Analysis:

Demand Prediction Algorithm Analysis:

$$ Conducted an in$-$depth analysis of existing demand prediction algorithms in the literature.

$$ Identified key parameters and data features for effective demand forecasting.

$$ Explored machine learning models suitable for analyzing historical booking data.

IoT Device Analysis:

$$ Evaluated various IoT devices for integration into the reservation system.

$$ Considered factors such as compatibility, ease of implementation, and cost$-$effectiveness.

$$ Conducted feasibility studies for the integration of smart door locks, motion sensors etc.

$9.$ Design:

AI Algorithm Design:

$$ Designed machine learning algorithms tailored for demand prediction.

$$ Defined input parameters, training datasets, and validation processes.

$$ Established a framework for continuous learning to adapt to evolving patterns.

IoT Integration Design:

$$ Developed a comprehensive design for the integration of IoT devices into the reservation system.

$10.$ Initial Implementations:

Demand Prediction Algorithm:

$$ Implemented initial versions of the demand prediction algorithm using Python and relevant machine learning libraries.

$$ Conducted preliminary testing on historical booking data to validate algorithm performance.

$$ Iteratively refined the algorithm based on testing results.