Which algorithms are used in this obesity classification project?

The system implements and compares two classification algorithms: Random Forest and Logistic Regression. Random Forest achieved the best performance with 94.2% accuracy and a weighted F1-score of 94.1%.

What features are used for obesity level classification?

The system uses 16 features including demographic information (age, gender), dietary habits (vegetable consumption, meal frequency), physical activity levels, family history of obesity, smoking status, alcohol consumption, and transportation methods.

What is the accuracy of this obesity classification model?

The Random Forest model achieves 94.2% test accuracy with precision of 94.3%, recall of 94.2%, F1-score of 94.1%, and ROC-AUC of 0.991, making it highly reliable for clinical decision support.

Who can benefit from this obesity classification system?

Healthcare professionals can use it for early obesity screening, primary care physicians for patient risk assessment, nutritionists for personalized dietary planning, and public health officials for population-level health monitoring.

What technologies are used to build this project?

The project is built using Python with Pandas, NumPy, Scikit-learn, imbalanced-learn (SMOTE), Matplotlib, and Seaborn for machine learning. The web interface is developed using Flask with HTML, CSS, and JavaScript.

Watch Demo Video

Machine Learning

Lifestyle-Based Obesity Level Classification Using Machine Learning

The Lifestyle-Based Obesity Level Classification Using Machine Learning system is a comprehensive machine learning solution that classifies individuals into seven obesity categories using lifestyle and health-related factors. This project is ideal for B.Tech, MCA, CSE, and engineering students as their final year project. It uses advanced algorithms like Random Forest and Logistic Regression with SMOTE to achieve 94.2% accuracy.

The system leverages 16 features including demographic information, dietary habits, physical activity levels, family history, and health history. It provides interactive visualizations, feature importance analysis, model comparison, and real-time obesity classification to help healthcare professionals make data-driven decisions for early intervention.

Python 3.8+ Machine Learning Random Forest Logistic Regression SMOTE Scikit-learn imbalanced-learn Pandas NumPy Matplotlib Seaborn Flask HTML/CSS/JS

Key Features:

Multi-Class Obesity Classification
Random Forest (94.2% Accuracy)
Logistic Regression (89.7% Accuracy)
SMOTE for Class Imbalance
Feature Importance Analysis
Real-time Obesity Prediction
Model Performance Comparison
Interactive Visualizations
5-Fold Cross-Validation
Flask Web Application

Algorithms Used

🌲 Random Forest Classifier

Ensemble learning with 300 trees, captures non-linear relationships effectively

🎯 Accuracy: 94.2%

📊 Logistic Regression

Multinomial regression with L2 regularization, interpretable coefficients

🎯 Accuracy: 89.7%

🔄 SMOTE

Synthetic Minority Over-sampling for handling class imbalance

📊 k-neighbors: 5

🔍 5-Fold CV

Cross-validation for model stability and generalization

📊 RF: 93.6% ± 1.3%

Methodology & Workflow

1 Data Loading & Inspection
Obesity dataset with 2,111 records and 16 features

2 Data Preprocessing
Cleaning, encoding, BMI calculation, feature scaling

3 Exploratory Data Analysis
Class distribution, correlation analysis, feature importance

4 Model Training
Random Forest & Logistic Regression with SMOTE pipelines

5 Model Evaluation
Accuracy, Precision, Recall, F1-Score, ROC-AUC, 5-fold CV

6 Web Deployment
Flask web app for real-time obesity classification

Model Performance Comparison

Metric	Random Forest	Logistic Regression	Best
Accuracy	0.942	0.897	Random Forest
Precision (Weighted)	0.943	0.891	Random Forest
Recall (Weighted)	0.942	0.897	Random Forest
F1-Score (Weighted)	0.941	0.889	Random Forest
ROC-AUC (Weighted)	0.991	0.973	Random Forest
CV Mean (5-Fold)	0.936	0.882	Random Forest

Project Package Includes:

Complete Source Code Documentation (50+ pages) Video Tutorial Dataset (2,111 records) Flask Web App Model Files (Pickle) Visualizations 24/7 Expert Support

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