🚴‍♂️ Bike Store Sales Analytics

📊 Project Overview

This project analyzes the sales data of a bike store to uncover purchasing patterns based on customer demographics and other key factors.

All stages were completed in Microsoft Excel, including data cleaning, pivot-based analysis, and dashboard development.

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📁 Workflow Structure

1. Data Separation

   • Raw Data: Unmodified source data
   • Worksheet: Data cleaning and transformation
   • Pivot Table: Summary and analysis
   • Dashboard: Visual representation

2. Data Cleaning (in Worksheet)

   • Removed duplicates
   • Applied text formatting functions (UPPER, LOWER, PROPER)
   • Trimmed extra white spaces (TRIM)
   • Used filters to detect spelling errors

3. Data Analysis

   • Built pivot tables to uncover insights

4. Data Visualization

   • Created charts based on pivot tables
   • Added slicers for interactive filtering

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📈 Key Insights

• Relationship between Income and Purchase Decision
• Product sales volume by:
  • Commute Distance
  • Age Category
  • Number of Children
• Slicers applied to filter by:
  • Marital Status
  • Education Level
  • Region
  • Home Ownership
  • Number of Cars

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📂 File Structure

/BIKE-STORE-ANALYTICS/ ├── Data-Sets | ├── raw-data.xlsx ├── Images | ├── cleaned-data.png | ├── dasboard.jpg | ├── pivot-tables.png | ├── raw-data.png ├── Worksheet | ├── Worksheet.xlsx ├── .gitattributes ├── README.md

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🛠 Tools Used

• Microsoft Excel
  • Data Cleaning
  • Pivot Table
  • Interactive Dashboard with Slicers

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💡 Lessons Learned

This project helped me:

• Strengthen my Excel data cleaning skills
• Interpret and segment customer behavior
• Build a functional, real-world dashboard for business insight