🔍 Problem Description: Retail sales forecasting is a critical need for businesses to manage inventory effectively and optimize sales strategies. Accurate predictions allow retailers to stock appropriately, minimize waste, and enhance customer satisfaction. Without a reliable forecasting model, businesses may face stockouts or overstock situations, leading to lost sales or increased holding costs.

🧠 Model Description: I plan to implement a Retail Sales Forecasting model using time series analysis and machine learning techniques such as Linear Regression or LSTM (Long Short-Term Memory) networks. These models are suitable for capturing trends and seasonality in sales data. The model will take historical sales data, promotional activities, and external factors (e.g., holidays) as inputs to generate accurate future sales predictions.

⏲️ Estimated Time for Completion:

Model Development & Training: 1 weeks
Integration into Predictive Calc: 1 week
Total Time: Approximately 2 weeks
*Note: I will try to complete this within 1 week. So please assign me.

🎯 Expected Outcome: The expected outcome is a robust sales forecasting model that provides accurate predictions, enhancing inventory management and decision-making for retailers. This addition will improve the functionality of the Predictive Calc project and attract users from the retail sector.

📄 Additional Context: This model aligns with current market demands and leverages existing data to provide actionable insights. I am committed to thorough testing and documentation to ensure a smooth integration into the Predictive Calc framework.