Leo’s Smart Shelf: The Magic of Data

Create a complete Smart Retail Demand Forecasting system using Machine Learning in the backend. The system should help retail stores predict product demand based on historical sales data, seasonal trends, promotions, and holidays. The goal is to help retailers manage inventory efficiently by avoiding overstocking and stockouts. Requirements Frontend UI Build a simple web interface where users can: Select a date Choose whether a promotion is active Choose whether the date is a holiday Click a button to predict demand Display the predicted demand clearly. Use HTML, CSS, and JavaScript for the UI. Backend Use Python with Flask to build the backend API. The backend should receive input from the frontend and send it to the machine learning model for prediction. Return the predicted demand in JSON format. Machine Learning Model Use Random Forest Regressor from scikit-learn to forecast product demand. Train the model using a dataset containing: Date Product name Sales quantity Promotion indicator (0 or 1) Holiday indicator (0 or 1) Data Processing Convert the date column into useful features such as: Day Month Weekday Use these features along with promotion and holiday indicators to train the model. Project Structure The project should follow this structure: RetailDemandForecast │ ├── data │ └── sales_data.csv │ ├── model │ └── train_model.py │ ├── backend │ └── app.py │ ├── frontend │ └── index.html │ └── model.pkl Functional Workflow Historical sales data is stored in a CSV file. A machine learning model is trained using this data. The trained model is saved as a .pkl file. The Flask backend loads the model. The frontend sends user input to the backend. The backend processes the input and predicts the demand. The prediction is returned and displayed in the UI. Additional Features (Optional but preferred) Display demand prediction using charts. Provide an inventory recommendation based on predicted demand. Make the UI visually appealing.