Obesity Risk Classification Supervised ML Project

Overview

This repository hosts a supervised machine learning project focused on classifying the risk of obesity based on various factors. Leveraging datasets containing features such as diet, physical activity, demographics, and medical history, the project aims to develop predictive models that can accurately categorize individuals into different obesity risk classes.

Goals

• Obesity Risk Assessment: Develop models to accurately predict obesity risk levels based on input features.
• Feature Analysis: Investigate the importance of different features in determining obesity risk.
• Model Evaluation: Compare the performance of different machine learning algorithms in classifying obesity risk.

Dataset

Machine Learning Techniques

The project employs various supervised learning techniques, including but not limited to:

• Logistic Regression
• Decision Trees
• Random Forest
• Support Vector Machines (SVM)
• XGBoost
• Gradient Boosting
• LightGBM
• CatBoost

Usage

• Exploratory Data Analysis (EDA): Explore the dataset to understand the distribution of features and relationships between variables.
• Data Preprocessing: Cleanse and preprocess the data to prepare it for model training.
• Model Training: Train machine learning models using different algorithms and evaluate their performance.
• Model Evaluation: Assess the performance of trained models using appropriate evaluation metrics.
• Deployment: You can visit [] to try it.