Gaming Behavior & Mental Health Analytics Dashboard

An advanced, multi-page Business Intelligence solution designed to analyze the intersection between gaming habits, psychological metrics, socioeconomic factors, and daily productivity. This project processes complex behavioral data to uncover how gaming intensity impacts mental well-being, academic/work performance, and lifestyle quality.

Technical Architecture & Methodology:

• Data Engineering & Cleaning (Python): Processed the core behavioral dataset using Pandas and NumPy to handle data normalization, treat missing values, and structure relational metrics for multi-dimensional analysis.

• Advanced BI Architecture (Power BI): Developed a highly interactive, 3-page dark-themed dashboard (Overview, Productivity, and Health Impacts) utilizing professional UI/UX design standards to ensure seamless navigation and data storytelling.

Comprehensive Dashboard Breakdown:

1. Executive Overview Page:

• Tracked core psychological KPIs: High Addiction Rate (4.8%), Medium Addiction (20.3%), Low Addiction (74.9%), and an Overall Average Addiction Index of 2.83.

• Visualized demographic and academic distributions, examining how addiction levels vary by Age Groups and evaluating the correlation between stress levels and gaming intensity.

2. Productivity & Socioeconomic Analysis Page:

• Analyzed key behavioral matrices against performance metrics, including Work Productivity, Stress Levels, and Sleep Hours across different risk categories.

• Correlated long-term gaming metrics by mapping the average productivity trends against years of gaming experience, alongside evaluating financial status (Income by Age Group).

3. Health & Social Impacts Page:

• Evaluated physical and emotional tolls by visualizing cumulative scores for Eye Strain, Back Pain, Aggression, Happiness, and Loneliness across demographics.

• Examined environmental influences by tracking the impact of Parental Supervision on happiness scores and weekend gaming hours.

Key Deliverables:

• End-to-end data pipeline from Python preprocessing to Power BI deployment.

• Multi-dimensional dynamic filtering (Age Group, Gender, Weekly Sessions) enabling immediate ad-hoc querying.

• Actionable data insights mapping behavioral risk factors to physical and mental health consequences.