Project 1

Gadget Sales & Business Performance Dashboard 

Analysis Overview: This project involved a comprehensive business intelligence audit of HKS Telecoms' retail operations. Using Power BI, I transformed raw transactional data into an interactive executive dashboard to track profitability and regional growth.

Key Insights & Achievements:

• Profitability Tracking: Successfully visualized a 36.69% Profit Margin and ₦9.84M in Total Revenue across the 2023–2026 period.

• Regional Growth Analysis: Identified Yaba (₦2.1M) and Ajah (₦1.9M) as the lead revenue-generating locations, providing a roadmap for resource allocation.

• Inventory Velocity: Analyzed product performance, identifying Power Banks (178 units) and USB Cables (173 units) as the highest-volume items.

• Customer Segmentation: Ranked top-performing customers to help the sales team identify high-value retention opportunities.

Tools Used: Microsoft Excel (Data Cleaning and Visualization).

Project 2

Lewis DB Relational Business Audit 

Executive Summary

In this project, I performed a comprehensive relational database audit using PostgreSQL to extract actionable business insights from the Lewis_DB dataset. The objective was to transform raw transactional data into high-level reports that support inventory management, regional sales strategies, and pricing optimization.

Key Technical Milestones:

• Data Integrity & Exploration: Performed structural audits to identify unique product categories and geographic property distributions, ensuring a clean foundation for analysis.

• Business Intelligence Aggregations: Developed complex statistical queries to calculate average price points by category and projected inventory valuations, providing a clear view of asset worth.

• Advanced Relational Mapping: Leveraged INNER JOINs to connect disparate tables, successfully identifying high-volume "Most Purchased" products and tracking time-sensitive sales trends across specific date ranges.

The Impact

By bridging the gap between raw data and operational logic, these queries enable a business to:

1. Optimize Stock: Identify which products drive the most volume to prevent stockouts.

2. Strategic Pricing: Understand category averages to remain competitive in the market.

3. Targeted Marketing: Identify which properties (locations) are most active for better resource allocation.

Project 3

Airline Route Profitability and Operational Cost Analysis

Executive Summary

The Airline Route Profitability Dashboard analyzes revenue performance, operating costs, and aircraft efficiency across multiple airline routes. The analysis identifies the most profitable and underperforming routes, as well as key operational factors affecting profitability.

Findings reveal that long-haul routes generate the highest revenue, while some routes operate at a loss due to high operating costs and lower load factors.

Project Objective

The objective of this project is to evaluate airline route performance using key financial and operational metrics. The analysis helps identify profitable routes, understand revenue drivers, and assess aircraft performance to support data-driven operational decision-making. 

Data Source

The dataset used for this analysis is a publicly available dataset designed for data analysis practice. It contains information on airline routes, aircraft types, operating costs, revenue, profit, and passenger load factors.

Tool Used

Microsoft Excel

Business Questions

1. Which airline routes generate the highest profits?
2. Which routes are underperforming or generating losses?
3. How does load factor affect revenue generation?
4. Which aircraft types generate the highest profitability?
5. How do profits change across months?
6. Which route category generates the highest revenue?

Data Cleaning Process

• Removed duplicate records
• Standardized route names
• Verified revenue and cost values
• Created calculated fields for profit and profit margin
• Categorized routes into short‑haul, medium‑haul, and long‑haul

Key Measures [DAX]

Total Revenue = SUM(AirlineData[Revenue])
Total Profit = SUM(AirlineData[Profit])
Avg Profit Margin = AVERAGE(AirlineData[Profit Margin])
Total Operating Cost = SUM(AirlineData[Operating Cost])
Avg Load Factor = AVERAGE(AirlineData[Load Factor])

Dashboard Features:

• KPI cards displaying key metrics

• Route profitability comparison charts

• Load factor vs revenue scatter plot

• Monthly profit trend visualization

• Interactive filters for origin, destination, and season

Key Insights

• Routes such as DXB–FRA and DXB–SIN generate the highest profits.

• Some routes operate at a loss due to high operating costs.

• Higher passenger load factors significantly increase revenue.

• Long-haul routes generate the highest revenue despite higher costs.

• Aircraft types such as Boeing 777 and Airbus A380 deliver higher profits.

Business Recommendations

• Increase flight frequency on high-performing routes.

• Reevaluate pricing or scheduling for underperforming routes.

• Improve seat utilization through better demand forecasting.

• Deploy larger aircraft on high-demand routes.

Conclusion

The analysis demonstrates that route demand, aircraft capacity, and passenger load factors play critical roles in airline profitability. Optimizing these factors can significantly improve financial performance.

Skills Demonstrated

• Data Cleaning

• Data Visualization

• Business Analysis

• Dashboard Development

• Data Storytelling

Project 4

Global Climate Risk and Extreme Weather Impact Analysis

Executive Summary

This dashboard analyzes global climate change indicators, including carbon emissions, extreme weather events, temperature changes, and sea level rise.

The analysis highlights regions most affected by climate change and identifies relationships between emissions and environmental risks.

Project Objective

The objective of this project is to analyze climate data across regions to understand the environmental impact of carbon emissions and extreme weather events.

Data Source

The dataset used in this analysis contains information on carbon emissions, extreme weather events, temperature changes, sea level rise, and population affected by climate-related disasters.

Tools Used

• Power BI (Visualization)

• Microsoft Excel (Data Cleaning)

Business Questions

• Which regions produce the highest carbon emissions?

• Which regions experience the most extreme weather events?

• How many people are affected by climate change?

• Which regions have the highest climate risk scores?

• Is there a relationship between emissions and sea level rise?

Data Cleaning Process

• Removed incomplete records

• Standardized country and region names

• Verified emission values

• Aggregated weather events by region

• Ensured consistency in measurement units

Key Measures

• Total CO2 Emissions = SUM(CO2_Emissions)

• Population Affected = SUM(Population_Affected)

• Total Heatwaves = SUM(Heatwaves)

• Average Temperature Change = AVERAGE(Temperature_Change)

• Average Sea Level Rise = AVERAGE(Sea_Level_Rise)

• Climate Risk Score = AVERAGE(Risk_Score)

Dashboard Features:

• Regional comparison charts

• Climate risk score analysis

• Extreme weather event distribution

• Population impact analysis

• Scatter plot showing emissions vs sea level rise

Key Insights

• Asia records the highest carbon emissions globally.

• Asia also shows the highest climate risk score and population impact.

• Extreme weather events occur across all regions.

• Higher carbon emissions are associated with increased environmental risks.

Recommendations

• Governments should implement emission reduction policies.

• Invest in renewable energy and sustainable infrastructure.

• Improve disaster preparedness in high-risk regions.

Conclusion

The analysis highlights the strong relationship between emissions, environmental risk, and population vulnerability, emphasizing the need for urgent global climate action.

Skills Demonstrated

• Data Visualization

• Data Analysis

• Environmental Data Interpretation

• Dashboard Design

• Data Storytelling

 Project 5

Cardiovascular Disease Monitoring & Risk Analysis (2019–2023) 

Executive Summary

In this project, I developed an interactive Power BI dashboard to monitor Cardiovascular Disease prevalence and analyze key health indicators across a massive dataset of 383,800 patients. The goal was to provide healthcare administrators with a diagnostic tool to identify high-risk patient segments and lifestyle correlations.

The Challenge

Managing health outcomes for nearly 400,000 patients across multiple facilities requires more than just raw counts. The challenge was to transform a high-volume dataset into a responsive tool that correlates BMI, Glucose Levels, and Physical Activity with CVD status to pinpoint where preventative care is most needed.

Technical Stack

• BI Tool: Power BI

• Data Engineering: Power Query (ETL, Data Cleaning, and Attribute Mapping)

• Analytics: DAX (Dynamic Measures and Calculated Columns)

• Statistical Foundation: Prevalence Rate Analysis and Demographic Distribution Profiling.

Key Insights & Features

• Scale: Successfully managed and optimized a dataset of 383.8K records, ensuring high performance and seamless cross-filtering within the Power BI engine.

• End-to-End ETL: Performed all data cleaning and transformation within Power Query, including the creation of standardized BMI and Age categories (Young to Elderly).

• Demographic Slicing: Developed dynamic filters for Gender, Age Category, and Hospital Location to enable granular, facility-specific reporting.

• Risk Correlation: Visualized the direct link between Obesity (Obese/Overweight) and CVD cases, highlighting a 15% overall prevalence rate.

• Lifestyle Impact: Integrated a "Physical Activity" vs. "CVD Status" analysis, demonstrating the protective correlation of moderate-to-high activity levels.

The "So What?" (Impact)

This dashboard allows hospital boards to:

• Allocate Resources: Identify specific facilities or demographics with higher prevalence for better staffing and equipment distribution.

• Targeted Campaigns: Pinpoint "Pre-diabetic" and "Obese" clusters for early intervention and public health awareness.

• Policy Evaluation: Track prevalence trends from 2019 to 2023 to measure the effectiveness of historical health interventions.