National Park Trail Recommendation System

The National Park Recommendation System is a data project that I worked on with a team as part of my studies at Northeastern University. The goal of this project was to develop a recommendation system that could suggest National Park trails to users based on their preferences.

To achieve this, we first collected data on various National Park trails from AllTrails, a popular outdoor recreation app. We then used Python and machine learning techniques to cluster and classify the trails based on their features, such as trail difficulty, length, and elevation. We also imposed scalings on these features to influence the output of the system.

Finally, we developed an interactive Plotly interface using Python and object-oriented programming to allow users to input their preferences and receive personalized recommendations. This interface provides users with a visual representation of the recommended trails, along with relevant information such as trail ratings and reviews.

Skills: Python and Excel