We use MS Excel to analyze a bike sales dataset using data cleaning methods and visualize the insights using Excel dashboards.
This project analyzes a dataset containing bike (cycle) sales information, including variables such as ID, Marital Status, Gender, Age, Income, Children, Properties, Location Info, and purchase status. Through thorough examination and analysis of the dataset, valuable insights can be gained to inform decision-making processes.
Duplicates are removed, and the data is cleaned and simplified for analysis. Gender and Marital Status categories are standardized for clarity and consistency. Income data is formatted as currency and adjusted for readability. Errors such as spelling mistakes and blanks are identified and corrected. Age data is grouped into brackets to simplify analysis using logical formulas.
Pivot tables are utilized to extract meaningful insights from the dataset. Three distinct pivot tables are created to visualize average income per purchase, customer commute patterns, and age categories of customers. The pivot tables are adjusted and refined to present actionable data. Relevant charts are generated to accompany the pivot tables, enhancing visualization and understanding of the insights.
A comprehensive dashboard is constructed to consolidate and visualize the analysis findings. All pivot tables and charts are integrated into the dashboard, providing a holistic view of the data. Slicers are added to enable dynamic filtering based on Marital Status, Region, and Education, facilitating deeper exploration of the dataset. The dashboard is meticulously designed for clarity, coherence, and aesthetic appeal, with final touches applied to ensure a polished presentation.
Explore the fully interactive Bike Sales Dashboard here