This repository contains the code and data files for an analysis of sales data for a bike store. The data contains information on sales for different products, customer demographics, and sales by date, location, and category.

The data used in this analysis is stored in a TXT file called bike_data.txt. The file contains the following columns:

• Day: The day of the month on which the sale occurred.
• Month: The month in which the sale occurred.
• Year: The year in which the sale occurred.
• Customer_Age: The age of the customer who made the purchase.
• Age_Group: The age group to which the customer belongs.
• Customer_Gender: The gender of the customer who made the purchase.
• Country: The country in which the sale occurred.
• State: The state in which the sale occurred.
• Product_Category: The general category of the product.
• Sub_Category: The specific sub-category of the product.
• Product: The name of the product.
• Order_Quantity: The quantity of the product ordered.
• Unit_Cost: The cost of one unit of the product.
• Unit_Price: The price at which the product was sold.
• Profit: The profit made on the sale.
• Cost: The total cost of the products sold.
• Revenue: The total revenue generated by the sale.

• Clean_Profit: The clean profit from the sale
• Seasons: The season of the year on which the sale occurred.

The code for the analysis is contained in the Bike.ipynb Jupyter Notebook file. The notebook contains the following sections:

1. Data cleaning and preprocessing: This section involves cleaning the data and converting it into a format suitable for analysis. This includes handling missing values, transforming data types, and creating new columns.

2. Exploratory data analysis: This section involves exploring the data to gain insights into the sales patterns, customer demographics, and product categories. This includes visualizations and statistical analysis.

The following Python libraries are required to run the analysis:

• Pandas
• Numpy
• Matplotlib.pyplot

This analysis provides valuable insights into the sales patterns and customer demographics of a bike store. The results can be used to improve marketing strategies, optimize inventory management, and increase profitability.