🩺 Sleep Disorder Classification Project

This project analyzes a mini-database of clinical and professional data from patients to categorize their sleep disorders. The workflow includes:

1. Feature Engineering: Transforming raw data into a format consumable by machine learning models.
2. Model Training and Testing: Implementing and comparing the performance of four classification algorithms:
   • K-Nearest Neighbors (KNN)
   • Gaussian Naive Bayes
   • Random Forest
   • Decision Tree

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🚀 Getting Started

Prerequisites

Ensure you have Python 3.8+ installed on your system.

Installation

1. Clone the Repository

   git clone https://github.com/yourusername/sleep-disorder-classification.git
   cd sleep-disorder-classification

2. Create a Virtual Environment (Optional but Recommended)

   python -m venv env
   source env/bin/activate  # Linux/Mac
   env\Scripts\activate     # Windows

3. Install Dependencies
   Install the necessary Python libraries from the requirements.txt file:

   pip install -r requirements.txt

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🧠 Workflow

1. Feature Engineering

   • Transform clinical data into features suitable for machine learning models.
   • Includes handling missing values, encoding categorical data, and scaling numerical data.

2. Model Implementation

   • Train and test four classifiers:
     • K-Nearest Neighbors (KNN)
     • Gaussian Naive Bayes
     • Random Forest
     • Decision Tree
   • Compare performance metrics (accuracy, precision, recall, F1-score).

3. Evaluation

   • Analyze model predictions to identify the best-performing algorithm.

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