Mental Health & Technology Usage Analysis 🧠💻

Project Overview

This project analyzes the relationship between technology usage patterns and mental health/sleep quality using machine learning techniques. The analysis employs multiple classification and regression models to predict sleep quality and hours respectively based on various technology usage and mental health indicators.

Key Features

• Multiple machine learning models implementation
• Ensemble learning approach
• Feature importance analysis
• Comprehensive model evaluation
• Data preprocessing and feature engineering

Dataset

The project uses two main datasets:

• mental_health_with_sleep_quality.csv (Feature Engg.)

• mental_health_and_technology_usage_2024.csv

Requirements

R (>= 4.0.0)

Required R packages:

• tidyverse
• caret
• randomForest
• xgboost
• nnet
• e1071
• ROSE
• rpart
• gbm
• MLmetrics
• kernlab

Usage Guide

For R Analysis

1. Install the required R packages:

   install.packages(c("tidyverse", "caret", "randomForest", "xgboost", "nnet", "e1071", "ROSE", "rpart", "gbm", "MLmetrics", "kernlab"))

2. Run ✏️ FinalClass.Rmd for Classification.
3. Run ✏️ regression-analysis.ipynb for regression.

Model Performance Summary

Classification Models

Model	Accuracy
Multinomial Regression	52.58%
Decision Tree	52.55%
GBM	52.45%
XGBoost	52.25%
Random Forest	51.95%
SVM	49.25%

Regression Models

Model	RMSE
Linear Regression	0.45
Decision Tree	0.48
Random Forest	0.42
XGBoost	0.41
GBM	0.43
SVM	0.47