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Drug Classification Using Machine Learning

This project uses machine learning techniques to classify drugs based on patient features like Age, Sex, Blood Pressure (BP), Cholesterol levels, and Na-to-K ratio. The objective is to predict the appropriate drug class for each patient.

Table of Contents

• Project Overview
• Data
• Preprocessing
• Models Used
• Results
• Dependencies
• Usage
• Future Improvements

Project Overview

This project implements various machine learning models, including:

• K-Nearest Neighbors (KNN)
• Decision Tree Classifier
• Random Forest Classifier

The dataset contains patient information such as age, sex, blood pressure levels, cholesterol levels, and the Na-to-K ratio. Based on these features, the goal is to predict the appropriate drug from a set of possible drug classes.

Data

The dataset contains 200 records, each representing a patient. The features include:

• Age: Integer value representing the age of the patient.
• Sex: Categorical value (Male or Female).
• Blood Pressure: Categorical value (High, Normal, Low).
• Cholesterol: Categorical value (High, Normal).
• Na-to-K Ratio: A floating-point number representing the ratio of sodium to potassium in the blood.
• Drug: The target variable indicating the prescribed drug.

Preprocessing

• Encoding: Categorical features (Sex, BP, Cholesterol) are encoded into numerical form.
• Train-Test Split: The dataset is split into training and testing sets to validate model performance.
• Data Visualization: Histograms and pie charts are used to explore feature distributions.

Models Used

The following models are implemented:

1. K-Nearest Neighbors (KNN)

   • Accuracy: 73.3%
   • Distance-based classification, sensitive to the choice of k.

2. Decision Tree Classifier

   • Accuracy comparable to KNN.
   • Works by recursively splitting the data based on feature thresholds.

3. Random Forest Classifier

   • Ensemble learning technique combining multiple decision trees.
   • Improved accuracy and generalization compared to individual models.

Results

• Random Forest Classifier provided the best overall accuracy and stability across the test dataset.
• The model successfully classified drugs with higher accuracy and reduced overfitting compared to KNN and Decision Trees.

Dependencies

To run the project, the following Python libraries are required:

• pandas
• numpy
• matplotlib
• scikit-learn

Install dependencies via pip:

pip install -r requirements.txt

Usage

1. Clone the repository:

   git clone https://github.com/your-username/drug-classification.git
   cd drug-classification

2. Install the required libraries:

   pip install -r requirements.txt

3. Run the drug_classification.py script:

   python drug_classification.py

4. View the results and accuracy metrics for each model.

Future Improvements

• Hyperparameter Tuning: Adjusting parameters of Random Forest and KNN to further improve model accuracy.
• Feature Engineering: Creating additional features or transforming existing ones to enhance model performance.
• Cross-Validation: Applying cross-validation to better evaluate model performance on unseen data.
• Deep Learning Models: Exploring the potential of neural networks for drug classification tasks.

License

This project is licensed under the MIT License. See the LICENSE file for details.