Project Details

This project focuses on building a comprehensive Retail Sales Insights Dashboard using Power BI. The objective was to transform raw transactional data into meaningful business insights.

The dashboard includes key performance indicators such as Total Sales, Total Profit, Profit Margin, and Total Orders. Interactive visuals were created to analyze sales trends over time, product-wise performance, regional and store-level insights, customer demographics, and preferred payment methods.

Data modeling was performed using a star schema, and DAX measures were implemented to calculate sales, profit, profit margin, and order metrics. The final dashboard enables users to filter data dynamically by category, region, city, and date, helping stakeholders make data-driven decisions.

Live Interactive Dashboard

Project Details

As a data analyst, I worked on a revenue analysis project for AtliQ Grands, a leading five-star hotel chain in India facing declining revenue and market share. The objective was to support strategic decision-making through data intelligence. I was provided with historical data and a mock-up dashboard by stakeholders. I created key revenue metrics such as Occupancy %, RevPAR, ADR, and YoY Growth, built a fully interactive Power BI dashboard following the stakeholder design, and also added custom insights to highlight performance trends and growth opportunities across hotel segments, regions, and customer types. The final output enabled the revenue management team to make data-driven decisions and plan effective strategies to regain their market position.

Project Details

This project focuses on analyzing customer churn patterns using Python and Power BI. Data was cleaned and prepared in python, followed by modeling and visualization in Power BI using a star schema. The dashboard provides insights into churn by tenure, service usage, billing type, and customer demographics. Key KPIs and visualizations help identify early churn risks, especially among new customers, enabling better retention planning and decision-making.

Project Details

This project focuses on analyzing marketing campaign performance across multiple digital channels using Microsoft SQL Server and Power BI. The dataset was cleaned and structured in SQL, including data type handling, KPI calculations, and aggregation queries.
An interactive Power BI dashboard was developed to track key metrics such as impressions, clicks, conversion rate, cost, and ROI. Advanced visuals like KPI cards, line charts, bar charts, donut charts, and scatter plots were used to highlight trends, channel efficiency, and campaign effectiveness.
The dashboard enables stakeholders to compare channels, analyze performance over time, and make data-driven marketing decisions.

Project Details

This project focuses on analyzing employee attrition to uncover trends and factors influencing resignations. I used SQL to extract and clean employee data, Python to visualize attrition patterns, and Excel to prepare detailed summaries and reports. Key analyses include department-wise turnover, employee retention trends, and HR action effectiveness. The insights enable HR teams to implement targeted strategies to retain employees, improve engagement, and reduce attrition. Data was sourced from Kaggle and simulated HR datasets.

Project Details

This project involves an end-to-end analysis of International Debt Statistics published by the World Bank. Raw IDS files were cleaned, structured, and stored in relational tables using SQL. The analysis includes handling missing values, joining country and indicator metadata, and applying window functions to calculate year-over-year debt changes and volatility. SQL queries were used to identify long-term debt trends, compare countries and regions, and highlight patterns in external debt growth. Python was used for exploratory analysis and visualization to support insights derived from SQL queries. The project demonstrates strong SQL fundamentals, analytical thinking, and the ability to work with large economic datasets.

Project Details

Cloud Cost Optimization in Analytics is a business-focused data analysis project that evaluates cloud billing data to identify spending inefficiencies and optimization opportunities. Using Python, Pandas, and visualization libraries, I performed data cleaning, feature engineering, and service-level cost aggregation to determine the primary cost drivers.

The analysis includes monthly cost trends, region-wise cost distribution, and network usage impact on billing. Additional derived metrics such as Total Network (GB) were created to better understand infrastructure consumption patterns.

The project simulates a real-world scenario where organizations need visibility into cloud expenses to reduce waste, improve budgeting, and support financial decision-making through data-driven insights.