This project develops a cryptocurrency recommendation engine (https://www.coinrex.org), which recommends cryptocurrency projects for users based on what they want to invest in.

Data for the recommendation engine was acquired online from the following sources:

• Cryptocompare
• CoinMarketCap
• Kraken
• CER
• Cryptowisser

In addition, recommendations provided a link to project whitepapers (if readily available), which were sourced from:

• All Crypto White Papers

The data processing and analysis steps of the pipeline are described more in-depth on the "How it works" section of the website, but here is an outline of the process that indicates the files used in each step.

• Data Acquisition: Relevant information from these sources was gathered via APIs and scraping using functions in the acquisition.py file, and is stored in intermediate files in the coin_data folder.
  
  
• Data Processing and Analysis: Data from each source was processed by functions in the feature_matrix.py file. When this involves non-trivial processing of text (i.e., project descriptions), functions in text_analysis.py are also used.
  
  
• Feature Matrix Construction: The feature matrix was constructed from processed data using the build_feature_matrix() function and stored as a JSON file. Relevant information included with each recommendation (i.e., project links and whitepapers) is also stored in a JSON created by the create_recommendation_json() function. Both of these functions are also in the feature_matrix.py file.
  
  
• Finding Recommendations: The recommendation engine may be tested using the find_recs() function in the recommendations.py file. For each recommendation, the relevant information to provide to the user may also be accessed with the read_recommendation_json() function.
  
  
• Visualization: Data visualization helped inform the other steps of the pipeline, and some examples are included on the website. The code for this is included in the visualizations.py file.