Project Objective:
The goal was to visualize and analyze renewable vs. non-renewable energy consumption across multiple cities, assess cost efficiency, and support decision-making for clean energy adoption.

Tools Used:

• Microsoft Power BI: For dashboard development and interactive visualizations
  DAX: For calculated fields and performance metrics
  Key Highlights:

• Total Energy Analysis:
  Renewable Energy: 33M kWh
  Non-Renewable Energy: 23.85M kWh
  Combined Consumption: 56.87M kWh
  Cost Efficiency:
  Renewable Energy Cost: $5.51M
  Non-Renewable Energy Cost: $23.85M
  Renewable energy is 76.9% more cost-effective, promoting sustainability.
  Emission Insight:
  Emission Factor: 389.82 (kWh CO₂/Kg)
  Supports eco-friendly policy decisions.
  City-wise & Seasonal Trends:
  Monthly renewable energy flow observed in cities like Atlanta, Chicago, and New York for 2023 & 2024.
  Summer was identified as the top-performing season for renewables.
  Interactive Parameters:
  Dynamic filtering allows users to toggle metrics based on energy type, cost, and emissions.
  Energy Share Breakdown:
  Renewable Energy: 58.06%
  Non-Renewable Energy: 41.94%
  Impact:
  This dashboard bridges the gap between raw energy data and decision-making, offering actionable insights into energy distribution, emissions, and cost. It emphasizes how clean energy adoption leads to both financial and environmental benefits, supporting sustainability goals.

Objective:
Developed an interactive Power BI dashboard to monitor and analyze agent performance, call resolution efficiency, and customer satisfaction levels over time.

Tools & Techniques:

• Power BI – Data visualization, interactivity, and storytelling

• DAX – Custom metrics for satisfaction scores, speed, and filters

• UX Design – Clean layout, intuitive slicers, and visual hierarchy

Dashboard Highlights:

• KPIs Tracked:

  • Answer & Resolution Rates: 81.08% calls answered, 72.92% resolved

  • Overall Satisfaction Score: 67.52 (across all agents and topics)

  • Topic-wise Insights: High, Middle, and Low categories for issue types

• Agent-Level Breakdown:

  • Performance metrics for each agent: answered/resolved count, satisfaction level, and average response time

  • Helps identify top and underperforming agents

• Time Filters:

  • Dynamic filtering by date, agent, and topic

  • Monthly breakdown of answered and missed calls

Impact:

• Empowered supervisors with a real-time performance snapshot

• Enabled data-driven decision-making to boost resolution rates and agent productivity

• Created a polished, branded dashboard experience for PwC-styled environments