Supervised machine learning model for determining staffing levels

At least one non-transitory machine-readable medium including instructions, which when executed by processing circuitry, cause the processing circuitry to perform operations comprising:

• generating a training dataset including input data corresponding to sales data at a store over a time period, the training dataset labeled with a corresponding number of cashiers working at front-end lanes at respective time increments in the time period;
• removing outliers from the training dataset using an outlier detection model to generate a clean labeled training dataset;
• training a supervised regression machine learning model using the clean labeled training dataset;
• generating an inference dataset;
• predicting, using the supervised regression machine learning model, an expected number of cashiers for a subset of data from the inference dataset corresponding to a particular time;
• comparing an actual number of cashiers at the particular time to the expected number of cashiers;
• and
• outputting an indication of whether the store was overstaffed, understaffed, or adequately staffed based on a result of comparing the actual number of cashiers at the particular time to the expected number of cashiers.

A system comprising:

• processing circuitry;
• and
• memory, including instructions, which when executed by the processing circuitry, cause the processing circuitry to perform operations comprising:
• generating a training dataset including input data corresponding to sales data at a store over a time period, the training dataset labeled with a corresponding number of cashiers working at front-end lanes at respective time increments in the time period;
• removing outliers from the training dataset using an outlier detection model to generate a clean labeled training dataset;
• training a supervised regression machine learning model using the clean labeled training dataset;
• generating an inference dataset;
• predicting, using the supervised regression machine learning model, an expected number of cashiers for a subset of data from the inference dataset corresponding to a particular time;
• comparing an actual number of cashiers at the particular time to the expected number of cashiers;
• and
• outputting an indication of whether the store was overstaffed, understaffed, or adequately staffed based on a result of comparing the actual number of cashiers at the particular time to the expected number of cashiers.