Jeevan Kumar
AI Annotation,SQL, Python, Excel, and Power BI,Data Analyst
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DMart Sales & Customer Analytics Dashboard
Overview
Developed an interactive Power BI dashboard to analyze retail sales, profitability, customer behaviour, product performance, and store-level performance using a 9,994-row DMart-style retail dataset.
Key Analysis
Sales and profit performance across time
Category and sub-category performance
Top-selling products
Customer segmentation and demographics
Payment method analysis
State, city, and store performance
Discount and profitability analysis
Customer rating analysis
Tools & Technologies
Power BI
DAX
Microsoft Excel
Data Cleaning
Data Modeling
Data Visualization
Key Power BI Features
Interactive slicers
KPI cards
Time-series analysis
Drill-down analysis
Conditional formatting
Dynamic DAX measures
Interactive page navigation
Tooltips
Date-table based time intelligence
Business Objective
The dashboard provides a consolidated view of retail performance and enables users to explore sales, profitability, customer behaviour, and geographic performance through interactive visualizations.
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Developed an interactive Power BI dashboard to analyze Chennai Super Kings’ IPL playoff performance from 2008–2023. I cleaned and transformed the match data, created DAX measures and KPIs, and designed interactive filters for season, playoff stage, and venue. The dashboard analyzes total matches, wins, losses, win percentage, average runs, highest score, performance by season and venue, top scorers, and opponent-wise results. This project helped me turn historical cricket data into clear, interactive insights for performance analysis.
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My Data Analytics Work
I have worked on four data analytics projects using SQL, Python, Excel, Power Query, and Power BI. My work includes cleaning and transforming raw datasets, creating interactive dashboards, developing DAX measures and KPIs, and analyzing business trends. I built dashboards for Uber trip and revenue analytics, Swiggy restaurant analysis, and CSK playoff performance, while also applying data-cleaning techniques to prepare accurate datasets. These projects helped me develop practical skills in data analysis, visualization, reporting, and extracting meaningful insights from data.
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“I developed an Uber Analytics Dashboard using Power BI to analyze trip performance, revenue, demand, customer experience, and operational trends. I worked on data cleaning and transformation using Power Query, created DAX measures and KPIs, and designed interactive visuals for trips by hour/day, revenue, payment types, pickup and drop areas, average fare, and distance. The dashboard helps identify peak demand periods, revenue patterns, popular areas, and customer payment preferences for better business decision-making.”
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