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Ruby CLI - scrapes Formula 1 information from the internet and presents it to the user, estimating next GP results through some basic machine learning.

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luiz-pereira/F1-Ruby-CLI-Project

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Objectives:

  • This application allows the user to capture information about each F1 driver, and present information about him in a friendly and fast manner.
  • Most information comes from several sites from wikipedia (F1 drivers list, F1 constructors list, profiles, 2018/2019 results).
  • classify active drivers on ranking to try to predict what will be the next race's results.

#CLI I've made separate classes for the CLI that will contain the dynamics of user interaction Steps:

  • Salutation
  • Scrape for drivers list
  • Scrape for teams list (will be used to get stats for drivers)
  • Scrape team profile #struggling to get the drivers << Managed to get the drivers>>
  • list of drivers or do you know the driver?
  • search for driver
  • scrape driver profile
  • if status is active, search for last results and get team stats to estimate position for next race
  • show card for driver

#environment file contain all requirements - I've used require_all to avoid going back to the file and inserting a new 'require' every time i create a new file.

classes:

  • interface - performs the interaction with the user and calls classes and methods -F1 driver - each driver is an instance of this class, that contains many attributes
  • F1 Team - each team is an instance of this class, that contains many attributes
  • ScraperWikiDriver - used to scrape information about each driver
  • ScraperWikiTeam - used to scrape information about each team
  • Predictor - does all the calculations to rank each active driver and present to the user

Gems:

-nokogiri -CLI -pry -require_all -I/O console -colorize

installations instructions: -fork and close the repository -run bundle install -ruby ./bin/run to tun the application

License: F1-Ruby-CLI/LICENSE

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Ruby CLI - scrapes Formula 1 information from the internet and presents it to the user, estimating next GP results through some basic machine learning.

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