/Obesity-Prediction-Models

Decision Tree and KNN models were used to estimate obesity levels using the ‘Estimation of Obesity Levels Based On Eating Habits and Physical Condition’ dataset.

Primary LanguageJupyter NotebookGNU General Public License v3.0GPL-3.0

Obesity Prediction Models

This repository contains implementations of Decision Tree and K-Nearest Neighbors (KNN) models to estimate obesity levels based on eating habits and physical condition. The models were developed and evaluated using the ‘Estimation of Obesity Levels Based On Eating Habits and Physical Condition’ dataset.

Project Overview

The objective of this project is to:

  • Analyze eating habits and physical activity data.
  • Predict obesity levels using machine learning models.
  • Compare the performance of Decision Tree and KNN models.

Each model is provided as a Jupyter Notebook, showcasing the workflow from data preprocessing to evaluation.

Models Implemented

1. Decision Tree

  • Description: A classification model that uses a tree-like structure to make decisions based on feature values.
  • Key Features:
    • Handles categorical and numerical features effectively.
    • Provides interpretable results for decision-making.
  • View Notebook

2. K-Nearest Neighbors (KNN)

  • Description: A non-parametric algorithm that predicts the class of a sample based on its closest neighbors in the feature space.
  • Key Features:
    • Simple yet effective for classification problems.
    • Performance heavily depends on the choice of k and distance metric.
  • View Notebook

How to Use

  1. Clone the repository: 
    git clone https://github.com/npradaschnor/Obesity-Prediction-Models.git