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This project analyzes coffee shop transactional data to evaluate revenue performance, customer behavior, product demand, and store-level sales distribution using Excel. The dataset contains 149,116 transactions including date, product category, item name, quantity, price, and location details.
Data was cleaned and structured using calculated columns, standardized date formats, and Pivot Tables to aggregate performance metrics. Key KPIs such as Total Sales ($698,812), Total Footfall (149,116), Average Order Value ($4.7), and Order Frequency (1.44) were calculated to measure operational efficiency.
The analysis identified peak demand on Friday (101,373 orders), the top-selling product (Barista Espresso – $91,406), and the highest-performing store (Hell’s Kitchen – $236,511). Large-sized orders contributed 34% of total transactions.
These insights support inventory optimization, staffing decisions, promotional planning, and location-based performance improvement strategies.

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This project analyzes retail sales data to evaluate outlet performance, item category contribution, location-tier distribution, and revenue trends using Excel dashboards. The dataset includes sales amount, item type, outlet size, location tier, rating, and establishment year.
Key KPIs such as Total Sales ($1.2M), Average Sales ($141), Number of Items (8,523), and Average Rating (4.0) were calculated to measure business performance.
The analysis identified Tier 3 outlets as the highest revenue contributors ($472K), Medium-sized outlets generating the largest share of sales ($507K), and Fruits & Vegetables as the top-performing category ($178K). Sales peaked significantly in 2018 ($204K), indicating strong year-based growth trends.
The insights support outlet expansion planning, category prioritization, and performance benchmarking across location tiers and outlet types

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This project presents a multi-page Power BI dashboard analyzing hospitality revenue and operational performance for AtliQ Hotels. The dataset includes booking transactions, property details, room categories, platform sources, and time-based dimensions structured into a relational data model. A star-schema architecture was designed linking fact tables (Bookings, Aggregate Bookings) with dimension tables (Date, Hotels, Rooms).
Over 25 DAX measures were created to compute KPIs such as Revenue (1.71bn), RevPAR, ADR, Occupancy %, Realisation %, Cancellation Rate, and Average Rating. The dashboard delivers executive insights across cities, room classes, hotel categories, and booking platforms, including weekday vs weekend comparisons and weekly trend analysis.
Skills applied: - Data Modeling, DAX calculations, KPI design, Time intelligence, Dashboard storytelling, segmentation analysis, and Performance analytics .

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This project presents a Power BI dashboard analysing AtliQ's workforce attendance, remote work (WFH), and sick leave trends. The dataset includes employee-level presence records with daily status indicators (Present, WFH, Sick Leave) across multiple months.

The dashboard tracks key KPIs such as Attendance % (91.8%), WFH % (10%), and SL % (1.1%) with time-series analysis to identify trends and behavioural patterns. It provides day-of-week comparisons, employee-level performance monitoring, and monthly trend visualization.

Interactive filters enable HR teams to analyze attendance behavior over time, detect inconsistencies, and monitor workforce productivity.
Skills applied: DAX calculations, time intelligence, KPI design, trend analysis, HR analytics, and dashboard storytelling in Power BI.

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This project analyzes movie industry data using SQL to explore financial performance, actor influence, and industry trends. The dataset contains relational tables including movies, actors, languages, and financial data such as budgets and revenues. By joining these datasets, the analysis investigates questions like which movies generate the highest return on investment, which actors contribute most to box-office revenue, and how revenue varies across industries and languages. The project also examines the relationship between IMDb ratings and commercial success. SQL techniques such as joins, aggregations, CASE statements, Common Table Expressions (CTEs), and window functions were used to generate insights about profitability, revenue concentration, and star power in the film industry.

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