click here to download project abstract of machine learning classification algorithms
At DataPro, we provide final year projects with source code in python for computer science students in Hyderabad , Visakhapatnam.
ABSTARCT
In the vibrant tapestry of the hospitality industry, cancellations loom as a challenging adversary, wielding the potential to wield a detrimental impact on both revenue streams and the esteemed reputation of hotels. These cancellations, often unpredictable and disruptive, have prompted hotel management to fortify their arsenal with various policies and, at times, risky overbooking strategies, each carrying the weight of potential financial and reputational damage.
The conundrum intensifies as these precautionary measures themselves might inadvertently exacerbate the problem they seek to address. Overbooking, for instance, risks compounding the issue by stretching resources thin and tarnishing the guest experience. The fine balance between accommodating guests and safeguarding against cancellations becomes a high-stakes tightrope walk.
Enter the beacon of hope in the form of machine learning (ML) models. These technological marvels harness the power of historical data from hotels, diving deep into past patterns to discern and predict the likelihood of a booking cancellation. The utilization of such predictive models becomes a game-changer, a strategic tool in the hands of hotel management. By empowering decision-makers with foresight into potential cancellations, these models arm them to make informed, proactive choices rather than reactive ones.
The ultimate goal transcends mere prediction; it’s about revolutionizing the approach to managing cancellations.
In essence, these ML models represent a pivotal paradigm shift, fostering a culture of proactive decision-making rooted in empirical data rather than haphazard guesswork. They serve as the harbinger of change, propelling the hospitality industry toward a future where cancellations are not just anticipated but actively managed and minimized, safeguarding both revenue streams and the esteemed reputation of hotels.
