To build a recommendation system that can recommend beers based on previous user's ratings from beeradvocate.com.

The craft beer scene has exploded in recent years with microbreweries and major beer suppliers now consistently making new kinds of beer on a daily basis. This has led to a lot of confusion when trying to pick out a beer that you like. And this can be difficult for a casual beer drinker or even for those that consider themselves beer connoisseurs. There are several apps and websites now solely dedicated to ranking and classifying beers. Whether the point is to help an individual remember what they liked and didn't like or to help others discover new beers it is worthwhile to explore the reviews given to try and predict new kinds of beers that an individual would like.

This project uses data scraped from beeradvocate.com using Beautiful Soup, where users create profiles and are able to rate any beer they try on a scale of 1 to 5. This site uses a rating system that allows for users to take multiple factors in to consideration during their rating process that everyone can relate.

The initial factor that we looked at were which users to collect data from. The website breaks reviews down in to five different sections: recent reviews, top reviews for the last year, top reviews of all time, most popular reviews, and a beer hall of fame reviews. We decided to take a small section of users from each category to try and catch a variety of different users in terms of types of beers, frequency, and variety that we could sample from.

Using Beautiful Soup we were able to scrape user's profiles and create a sparse matrix for 2,682 users that resulted in 32,917 different beers that were reviewed. This totalled 86,133 reviews amongst all users that were collected.

Next we created a beer matrix that included the name of the beer, ABV, brewery, and type of the beer.

Now that we had all of our data we had to normalize the results. We extracted all of the values from the sparse matrix and subtracted the average from each column from each of the populated values. From there we created a were able to run a Single Value Decomposition (SVD) using the scipy.sparse.linalg library. This applies a linear transformation using the sigma matrix as weights for the transformation.

Next we make our predictions from this weighted diagonal matrix. These predictions are rating recommendations for every user against every beer that the user has already reviewed. We then applied this model against the entire data set to allow us to predict new ratings that users have not yet rated. From this we built a function to suggest top recommendations for each user using the weighted predictions and from the original user ratings.

If you wanted suggestions given a particular beer that you like, we created a function that would return the beer's highest rating user's top recommendations.

After collecting all of this data we cross validated using the surprise.model_selection library and including models SVD, KNNBaseline, KNNBasic, KNNWithMeans, KNNWithZScore, BaselineOnly, and CoClustering. We found that the SVD that we ran had a comparitively acceptable lowest RMSE!

Additionally we found which type of beer received the most reviews,

the brewery with the most reviews,

which ABV of beer was most frequently reviewed,

and the spread of the most reivewd beer types across the most rated breweries.