Game Analytics is an area that involves the processing of video game data, in order to make a better game experience for the user. It also helps to check the patterns in players behaviour, making it easier to identify the target audience. Gathering player data helps game developers identify problems earlier and know why players left the game or kept playing. These players behavior usually follows a pattern, making them fit in different player profiles. Game analytics experts create and use models of player types, usually variants of Bartle s model, to help identify player profiles. These experts use clustering algorithms to separate players into different and identifiable groups, labeling each group with the profile type defined by the proposed model. The main goal of this project is to create a generic Unity plugin to help identify Player Profiles in games. This plugin uses a Python API, which deals with the game data stored in a MongoDB database, to cluster and label each match or level of the chosen game while the game is running. In this plugin, game developers can configure the number of player types they want to identify, the player labels, and even the algorithms they wish to use. This online clustering approach is not usual in game development. As far as we are aware, there is no software component in the game analytics literature with the same direction and features.