Paola Alean Datamex 0719
I love hiking. Personally, it's one of the ways to get to know a new country. So, what if we map all available hikes in Mexico and find out if there are routes that have desirable characteristics? Well, this recommender system will provide that first approach. It includes difficulty level, facilities for kids or even if you can do mountain bike. Additionally, it will recommend hikes based on trails you know you like.
I got all data from AllTrails.com. Using Selenium, I web scraped their list of the existing 322 hikes in Mexico. This included all of the hike meta data such as the users who reviewed the hike and the corresponding ratings. Once I cleaned the data by getting rid of the hikes that had no ratings or were missing data, I was left with 252 trails.
I addition, I turned some of the hike attributes into features. The features for each hike were: hike ID, hike Name, region, distance in kms, elevation gain in mts, difficulty level, global stars, number of reviews, user ID, user name, rating given by user, Route Type (loop, out and back, point to point), dog Friendly, kid Friendly, camping, near water, mountain biking, views, bird watching, climbing, forest, trail running and historic place.
Recommendation engines basically compute the similarities between two entities and on that basis, it gives the targeted output. For this project, I tried to find out the amount of similarity between users regarding their rating. In this implementation, I used the euclidean, cityblock and cosine distances.
However, calculating similarity scores based on the distances have an inherent problem. It is not certain how to decide how much distance between 'n' users could be considered close enough or far enough. This was resolved by try and error. As a result, the cosine distance provided the closest metric for the present model.
The hiking recommendation system in Mexico was implemented by just using pandas and a basic math library functions. In addition, it was a nice training to get to know the intuition behind recommendation engines. Naturally, there is a vast field to the recommendation engines than these aforementioned, and of course not just the ratings. In fact, the use of other features applied in this model helped to do a more accurate recommendation of the route.
Next steps, is the aplication of these tools to a web page.