This project performs exploratory data analysis (EDA) on a superstore sales dataset using Python. It includes data loading, cleaning, visualization, and insights generation.
- Load sales data from an Excel file
- Perform data exploration (shape, info, statistics, unique values, and outlier detection)
- Visualize sales trends using Seaborn and Matplotlib
- Identify key factors influencing sales performance
- Python (Pandas, NumPy)
- Data Visualization (Matplotlib, Seaborn)
The dataset, superstore_sales.xlsx, contains transactional data, including order details, customer information, and product categories.
-What is the overall sales trend?
-Which are the Top 10 products by sales?
-Which are the Most Selling Products?
-Which is the most preferred Ship Mode?
-Which are the Most Profitable Category and Sub-Category?