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📌 Project Overview

The Gaming Behavior & Mental Health Analytics Project is an end-to-end analytics solution designed to investigate the relationship between gaming behavior and mental health indicators.

The project analyzes player behavioral patterns, lifestyle indicators, and environmental factors to identify risk drivers associated with stress, addiction, aggression, and depression among gamers.

Using SQL data pipelines, feature engineering, and Power BI dashboards, the project transforms raw behavioral gaming data into actionable insights that help identify:

• Behavioral escalation patterns
• High-risk player segments
• Lifestyle factors influencing mental health
• Toxic gaming environment impacts
• Gaming intensity correlations with mental risk

The result is a two-layer interactive Power BI dashboard designed for both executive monitoring and deep behavioral analysis.

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🎯 Business Problem

The rapid expansion of online gaming has introduced behavioral challenges associated with excessive gaming engagement.

Gaming platforms and behavioral researchers need tools to answer questions such as:

• How does gaming intensity impact mental health risk?
• Are there early warning indicators of gaming addiction?
• How do lifestyle patterns like sleep influence stress levels?
• Does toxic gaming exposure increase aggression?
• Which player segments exhibit the highest behavioral risk?

However, raw behavioral data alone does not provide clear insights. A structured analytical framework is required to transform behavioral signals into meaningful risk indicators.

This project addresses that challenge by building a scalable behavioral analytics pipeline.

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🏗 Solution Architecture

The analytical framework follows a layered architecture commonly used in industry analytics solutions.

Raw Gaming Data
↓
SQL Data Cleaning & Feature Engineering
↓
Analytical Views (Risk Modeling & Segmentation)
↓
Power BI Semantic Model (Measures & KPIs)
↓
Interactive Executive Dashboard

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⚙️ Technology Stack

Layer	Technology
Data Processing	SQL
Data Modeling	Power BI Semantic Model
Data Visualization	Power BI
Analytical Metrics	DAX
Data Source	Behavioral Gaming Dataset

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📊 Dashboard Overview

The Power BI dashboard consists of two analytical layers designed for different decision-making needs.

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1️⃣ Executive Snapshot

This page provides a high-level overview of player behavior and risk distribution.

Key Insights

• Total player population analysis
• Risk segmentation across players
• Gaming intensity metrics
• Demographic distribution by age and gender
• Behavioral escalation patterns
• Gaming intensity vs mental risk correlation

This view is designed for executive stakeholders monitoring behavioral risk trends.

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2️⃣ Behavioral Risk Analysis

This page performs deeper behavioral investigation into drivers of mental health risk.

Analytical Insights

• Addiction level vs depression severity
• Sleep duration vs stress level patterns
• Toxic gaming exposure vs aggression
• Gaming intensity vs mental health risk

These analyses help identify behavioral drivers influencing psychological wellbeing among gamers.

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🧠 Key Business Insights

Gaming Intensity Drives Mental Risk

Players with higher daily gaming hours consistently demonstrate increased mental risk indicators and addiction levels.

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Addiction Correlates With Depression

Higher gaming addiction levels show strong correlation with increased depression scores, indicating behavioral escalation patterns.

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Sleep Patterns Influence Stress

Players with lower sleep duration exhibit higher stress levels, highlighting lifestyle factors impacting mental health.

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Toxic Gaming Environments Increase Aggression

Players exposed to toxic gaming interactions demonstrate higher aggression scores, suggesting environmental factors influence behavioral outcomes.

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🧩 Data Pipeline Design

The project implements a view-based SQL analytical pipeline to ensure modular transformation and scalable analysis.

Key Analytical Views

View	Purpose
vw_behavioral_escalation	Detect gaming intensity escalation patterns
vw_composite_mental_risk	Calculate mental health risk index
vw_demographic_risk	Analyze demographic risk distribution
vw_risk_classification	Categorize players into behavioral risk levels
vw_gamer_personas	Segment players into behavioral personas
vw_persona_summary	Aggregate persona behavioral metrics
vw_spending_risk_analysis	Analyze gaming spending behavior

This layered design enables efficient transformation of behavioral gaming data into analytical insights.

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📁 Project Structure

gaming-behavior-mental-health-analytics
│
├── data
│   └── gaming_mental_health_10M_40features.zip
│
├── sql
│   └── gaming_analytics_pipeline.sql
│
├── powerbi
│   ├── gaming_analytics.pbit
│   └── gaming_analytics_dashboard.pdf
│
├── docs
│   ├── problem_statement.pdf
│   ├── solution_architecture.pdf
│   └── business_insights.pdf
│
├── images
│   ├── executive_snapshot.png
│   └── behavioral_risk_analysis.png
│
├── assets
│   └── dashboard_background.png
│
└── README.md

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📌 Key KPIs

The dashboard tracks several behavioral indicators:

• Total Players
• Risk Player Count
• Average Gaming Hours
• Average Addiction Level
• Average Mental Risk Score
• Average Behavioral Escalation Score
• Average Sleep Duration
• Average Stress Level
• Average Depression Score

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📈 Expected Business Impact

This analytical framework helps gaming organizations:

• Detect early warning signals of gaming addiction
• Monitor behavioral risk escalation trends
• Identify high-risk player segments
• Understand lifestyle drivers influencing mental health
• Design data-driven wellbeing interventions

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👨‍💻 Author

Shivansh Yadav

Data Analytics | SQL | Power BI | Behavioral Analytics

🔗 LinkedIn
www.linkedin.com/in/the-venom

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Consider giving this repository a star ⭐ to support the project.

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