<![CDATA[Classifying cat breeds based on images presents challenges due to subtle differences in appearance among breeds and environmental influences. This study developed an automated classification system utilizing the Inception V4 architecture with a CRISP-DM approach, encompassing business understanding, data preparation, modeling, evaluation, and deployment. The dataset used was derived from the Oxford IIIT Pet Dataset, covering 12 popular cat breeds, and underwent cleaning, augmentation, normalization, and partitioning into training (80%) and validation (20%) datasets. The model was trained over 25 epochs, achieving a highest validation accuracy of 93.31% with average precision, recall, and f1-score of 93%. The system was implemented as a Flask-based web application, enabling real-time classification through image uploads. While overall performance was strong, certain breeds such as Bengal exhibited potential for further improvement. The findings demonstrate the model's significant potential to support pet health diagnosis and breed conservation efforts. This study contributes substantially to the development of image-based classification technology, with recommendations for performance enhancements through GAN-based data augmentation and testing on more diverse datasets to improve generalizability.]]>
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