Every year, the Hunters’ Association of Slovenia and the Slovenia Forest Service determine the annual cull. As the number of hunters is declining, a program has been developed to help them achieve the expected results. The program helps with their decision whether to go hunting on a specific day based on existing and predicted conditions. Data about the daily number of culls in individual hunting areas was combined with the data about time of the culls and additional information about hunting families, conditions and game. From a large set of attributes, a subset with the most important attributes is selected. On that subset, a decision-making model is made with the help of the most frequently used machine learning methods. Used methods are $k$-nearest neighbours, linear regression, support vector regression, artificial neural networks, and random forests. The most successful model was predicted with random forests which informs whether hunters should go hunting for the weekend. Attributes which best define the decision to go hunting proved to be wind strength and the presence of fog.
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