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This is a recomendation system based on clustering analysis. After clustering a user_profiles.json file wil be generated keeping the users preferences. This system generates personalized recommendations, based on each user's preferences and other users preferences that belong in the same cluster.

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Content Overview

  • Analysis
  • Application

Kmeans-API

This is a recomendation system based on clustering analysis. After clustering a user_profiles.json file wil be generated keeping the users preferences. This system generates personalized recommendations, based on each user's preferences and other users preferences that belong in the same cluster.

  • More info about the application project can be found in the README.md files on Application folder.

Dataset - The data set is NOT included due to a confidentiality agreement

Root

  • time : A string representing the datetime when the bet was placed. The format is yyyy-MM-dd HH:mm:ss.fff expressed as the Universal Coordinated Time (UTC+0).
  • bet : An array of bet objects placed by the user. Note: In the current context, this array is expected to contain a single bet object.
  • userid : An integer representing the unique identifier of the user who placed the bet.

Bet

  • stake : A float representing the amount of money placed on the bet.
  • pick : An array of pick objects representing the user's selections for the bet. Note: In the current context, this array is expected to contain a single pick object.

Pick

  • eventType : A string representing the type of event the bet is placed on. In general, a value of "live" indicates a game that has started (i.e. after the dateofmatch ), "pregame" indicates a game that has not started yet (i.e. before the dateofmatch ).
  • match : An object containing detailed information about the match.
  • market : An object containing detailed information about the market. A market is a specific type of bet. For example, consider a soccer game, the betting markets for this event may be the final result of the match (win, draw, or lose -- 1x2 ), the total number of goals scored, or the player who will score the first goal.
  • oddField : An object containing detailed information about the odd field. An odd is a specific outcome for a related market (e.g. 1 in 1x2 ). Match
  • id : An integer representing the identifier of the match. dateofmatch : A string representing the date and time of the match in ISO 8601 format.
  • home : A string representing the home team's name.
  • homeId : An integer representing the identifier of the home team.
  • away : A string representing the away team's name.
  • awayId : An integer representing the identifier of the away team.
  • sport : A string representing the sport of the match.
  • category : A string representing the category of the match.
  • tournament : A string representing the tournament of the match.
  • sportId : An integer representing the identifier of the sport.
  • categoryId : An integer representing the identifier of the category.
  • tournamentId : An integer representing the identifier of the tournament.

Market

  • freetext : A string representing the free text (name + specialoddsvalue ) of the market.
  • specialoddsvalue : A string representing the special odds value of the market. This value indicates the specific variation of the market type. For example, in
  • Total a special value of {"total": "2.5"}" refers to whether the total number of goals scored will be less/more than 2.5. Null if not applicable. typeid : An integer representing the type identifier of the market.

OddField

  • oddTypeId : An integer representing the odd type identifier.

  • type : A string representing the type of the odd field. Following the above example, a type of over {total} would mean that you bet on more than 2.5 goals being scored in total.

  • value : A float representing the value of the odd field. This value correlates with the probability of this outcome.

About

This is a recomendation system based on clustering analysis. After clustering a user_profiles.json file wil be generated keeping the users preferences. This system generates personalized recommendations, based on each user's preferences and other users preferences that belong in the same cluster.

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