A complete hands-on practice repository for learning Pandas in Python. Covers data cleaning, missing data handling, merging & joining, row operations, updating values, descriptive analysis, and working with real datasets like Superstore. Ideal for beginners and data analysis learners.

SuperStore Business Intelligence Dashboard

Executive-level Power BI dashboard built on the Superstore dataset using the .pbip project structure for version control and collaboration. Delivers quarterly sales insights across regions, product categories, and customer segments, with a clean semantic model and consistent design system.

An interactive sales dashboard developed using Power BI / Tableau to analyze key business metrics, identify trends, and support data-driven decision-making.

My first portfolio project showcasing data cleaning, analysis, and visualization of Superstore sales data.

Interactive Excel sales dashboard built with PivotTables, PivotCharts, and Slicers.

End-to-end Exploratory Data Analysis (EDA) project on Superstore Sales dataset including data cleaning, feature engineering, outlier handling, customer segmentation, and business insights using Python, Pandas, Matplotlib, and Seaborn.

Data Analytics internship project — retail sales analysis, RFM segmentation & business insights using Python, Pandas, and the SuperStore dataset (MashupStack, 2026)

Interactive Tableau dashboards analyzing sales performance and customer behavior to drive data-driven business decisions.

This is the project for analysis of SuperStore Dataset using MSExcel.

visualization of 2018-2021 superstore sample data in US

superstore Sales Analysis Using Python

Sales Trend and Profit Analysis using Python, Pandas, Matplotlib, and Seaborn to analyze business sales performance, profitability, product trends, and customer insights from the Superstore dataset.

An end-to-end Excel project: cleaned Superstore dataset and built an interactive sales dashboard to uncover key business insights.

Interactive Tableau Story analyzing Superstore sales data. Evaluates profitability across regions and cities, identifying high-value markets (e.g., California, New York) and underperforming areas to optimize revenue strategy.

Interactive Power BI dashboard analyzing Superstore sales (2014–2017). Includes KPI cards, trend analysis, regional and category breakdowns, and drill-down filters to identify top-performing segments and business insights.

Tableau dashboards analyzing Superstore sales data. Covers segment-wise sales, profit by category, regional performance, shipping trends, and time-based insights. Focused on visual storytelling for actionable business decisions.

SQL analysis of customer value from Superstore dataset.

This repository showcases various data analyses on the popular Superstore dataset using SQL queries. The analyses cover a range of business insights, including sales performance, customer segmentation, and product profitability. Each analysis is documented with the SQL queries used and explanations of the steps involved.