This repository contains the code and data files for a project that analyzes bike sales data. The project aims to uncover insights and trends from the data using various analytical and visualization techniques.

The data set used in this project is sourced from Alex The Analyst's GitHub repository. It contains information about customers who have purchased or not purchased bikes, including their demographic information, commute distance, and income.

Before the analysis, the data set underwent several cleaning processes. The processes included:

• Copying the raw data to a working sheet
• Standardizing the column names
• Removing duplicates and extra spaces
• Replacing spelling errors in the marital status and gender columns
• Removing symbols in the income column
• Creating age brackets based on the age column

The data set was summarized using pivot tables. The following pivot tables were created:

• Average income by gender customers who buy or don't buy bikes
• Total sales purchased or not bikes based on customer commuter distance
• Total sales purchased or not bikes based on customer age brackets
• Top 5 total sales purchased bikes based on customer middle age

The data was also visualized using graphs. The following graphs were created:

• Average income by gender customers who buy or don't buy bikes
• Total sales purchased or not bikes based on customer commuter distance
• Total sales purchased or not bikes based on customer age brackets The graphs helped to reveal several insights, including the following:

Male bike customers have a higher average income than female customers

• Bikes are popular for short trips, with most purchases occurring for distances under 1 mile
• Sales decrease as the distance increases, possibly due to a preference for cars or public transport
• Overall, the project provides valuable insights into the bike sales data and highlights trends that may be useful for businesses in the bike industry.

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