Raw data is only valuable when it leads to actionable insights. In this project, I transformed an...

Raw data is only valuable when it leads to actionable insights. In this project, I transformed an...Raw data is only valuable when it leads to actionable insights. In this project, I transformed an...

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Muhammad Muneeb

• Jul 18

Raw data is only valuable when it leads to actionable insights.

In this project, I transformed an e-commerce sales dataset into a structured and analysis-ready format using Microsoft Excel. The dataset includes customer information, product details, payment methods, order status, discounts, referral sources, and sales records—providing a strong foundation for business reporting and decision-making.

Project Highlights

Data Cleaning & Validation Structured Sales Database Customer & Product Analysis Payment Method Analysis Order Status Tracking Excel Functions & Data Management Dashboard-Ready Dataset

Tools Used: Microsoft Excel, Data Cleaning, Data Analysis, Excel Functions, Business Analytics

I help businesses organize, clean, and analyze their data to uncover meaningful insights and support smarter decisions.

#MicrosoftExcel #DataAnalytics #BusinessAnalytics #Excel #DataCleaning #DataManagement #DataVisualization #Ecommerce #Freelance #Contra

Data Analysis Microsoft Excel Data Entry Web Scraping

Humrahi Teaching hub

• Jul 16

:📊 Hello everyone! I'm a beginner Data Analyst with skills in Microsoft Excel and PDF to Excel conversion.

I can help with: • Excel data cleaning • PDF to Excel conversion • Data formatting data analysis

Microsoft Excel pdfconverting Data Analysis

RANEEM 52

• Jul 14

Hi that's my first post I work as Data entry on EXCEL

If you wanna any thing about it text me

That's example about my work

Data Entry Excel VBA

saranya Kangayam

• Jul 16

hi i want to join with you

Advik Rathee

• Jul 14

Built this one for an e-commerce customer behavior project - 18,000+ customers, segmented using RFM (Recency, Frequency, Monetary) analysis.

The interesting part wasn't the segmentation itself, it was what it revealed: nearly 22% of customers were "at risk" of churning, and most businesses wouldn't know that until those customers had already stopped buying.

This is the kind of insight that's usually sitting in your order data already, it just needs the right lens. Happy to take a look at your numbers if you're curious what's hiding in yours.

Tools used: Power BI, SQL, Python (Pandas, Scikit-learn for clustering)