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This project leverages the AdventureWorks dataset to analyze sales performance using Power BI. The dashboard provides a comprehensive view of:
Sales by salesperson
Year-over-Year (YoY) growth
Total and lifetime sales
Sales by region and country
The purpose of this project is to help stakeholders understand sales trends, identify top-performing sales representatives, and evaluate business growth over time.
This project is a deep dive into telecom customer churn using an interactive Excel dashboard I built from scratch. It blends data cleaning, thoughtful visualization, and storytelling to uncover why customers leave and how businesses can win them back.
Click here to interact with the live visualization
Highlights:
📈 26.86% churn rate uncovered from a 6,687‑customer dataset
🔍 Segmented churn by age, usage, geography, and reason for more actionable targeting
🗺 Identified high‑risk regions with up to 75% churn among specific plan holders
🛠 Offered data‑driven retention strategies from plan re‑bundling to device upgrade programs
Key Insights:
Competitors are winning on offer value and device appeal
Heavy data users churn more, signalling a price‑to‑value gap
Younger customers are more offer‑sensitive, while seniors value reliability
Certain states showed extreme churn rates for international‑plan customers
What I Did:
Cleaned and prepared raw churn data in Excel
Built pivot‑based interactive visuals with slicers and conditional formatting
Designed a dashboard that allows quick filtering by segment and churn reason
Summarized findings into clear, strategic recommendations
Skills Demonstrated:
Data Cleaning · Data Visualization · Cohort Analysis · Business Insights · Storytelling with Data · Excel Dashboard Design
Why It Matters:
This wasn’t just about making charts; it was about helping a business see the story hidden in its churn numbers, so they can take targeted, profitable action to keep customers loyal.
This interactive Excel dashboard visualizes who buys bikes and why. By analyzing demographic and lifestyle data, I discovered that:
Middle‑aged customers are the biggest buyers
2–5 mile commuters purchase most often
Income has a nuanced relationship with purchase decisions — not always higher income = more purchases
What I Did:
Cleaned and analyzed sales data in Excel
Built an interactive dashboard with filters for marital status, region, and education
Used charts to highlight gender‑income patterns, age purchase trends, and commute influences
Skills: Data Cleaning · Pivot Analysis · Dashboard Design · Business Insights
This project shows how a well‑designed dashboard can turn raw sales data into marketing actions such as targeting mid‑distance commuters with tailored bike offers.
This project explores insights from a Data Professionals Survey using Power BI. The dashboard highlights key aspects such as:
Demographics of survey participants
Salary distribution by job role
Programming language preferences
Perceptions on breaking into the data field
Work-life balance and salary satisfaction
The goal of this project is to analyze the career trends, challenges, and satisfaction levels of data professionals across the globe.
Click here to interact with the Power BI report
This project focuses on data cleaning using SQL. The dataset used is the Nashville Housing dataset, which contains property sales information. The goal was to prepare the raw dataset for analysis by handling missing values, standardizing formats, splitting columns, and removing duplicates.
🎯 Objectives
🎯 Objectives
Standardize date formats for consistency.
Populate missing property addresses.
Split combined columns (Address, City, State) into individual fields.
Normalize categorical values (e.g., converting Y/N to Yes/No).
Identify and remove duplicate records.
Drop unnecessary columns for a cleaner dataset.
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