Sales & Demand Forecasting for Businesses

Project Overview

This project predicts product sales using machine learning techniques based on historical retail store data.

Dataset Features

• Date
• Store ID
• Product ID
• Category
• Region
• Inventory Level
• Units Ordered
• Price
• Discount
• Weather Condition
• Holiday/Promotion
• Competitor Pricing
• Seasonality

Target Variable

Units Sold

Model Used

Linear Regression

Evaluation Metrics

Mean Absolute Error (MAE): 7.41
R2 Score: 0.9937

Tools & Libraries

• Python
• Pandas
• Scikit-learn
• Matplotlib
• Jupyter Notebook / VS Code

Conclusion

The model successfully predicts product sales with high accuracy and can help businesses plan inventory and demand effectively.