Meal Prep Times

Requirements Clarification & Assessment

1. Objective:

   • Develop a predictive model to estimate food preparation times at restaurants.
   • Ensure predictions are accurate enough to optimize the delivery process and enhance customer satisfaction.

2. Assumptions:

   • The problem is framed as a regression problem since the target variable (preparation time) is continuous.
   • The model will be evaluated based on both bias and variance to ensure optimal performance.

3. Data Requirements:

   • Historical data on order times, preparation times, and pickup times.
   • Restaurant-specific features such as staff workload, peak hours, and menu complexity.
   • External factors such as weather conditions or special events that could impact preparation times.

4. Stakeholders:

   • Customers: Expect timely delivery and hot food.
   • Delivery Drivers: Prefer minimal wait times and efficient routes.
   • Restaurants: Aim to streamline operations and reduce idle time.

5. Constraints:

   • Real-time prediction capability to support live order processing.
   • Scalability to handle varying volumes of orders across multiple locations.