ZakyF/Global-Digital-Wellnes

Global Digital Wellness & Subscription Fatigue Dataset

🌍 Overview

This is a synthetic but realistic dataset designed to explore the intersection of digital technology adoption, screen time addiction, subscription fatigue, and personal wellness globally. It features 10,000 users across 6 geographic regions with comprehensive behavioral and psychological metrics.

Perfect for: Machine Learning competitions, academic research, NLP projects, data visualization, and portfolio building.

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📊 Dataset Highlights

Metric	Value
Total Rows	10,000
Total Columns	11
File Size	1.43 MB
Missing Values	0% (cleaned)
Regions	6 (North America, Europe, Asia-Pacific, LATAM, Africa, Middle East)
Age Groups	4 (Gen Z, Millennial, Gen X, Boomer)
Target Classes	3 (Digital Addict, Balanced, Minimalist)

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📋 Columns (11 total)

1. User_ID — Unique identifier (1 to 10,000)
2. Region — Geographic location (6 regions)
3. Age_Group — Generational bracket (ordinal, 4 categories)
4. Daily_Screen_Time — Hours per day (continuous, 0.5-16 range)
5. Subscription_Count — Number of paid services (discrete, 0-15)
6. Digital_Fatigue_Score — Self-reported fatigue level (ordinal, 1-10 scale)
7. Sleep_Quality — Sleep quality category (ordinal, 4 categories)
8. Monthly_Digital_Spend — USD spending on digital services (continuous)
9. User_Review_Note — Text review about digital wellness (text/NLP)
10. Sentiment_Index — Computed sentiment score (continuous, -1 to 1)
11. Lifestyle_Class — Target: Digital Addict / Balanced / Minimalist (3-class classification)

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🎯 Use Cases

1. Classification Tasks

• Predict Lifestyle_Class from behavioral features
• Multi-class classification (3 balanced classes)
• Baseline accuracy benchmarks

2. Regression Tasks

• Predict Digital_Fatigue_Score (1-10 ordinal scale)
• Predict Monthly_Digital_Spend (USD amounts)
• Predict Sentiment_Index (-1 to 1 range)

3. NLP & Text Analysis

• Sentiment analysis on User_Review_Note
• Text classification by sentiment and lifestyle
• Opinion mining in wellness domain

4. Exploratory Data Analysis (EDA)

• Regional patterns in digital behavior
• Generational differences (Gen Z vs Boomers)
• Sleep vs. screen time correlations
• Age-based purchasing patterns

5. Clustering & Segmentation

• Identify user behavioral clusters
• Regional behavioral profiles
• Hidden patterns in digital wellness

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🚀 Quick Start

Load the Dataset

import pandas as pd

df = pd.read_csv('global_digital_wellness_dataset.csv')
print(df.head())
print(df.info())
print(df.describe())

Basic Exploratory Analysis

# Class distribution
print(df['Lifestyle_Class'].value_counts())

# Regional differences
print(df.groupby('Region')['Digital_Fatigue_Score'].mean())

# Age group analysis
print(df.groupby('Age_Group')['Daily_Screen_Time'].mean())

# Correlation analysis
corr = df[['Daily_Screen_Time', 'Subscription_Count', 'Digital_Fatigue_Score', 'Monthly_Digital_Spend']].corr()
print(corr)

Train a Classification Model

from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report, confusion_matrix

# Prepare features and target
X = df[['Daily_Screen_Time', 'Subscription_Count', 'Monthly_Digital_Spend', 'Age_Group']]
y = df['Lifestyle_Class']

# Encode categorical features
X = pd.get_dummies(X, columns=['Age_Group'])

# Split data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Train model
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)

# Evaluate
y_pred = model.predict(X_test)
print(classification_report(y_test, y_pred))
print(confusion_matrix(y_test, y_pred))

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