drug C Drug X Drug Y

Model Application

This dataset is suitable for a multiclass classification task. One approach is to build a decision tree model using the provided features to predict the appropriate drug for a patient. The decision tree model can then be used to predict the class of an unknown patient or prescribe a drug to a new patient based on their characteristics.

Acknowledgements

This dataset is obtained from the IBM Developer Skills Network course "Machine Learning with Python" (Module 3) and is used for educational purposes.

Note: The code provided in this README is a part of the project and should be used in conjunction with the dataset and appropriate libraries.

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