💊 Medicine & Health Classification Using Machine Learning

A machine learning classification project for categorizing pharmaceutical drug types and health indicator data using Decision Trees and Random Forest classifiers.

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📌 Project Overview

This repository contains data analysis workflows, feature engineering, and Decision Tree classification algorithms for pharmaceutical and health metrics modeling (Decision_Tree.ipynb & bmi.csv).

Key objectives include:

• Data Preprocessing & Encoding: Handling categorical features, missing values, and numerical normalization.
• Model Training: Implementation of Decision Tree classifiers (DecisionTreeClassifier), Gini impurity/Entropy splitting, and hyperparameter tuning (max_depth, min_samples_split).
• Model Evaluation: Precision, Recall, F1-Score, Confusion Matrix visualization, and Decision Tree graph rendering.

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📁 Repository Structure

medicine-type-classification/
├── Decision_Tree.ipynb  # Primary Jupyter notebook for ML classification modeling
├── bmi.csv              # Health metrics and demographic dataset
└── README.md            # Repository documentation

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🛠 Prerequisites & Installation

git clone https://github.com/amar-codes02/medicine-type-classification.git
cd medicine-type-classification

pip install numpy pandas matplotlib seaborn scikit-learn jupyter

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🚀 Usage

Open the Jupyter notebook environment:

jupyter notebook Decision_Tree.ipynb

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📄 License

Licensed under the MIT License.