E Commerce Data Analysis In Excel Readme Md At Main Github

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This project involves a comprehensive Exploratory Data Analysis (EDA) of a simulated global online sales dataset. The primary goal was to transform raw transactional metrics into clear, actionable business insights regarding product performance, regional dominance, and sales trends over time. Tools Used: SQL (Querying & Aggregation), Microsoft Excel (Visualization & Dashboarding) The primary goal of this analysis was to move beyond simple reporting and develop a data-driven understanding of online sales performance to optimize business strategy. Specifically, this project addresses the need to answer three core questions: Performance Drivers: Which product categories and geographic regions are contributing the most revenue?

This project is an Excel-based interactive dashboard created using an e-commerce dataset. The goal is to analyze sales performance, customer behavior, product demand, delivery timelines, and customer satisfaction. The dashboard converts raw sales data into meaningful insights using Excel Pivot Tables, Pivot Charts, Slicers, and formulas. It provides a complete view of business performance and answers key analytical questions. 📁 Ecommerce-Excel-Dashboard │── README.md │── Ecommerce_Dashboard.xlsx │── Screenshots/ │ ├── dashboard.png │ ├── kpi_summary.png │ └── charts.png =SUM(Quantity) =SUMIF(Status,"Delivered",Amount) =AVERAGE(Rating) =AVERAGEIF(Status,"Delivered",DeliveryDays) =COUNTIF(Gender,"Female")

The core objective was to perform a deep-dive performance review focusing on financial efficiency and identifying the primary drivers of loss and opportunity. This was achieved by creating a fully dynamic and interactive dashboard that turns raw data into refined, actionable business intelligence The project focused on answering three main questions about the e-commerce operations: The dashboard successfully transformed the multi-dimensional dataset into an interactive tool for comprehensive business review. While consistent year-over-year sales growth was confirmed, the business's overall profitability is being systematically undermined by highly aggressive and poorly targeted discounting practices within the low-margin Furniture category, and these losses are worsened within... Based on the quantitative insights, the following three actionable recommendations are proposed for immediate implementation

There was an error while loading. Please reload this page. This project focuses on analyzing and managing data for an e-commerce platform. The objective is to build insights into customer behavior, product performance, and operational efficiency. The project uses SQL for data handling, Python (via Jupyter Notebook) for ETL processes, and various datasets to generate actionable insights. The ETL (Extract, Transform, Load) pipeline is a critical part of this project.

It processes raw e-commerce data into clean, structured formats, ready for analysis. The ETL pipeline is implemented in Python within the ETL_ECommerce_Project.ipynb notebook. This ETL process is designed for flexibility and adaptability, making it a robust framework for handling e-commerce data workflows! There was an error while loading. Please reload this page. There was an error while loading.

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