Bike Buyers Data Analysis Project

Overview

This project analyzes a dataset of potential bike buyers to identify key trends and factors that influence purchasing decisions. The project involves data cleaning, creating pivot tables, and building visualizations and a dashboard using Microsoft Excel.

Project Features

Dataset: The dataset includes information about individuals' income, marital status, education, occupation, commute distance, and whether they purchased a bike. Data Cleaning: Removed inconsistencies, handled missing values, and prepared the data for analysis. Pivot Tables: Created pivot tables to summarize and explore relationships in the data, such as the correlation between income, marital status, and bike purchases. Visualizations: Designed charts and graphs to visualize key trends in the dataset, including income distribution, bike purchases by region, and more. Dashboard: Built a dynamic dashboard that presents insights at a glance using slicers and filters to make it interactive. Technologies Used

Microsoft Excel: For data analysis, pivot tables, and dashboard creation. Excel Formulas: Used for data transformation and organization. Data Visualization: Charts, graphs, and pivot charts created in Excel for better data interpretation. Project Files

Excel Project Dataset: The original dataset used for the analysis. Excel Workbook: Contains the cleaned data, pivot tables, visualizations, and dashboard. How to Use

Download the Excel workbook from the repository. Open the file and explore the various sheets: Working Sheet: Data cleaned and prepared for analysis. Pivot: Various pivot tables summarizing key insights. Dashboard: Interactive dashboard with filters for easy exploration. Insights

Some key insights derived from this project include:

Income level has a noticeable impact on bike purchase decisions. Commute distance and occupation also influence whether individuals are likely to purchase a bike.