Utilizing machine learning is a complex process that requires time, expertise, and continuous refinement. The term “machine learning” itself implies that the model must learn and be trained.
NB: This is an article from Hotellistat, one of our Expert Partners
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This process doesn’t just depend on time – it also requires that the instructors guiding the model are experts in their field, capable of monitoring the learning process and adapting the “curriculum” as needed.
Let’s take a deeper dive into our previous discussion about machine learning by talking about the most critical aspect of our work: developing the machine learning pipeline, starting from pure raw data, to predictions and recommendations generated hourly on our system.
If you have missed the first part, where we explain all the differences between AI and machine learning, and how it can add value to your day-to-day hotel management, then please check it here.
If you are also interested in AI’s usage in the industry and exploring its potential in various sectors (improving planning, efficiency, and guest comfort), then check out our other blog post series by Emilia here.
Step 1: Data Collection & Cleaning
Every great machine-learning model begins with one essential ingredient: high-quality data. In the current fast-developing world of AI, data isn’t just fuel, it’s the foundation. Even the most advanced algorithms and machine learning teams can’t perform well if the data they’re fed is incomplete, inconsistent, or irrelevant.
This is why I consider the first step of our pipeline, data collection and cleaning, the most critical part that sets the tone for the entire pipeline. Our preparation begins by gathering a comprehensive set of data that provides us with the most knowledge about a hotel and how it behaves in the market. This includes:
- PMS Information (Occupancy, Revenue, ADR, Cancellations, Arrivals, and even more KPI’s)
- Scraped prices, rates, and availability from several OTAs for the hotel, their competitors, and the market
- Reviews and search trends
- Weather reports
- Events and holidays
From each one of these sources, we extract key insights, known as features, which are then used to power customized and personal machine-learning models for each hotel. After the collection and scraping phase is done, the raw data goes through a specific preparation pipeline before usage:
- Cleansing: Removing any noise or irrelevant information from the data that could be wrongly handled to ensure we are only using data of high quality and consistency.
- Interpolation: Cleansing usually leads to a lot of gaps in the data that need to be filled; for this, we reconstruct the missing information using the already existing data that we have to ensure a smooth transition.
- Feature Engineering: Once we have our data cleaned and ready, we extract new features from already existing ones using revenue management knowledge to add more context to the model.
- Transformation: Once we have all our data ready, we modify it further to convert it into a state/encoding that the machine learning model can easily understand.
- Storage: The final stage of our data preparation, where data is safely stored in a secure place for easy access and future use. It is then later picked up for further analysis and integration into the pipeline.
