Analyzing Online Food Sales Transactions 

This project involves exploratory data analysis (EDA) on a dataset of online sales transactions. The dataset includes detailed information on various sales transactions across different product categories, including order ID, date, category, product name, quantity sold, unit price, total price, region, and payment method. The objective is to extract meaningful insights that can inform business decisions, such as identifying sales trends, popular products, and the impact of different payment methods. 

Data Loading and Preparation:

• Imported libraries and loaded the dataset.

• Converted 'Date' column to datetime format.

Data Analysis:

• Sales Trends: Plotted total revenue over time.

• Category Popularity: Used count plots to examine sales distribution across regions.

• Payment Methods Impact: Analyzed distribution and revenue impact using count and box plots.

• Top-Selling Products: Identified top products by aggregating units sold.

• Regional Performance: Evaluated revenue performance by category across regions using bar plots.

Visualization:

• Created line, count, box, and bar plots for insights.

Sales Trends Over Time:

• Identified fluctuations and seasonal patterns in total revenue over time.

Category Popularity Across Regions:

• Revealed regional preferences for different product categories.

Impact of Payment Methods on Sales:

• Credit Cards and PayPal were the most used payment methods.

• Revenue variability observed based on payment methods.

Top-Selling Products:

• Identified top products in each category (e.g., Hanes ComfortSoft T-Shirt in Clothing).

Regional Performance:

• Highlighted which regions drive sales for specific product categories.

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Conclusion: The EDA provided actionable insights to optimize sales strategies, inventory management, and marketing efforts, ultimately enhancing business performance and profitability.