Retail and Warehouse Sales Analysis Project Description
This project performs a detailed analysis of retail and warehouse sales data to uncover business insights, supplier performance, and seasonal sales trends. The analysis helps understand how different suppliers, item categories, and channels contribute to total revenue, providing actionable insights for business optimization.
Objectives
Perform data cleaning and exploratory analysis on retail sales data.
Visualize sales patterns and supplier performance.
Identify KPIs, top-performing categories, and growth opportunities.
Provide data-driven insights and recommendations for future sales strategies.
Key Features
Data Cleaning: Removed duplicates, handled missing values, and standardized column names.
Feature Engineering: Created total sales and time-based features.
Exploratory Data Analysis (EDA): Examined overall, supplier-wise, and category-wise sales.
Visualization: Highlighted key trends through charts and graphs.
Insights and Recommendations: Derived actionable strategies based on data trends.
Libraries Used
pandas
numpy
matplotlib
seaborn
Usage Instructions
Install required libraries pip install -r requirements.txt
Run the analysis python retail_sales_analysis.py
Review visualizations and insights generated in the console and graphs.
Key Outputs
Cleaned and structured retail data ready for visualization.
KPI calculations such as total sales, average monthly sales, and top supplier.
Visual insights on monthly trends, item category performance, and supplier rankings.
Actionable business recommendations for decision-making.
Dataset
Source: https://www.kaggle.com/datasets/abdullah0a/retail-sales-data-with-seasonal-trends-and-marketing
File Used: Retail and wherehouse Sale.csv
Attributes include:
Year and Month of sales
Supplier and Item details
Retail and Warehouse sales
Product category and transaction information
Visualizations
Key plots and charts generated include:
Monthly Sales Trends
Retail vs Warehouse Sales Comparison
Top 10 Suppliers by Total Sales
Sales by Item Type (Category)
You can save these charts in an "assets" folder and add them to your GitHub repository.
Contributions
Feel free to fork this repository and submit pull requests. All contributions that enhance analysis or visualizations are welcome!