Welcome to the Sales and Profit Analysis repository. This project focuses on a comprehensive analysis of sales and profit trends within a dataset (ACCS_Case_Study_Data.csv). Leveraging Python and popular data science libraries such as Pandas, Matplotlib, and Seaborn, we aim to provide valuable insights and actionable recommendations.

• Descriptive Statistics: Utilized statistical methods (data.describe() and data.info()) to gain a thorough understanding of the dataset.

• Data Cleaning: Addressed missing values, duplicates, and standardized data types for key columns, ensuring data integrity.

• Highest Profit Year: Identified the most profitable year and examined the corresponding category and sales data.

• Category-wise Profit: Explored and visualized profit distribution across Technology, Office Supplies, and Furniture categories.

• State-wise Profit: Investigated profits across different states and visualized total losses.

• Highest Profit Customer: Identified the customer with the highest profit, including details such as state, category, and sales.

• State-wise Loss Analysis: Explored losses in Maharashtra and identified the category with the highest loss.

• Discount Analysis: Examined average discounts by sub-category and explored the correlation between discount, quantity, and profit.

• Monthly Sales Trends: Visualized monthly sales trends in 2015 and explored segment-wise sales distribution.

• Monthly Sales Time Series: Analyzed the time series of monthly sales.

• State-wise Sales and Profit: Visualized total sales and profit by state, offering a clear understanding of regional performance.

• Correlation Matrix: Explored correlations between numerical columns, providing insights into relationships.

• Profit Margin Analysis: Investigated the average profit margin by category, enhancing understanding of profitability.

• Discount vs. Profit: Analyzed the impact of discounts on profit and quantity sold.

• Category-wise Discount vs. Profit: Visualized the relationship between discount, profit, and category.

• Top 10 Customers: Identified and visualized the top 10 customers based on total profit.