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Is your feature request related to a problem? Please describe.
Survival prediction in horses, particularly after medical interventions or surgeries, can be challenging due to the variability in responses to treatment and underlying health conditions. Traditional methods rely on clinical evaluations and subjective judgement, which may lead to inconsistent prognoses. This project addresses the need for a data-driven approach to predict survival rates, utilizing machine learning models trained on historical data. This approach aims to improve prediction accuracy, allowing better allocation of resources and optimized treatment strategies.
Describe the solution you'd like
The "Horse Survival Prediction" project is a machine learning application that uses multiple top-performing algorithms to predict the survival outcomes of horses based on clinical and health-related data. By leveraging models such as Random Forest, Decision Tree, and others, this project aims to determine the likelihood of a horse's survival, assisting veterinarians and caretakers in making informed decisions about medical treatments and prognosis.