Skip to content

Latest commit

 

History

History
17 lines (13 loc) · 879 Bytes

README.md

File metadata and controls

17 lines (13 loc) · 879 Bytes

Obesity Prediction Using Random Forest

Overview

This project utilizes the Random Forest algorithm implemented in MATLAB to predict obesity based on various features collected from a dataset. The model is trained using a set of training data and then tested on a separate dataset to evaluate its performance. The results are formatted for submission to Kaggle.

Project Structure

  • Main File:

    • proj2_og.m: The main script that performs data loading, processing, model training, and prediction.

  • Variations and Experiments: Additional scripts that explore different modeling techniques and configurations.

  • Data Files: The project uses two CSV files:

    • train.csv: Contains the training data.
    • test.csv: Contains the testing data.

Prerequisites

  • MATLAB installed on your machine.
  • Required toolboxes for statistics and machine learning.