This is python superstore sales data analysis project. The Python Superstore Sales project involved conducting exploratory analysis and visualization using Plotly Express to analyze sales data from a fictional superstore. Through thorough data exploration and preprocessing, key insights were uncovered regarding the sales performance of the superstore. Utilizing Plotly Express, a powerful Python library for visualization, various interactive and visually appealing charts were created, such as bar charts, line plots, and scatter plots. These visualizations effectively showcased sales patterns, identified top-selling product categories, high-performing regions, and customer segments with significant purchasing power. The project outcomes provide actionable insights for optimizing sales strategies and improving overall performance.
- When we find the sales by category we got to know that office supplies is the most sold category and furniture is the least sold.
- By sub category phones are the most sold and fasteners are the least sold.
- By segment analysis our highest profit was from consumer and least from home office.
- Our product return rate was 0.163 and return cost was 287.
- By category our highest revenue was from technology and least was from furniture.