The dataset consists of individual profiles, each uniquely identified by an ID. It encompasses a range of attributes, including their marital status, gender, annual income, the number of children in their family, educational attainment, occupation, home ownership status, the count of cars they possess, the distance they commute, regional location, and their age. Moreover, a binary variable indicates whether each individual has made a bike purchase or not.
Removed duplicate entries from the dataset to ensure data accuracy. Replaced abbreviations for marital status and gender with their corresponding full forms. Formatted the income data to reflect currency values. Categorized variables such as children, education, occupation, cars, commute distance, and region. Also created age brackets using conditional statements to group individuals based on their age ranges. Defined categories such as "Adolescent," "Middle Age," and "Old" based on specific age ranges.
Utilized pivot tables to analyze data and build the dashboard. Created visualizations, including charts and graphs, to represent key insights from the dataset based on the below Categories.
- Average Income by Bike Purchase and Gender
- Commute Distance and Bike Purchases
- Age Brackets and Bike Purchases
Constructed an interactive dashboard using pivot tables and visualizations. Customized the layout, appearance, and formatting of the dashboard to enhance user experience.
Incorporated filters and slicers to enable users to interact with the dashboard and explore specific data subsets. Implemented filtering options based on marital status, region, education, and other demographics.
