E Commerce Sales Analysis Using Excel Readme Md At Main Github

Leo Migdal

-Nov 18, 2025, 6:44 AM

e commerce sales analysis using excel readme md at main github

There was an error while loading. Please reload this page. There was an error while loading. Please reload this page. Business Intelligence Developer | Data Analyst | SQL Developer | Power BI & Excel Reporting | Turning Data into Insights 🚀 Excited to share my latest Excel Dashboard Project — FreshMart Sales Insights Dashboard!

The focus of this project was to take raw data from FreshMart Store and transform it into an interactive, insight-driven tool that helps decision-makers analyze performance with just a few clicks. 🛠 Tools & Techniques in Excel: Power Query → Automated data cleaning & preprocessing Power Pivot → Data modeling & calculated measures Pivot Tables & Pivot Charts → Interactive, filter-responsive analysis GETPIVOTDATA → Dynamic... Delivery Time: 24.36 mins | On-Time Rate: 76.08% • September Peak: 4,231 orders generating $1.213M • Organic channel drove most sales 🧠 This project strengthened my Excel modeling, data storytelling, and KPI design skills... 🔗 Explore full project on Github : https://lnkd.in/g6kxKiBf #ExcelDashboard #DataAnalytics #DataVisualization #ExcelSkills #PortfolioProject #Analytics #BusinessIntelligence #Excel #PowerQuery #PowerPivot #DataAnalytics #DataAnalysis #SalesAnalytics #DashboardDesign #InteractiveDashboard #ExcelCommunity #ExcelTips #DataVisualization #DataDriven #DataStorytelling #MicrosoftExcel #AnalyticsDashboard #Reporting #BI #ExcelProjects #DataModelling... Experienced Audit and Banking Professional | Transitioning into Data Analytics to Power Business Growth 🚀 Blinkit Sales & Regional Performance Dashboard – Power BI Project Thrilled to share my latest Power BI dashboard designed to analyze Blinkit’s sales performance across outlets, regions, and product categories.

🔍 Project Overview: This dashboard provides a centralized view of KPIs — Total Sales, Average Sales, Ratings, and Item Counts — helping management make data-driven decisions. It explores performance variations across outlet tiers, establishment years, and item attributes like fat content and product type. 💡 Tech Stack Used: Power BI Desktop | Power Query (M Language) | DAX | CSV/Excel | Git & Git LFS 📊 Dashboard Highlights: $96.3K Total Sales | 3.3 Avg. Rating | 35 Items Outlet Size & Tier Insights: Tier 1 outlets lead with $96K in total sales Sales Trend Analysis: Peaks in 2015 & 2021 Top Categories: Snacks, Soft Drinks & Breakfast items... 💻 Explore More: 🔗 View Dashboard: https://lnkd.in/g8VHDiUe 💻 GitHub Repository: https://lnkd.in/gm83gxHW 🌐 Portfolio: https://lnkd.in/gMqdrHWT Would love your thoughts or suggestions to make it even better! 🙌 #PowerBI #DataAnalytics #DashboardDesign #Blinkit #BusinessIntelligence #MicrosoftPowerBI #DataVisualization #AnalyticsProject #GitHubProjects #Portfolio #DataDriven

Aspiring Data Analyst | Data Science | B.Tech | Python | SQL • Tableau • R • Excel | EventScape @ GDGoC IPSA | Google Cloud Arcade | Author @ Guru Shishya Publications |... There was an error while loading. Please reload this page. Project Overview: This project involves a comprehensive analysis of e-commerce sales data using Microsoft Excel. The main objectives were data cleaning, data processing, data analysis, data visualization, and creating a dynamic dashboard to present insights effectively. In this project, I have analyzed a dataset containing e-commerce sales data to derive meaningful insights and patterns.

The analysis includes various stages starting from data cleaning to creating an interactive dashboard for better data interpretation. 1 Removed duplicates and irrelevant data. 2 Handled missing values by imputation or deletion as required. 3 Corrected data types for accurate analysis. There was an error while loading. Please reload this page.

[E-Commerce Sales Analysis Project]- This project analyzes an e-commerce dataset using Excel for data cleaning and Power BI for building an interactive dashboard. The goal is to uncover key business insights that help understand customer behavior, revenue patterns, and overall business performance. [Project Highlights] ✔️ Data cleaning and preprocessing in Excel ✔️ Interactive dashboard built in Power BI ✔️ Insights on: Monthly sales trends Top-performing cities Gender-wise order distribution Order status breakdown ✔️ Business-focused storytelling with clean visuals [Tools Used] Microsoft Excel – Cleaning, formatting, preprocessing Power BI – Dashboard design, DAX basics, business insights [Key Insights] -Peak sales observed during festive months -Customer orders higher in major metro cities -Female customers contributed slightly more to total orders -Most orders fall under Delivered and Shipped categories

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")

E Commerce Sales Analysis Using Excel Readme Md At Main Github

People Also Search

There Was An Error While Loading. Please Reload This Page.

The Focus Of This Project Was To Take Raw Data

🔍 Project Overview: This Dashboard Provides A Centralized View Of

Aspiring Data Analyst | Data Science | B.Tech | Python

The Analysis Includes Various Stages Starting From Data Cleaning To