Problem: Retailers face overstock or stockouts due to poor demand forecasting.
Outcome: Develop an AI-driven model to optimize inventory levels based on sales predictions.
Week 1-2: Data Collection & Cleaning
Gather past sales, supplier, and seasonal data.
Handle missing values & format datasets.
Week 3-4: Exploratory Data Analysis (EDA)
Analyze sales trends & seasonal effects.
Identify inventory inefficiencies.
Train ML models (ARIMA, LSTMs, Prophet).
Compare forecasting accuracy.
Implement demand-supply balancing strategies.
Integrate reinforcement learning for adaptive inventory control.
Visualize stock levels & demand trends.
Develop alert systems for low stock levels.
Week 11-12: Final Report & Business Strategy
Document AI-based inventory strategies.
Provide insights for warehouse & procurement teams.
