Intelligent and Explainable Travel Accommodation Recommendation System Using Budget and Location Preferences

An intelligent and explainable travel accommodation recommendation system based on budget and location preferences.

An intelligent and explainable travel accommodation recommendation system based on budget and location preferences. This reference project walks through the whole job, from the problem and the design to the build and the evaluation, so that a developer can study how it works, run it and extend it.

It suits learners who want hands-on experience of the whole machine learning workflow: data collection and cleaning, feature engineering, model training and tuning, evaluation and presenting results.

The main parts are explanations of each result (feature importance or visual evidence), personalised recommendations ranked by relevance, data preparation and exploration, model training and comparison of algorithms, and evaluation with standard metrics. The reference build uses SHAP, Python, scikit-learn, Pandas, and Flask, with a clear project structure, setup instructions and sample data so that it can be run and extended.