Excel-Practice-Project

Excel-based data cleaning and visualization Bike Sales Dashboard (Excel)

Project Overview

This project analyzes customer demographics and purchasing behavior to understand the factors influencing bike purchases. The goal is to identify patterns in income, age, commute distance, and customer attributes that impact buying decisions.

The dashboard was built entirely in Microsoft Excel using Pivot Tables, slicers, and visualizations.

Dataset DescriptionRaw Practice Bike Dataset.xlsx

The dataset contains customer-level records including demographic, socioeconomic, and behavioral variables.

Data Dictionary – Bike Sales Dataset

Column Name	Description
Age	Customer age in years
Gender	Male or Female
Marital Status	Single or Married
Education	Customer education level
Region	Geographic region of customer
Income	Annual income of customer
Commute Distance	Distance travelled daily to work
Purchased Bike	Indicates if customer purchased a bike (Yes/No)

Key Variables:

• Age
• Gender
• Marital Status
• Education Level
• Region
• Income
• Commute Distance
• Purchased Bike

The data was structured and cleaned in Excel before analysis.

Tools Used

• Microsoft Excel
  • Pivot Tables
  • Pivot Charts
  • Slicers
  • Data Cleaning (Excel functions)

Data Preparation

The following steps were performed before analysis:

• Checked for duplicates and removed where necessary
• Standardized categorical values (e.g., gender, marital status, age grouping)
• Verified no missing or inconsistent values
• Structured data into an Excel Table for pivot stability

Key Analysis & Visualizations

1. Income vs Bike Purchase

• Compared average income of customers who purchased vs those who did not
• Found that customers who purchased bikes generally had higher average income

2. Age Bracket Analysis

• Analyzed purchase behavior across age groups (Adolescent, Middle Age, Old Age)
• Middle-aged customers showed the highest purchase rate

3. Gender Comparison

• Compared male vs female purchase behavior
• Identified differences in purchase rate and income levels

4. Commute Distance Impact

• Examined how commute distance affects purchase decisions
• Short-to-medium commute customers were more likely to purchase bikes

5. Regional Trends

• Compared purchasing patterns across regions
• Identified which regions had stronger bike purchase adoption

Key Insights

• Customers with higher income levels show a higher likelihood of purchasing bikes
• Middle-aged individuals form the most responsive customer segment
• Commute distance influences purchase behavior, especially for shorter daily travel
• Regional and demographic differences suggest targeted marketing opportunities

Limitations

• Dataset does not include time-based variables (no trend analysis over time)
• No pricing or cost data to evaluate profitability
• Analysis is descriptive and does not include predictive modeling
• External factors (economic conditions, seasonality) are not included

Files Included

• Bike_Sales_Dashboard.xlsx (Excel dashboard with pivot tables and slicers)

• Dataset (cleaned dataset used for analysis) DATA PRACTICE ON BIKE SALES.xlsx

Business Recommendations

• Target middle-aged, higher-income customers with premium bike offers
• Focus marketing on short-to-medium commute users
• Use regional trends to localize promotions

Author

Oluwapelumi Atanda
Junior Data Analyst (Excel | Research | Healthcare Data)