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.
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Descriptive Statistics: Utilized statistical methods (
data.describe()
anddata.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.
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Highest Profit Year: Identified the most profitable year and examined the corresponding category and sales data.
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Category-wise Profit: Explored and visualized profit distribution across Technology, Office Supplies, and Furniture categories.
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State-wise Profit: Investigated profits across different states and visualized total losses.
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Highest Profit Customer: Identified the customer with the highest profit, including details such as state, category, and sales.
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State-wise Loss Analysis: Explored losses in Maharashtra and identified the category with the highest loss.
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Discount Analysis: Examined average discounts by sub-category and explored the correlation between discount, quantity, and profit.
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Monthly Sales Trends: Visualized monthly sales trends in 2015 and explored segment-wise sales distribution.
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Monthly Sales Time Series: Analyzed the time series of monthly sales.
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State-wise Sales and Profit: Visualized total sales and profit by state, offering a clear understanding of regional performance.
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Correlation Matrix: Explored correlations between numerical columns, providing insights into relationships.
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Profit Margin Analysis: Investigated the average profit margin by category, enhancing understanding of profitability.
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Discount vs. Profit: Analyzed the impact of discounts on profit and quantity sold.
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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.