I created this Freelancer Earnings Analysis Dashboard using Microsoft Excel to analyze income patterns of freelancers across different regions, roles, experience levels, education, and platforms. The dashboard brings together multiple earning metrics, including annual income and hourly rates, to provide a clear picture of how freelancers earn globally.
In this project, I analyzed earnings by job role (such as AI/ML Engineering, Backend Development, Cybersecurity, Data Engineering, DevOps, Full Stack, and Web Development) to compare high, medium, and low income levels. I also examined how experience level (junior, mid, senior) impacts earning potential across roles.
To add deeper insights, I built visualizations showing earning rates by education level (Bachelor’s, Bootcamp, PhD, and Self-taught) and earning distribution by freelancing platform, including Direct Clients, Fiverr, Freelancer, LinkedIn, Upwork, and others.
The dashboard is fully interactive, using Excel slicers to filter data by region, category, platform, and experience level, allowing users to explore earnings from multiple perspectives and quickly identify trends.
What this project demonstrates:
Advanced use of Excel dashboards and slicers
Data aggregation using Pivot Tables and Pivot Charts
Comparative income and rate analysis
Ability to design clear, insight-driven Excel visuals
Translating raw freelance data into practical insights
This project highlights my ability to use Excel for real-world data analysis, build interactive dashboards, and communicate complex earning patterns in a simple and effective way.

This project explores a retail store’s sales dataset using SQL to uncover key business insights. I began by cleaning the data, correcting column names, and fixing data types to ensure accurate analysis. After preparing the dataset, I answered 23 business-focused questions covering sales performance, customer behavior, profitability, and regional trends.
Using SQL techniques such as aggregation, grouping, date functions, and conditional logic, I analyzed total revenue, profit margins, top-performing categories, monthly sales trends, payment preferences, and repeat customer activity. The project also highlights products with high quantity but low profit, cities with the highest profitability, and seasonal sales patterns.
The results provide a clear, data-driven understanding of what drives revenue and profit for the store. This project demonstrates my ability to clean data, write efficient SQL queries, and translate raw data into actionable business insights.

In this project, I analyzed sales data to understand revenue performance, profitability, and business trends. The goal was to move beyond raw numbers and transform transactional data into meaningful financial insights that can support business decision-making.
Using Python in Jupyter Notebook, I built revenue and profit logic from scratch and performed exploratory data analysis to identify patterns, strengths, and areas for improvement in the business performance.
This project reflects my ability to combine technical analysis with business thinking.
Tools & Technologies Used
Python
Pandas
Matplotlib
Jupyter Notebook
Excel (for initial review and validation)
What I Did
🔹 Revenue & Profit Modeling
I developed core financial metrics including:
Revenue = Quantity × Unit Sale Price
Profit = (Quantity × Unit Sale Price) − (Quantity × Unit Cost)
Profit Margin % calculation
This allowed me to measure not just sales volume, but true profitability.
🔹 Exploratory Data Analysis (EDA)
I performed detailed analysis to:
Identify top-performing products
Analyze revenue distribution by region
Compare high-margin vs low-margin products
Evaluate overall business performance trends
I also created visualizations to clearly communicate patterns and financial insights.
Key Insights
A small group of products contributed the majority of total revenue.
Some high-selling products had low profit margins, affecting overall profitability.
Revenue performance varied across regions, revealing areas for strategic focus.
Profit margin analysis helped highlight opportunities for pricing optimization.
What This Project Demonstrates
This project showcases my ability to: Clean and manipulate structured data using Python
Apply financial logic to real business data
Perform exploratory data analysis
Translate technical findings into business insights
Communicate results clearly through visualization
It reflects my growing expertise in data analysis and my focus on solving business problems using data-driven approaches.

I built this interactive Smoking Health Risk Analysis dashboard using Power BI to analyze how smoking impacts different organs and overall health outcomes. The dashboard includes an organ-level slicer that allows users to switch between the Human Body, Lungs, Liver, Kidney, and Heart. In the current view, the slicer is set to Human Body, which is why the full anatomical visual is displayed.
The analysis is based on data from patients and focuses on key health indicators such as age, BMI, smoking status, cholesterol levels, and hypertension risk. I categorized patients into Never, Current, and Former smokers and analyzed their distribution across gender and age groups.
To deepen the analysis, I examined smoking duration (years of smoking) and daily cigarette intake, highlighting how smoking intensity changes across different age ranges. I also visualized cholesterol and hypertension risk levels (High, Normal, Low) by age group to identify populations with higher health risks.
I designed the dashboard to be highly interactive, using slicers, filters, KPIs, and a Healthy vs. Damaged toggle to make the insights easy to explore and understand. My goal was to transform complex healthcare data into a clear, engaging, and decision-focused visual experience.
Skills demonstrated:
Power BI dashboard development
Data modeling and visualization
Health risk and demographic analysis
Interactive slicers and filters
Data storytelling with healthcare data
This project demonstrates my ability to build personalized, insight-driven dashboards that communicate meaningful healthcare insights effectively.

This project is an interactive business intelligence dashboard developed using Microsoft Power BI
to analyze sales performance, revenue trends, product demand, and order channel effectiveness for a retail business. The dashboard was designed to provide decision-makers with a comprehensive view of key performance indicators and operational metrics, enabling data-driven decisions across sales, marketing, and inventory management.
The report analyzes more than $21.9 million in total revenue and 499,000 units sold across multiple retailers, product categories, and order methods, including Telephone, Web, E-mail, Mail, and Sales Visits. It includes dynamic filters for retailer name, date range, order method, and product line, allowing users to interactively explore the data and drill into specific business segments.
The dashboard combines a wide range of visualizations to present insights from multiple perspectives. KPI cards summarize overall revenue and quantity sold, while line charts highlight monthly sales trends and cumulative revenue growth throughout the year. Bar charts compare retailer performance and average order values, treemaps reveal revenue contribution by product type, and pie charts illustrate the distribution of revenue across different sales channels. Additional analyses identify the top-selling products by quantity and compare average sales and profit margins for each order method.
Through this analysis, stakeholders can quickly answer critical business questions such as:
Which retailers generate the highest revenue?
Which products sell the most units?
Which order methods deliver the greatest profitability?
How does revenue fluctuate throughout the year?
Which product categories contribute the most to total sales?
The project demonstrates strong capabilities in data cleaning and transformation with Power Query, data modeling and relationship building, DAX measure creation, KPI design, and executive dashboard development. It also reflects an understanding of business intelligence principles, data storytelling, and user-centered report design.
Overall, this dashboard serves as a practical example of how Power BI can transform raw transactional data into actionable insights, helping organizations monitor performance, identify growth opportunities, and make informed strategic decisions.Project Description
Developed an interactive Power BI dashboard analyzing $21.9M in revenue and 499K units sold, featuring KPI tracking, profit margin analysis, product and retailer performance, and dynamic filters for executive-level business insights.
Skills & Tools Used
Microsoft Power BI
DAX (Data Analysis Expressions)
Data Modeling
Power Query
Data Cleaning and Transformation
KPI Reporting
Data Visualization
Business Intelligence
Dashboard Design

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