Predictive Analysis of Pet Care Health Using Data Analytics

With the growing number of pet owners worldwide, the need for effective and intelligent pet healthcare systems has increased significantly. This research proposes a predictive pet care health system that uses data analytics techniques to monitor and evaluate the health conditions of pets. The system considers factors such as age, breed, diet, activity level, and medical history to generate health insights. The proposed approach applies data analytics methods including descriptive, diagnostic, and predictive analysis. Machine learning algorithms such as Decision Tree, Random Forest, and Logistic Regression are used to identify patterns and predict possible health risks. Data preprocessing techniques like cleaning, normalization, and feature selection are applied to improve model performance. The system is designed to assist pet owners and veterinarians by providing early warnings and recommendations. However, it is intended as a supportive tool and does not replace professional veterinary advice. The main objective of this system is to provide early warnings and useful recommendations to pet owners so that they can take timely action. While the system is helpful for basic guidance, it is not meant to replace professional veterinary advice but rather to support it.