@article{CRSIJ25000004,
                  author = {Hiren Prajapati},
                  title = {Predictive Analytics and Personalized Guidance using Celestial Objects: An AI-Driven Mobile Application for Astrological Insights},
                  journal = {Cosmo Research and Science International Journal},
                  year = {2025},
                  volume = {1},
                  number = {1},
                  pages = {30-38},
                  issn = {3108-1584},
                  url = {https://cosmorsij.com/published/CRSIJ25000004.pdf},
                  abstract = {This paper details the design, development, and initial evaluation of "MB App," an Android application engineered to deliver highly personalized astrological predictions. The application aims to bridge the historical practice of astrology with cutting-edge mobile technology, offering data-driven insights tailored to individual users based on their unique astrological profiles. The application was developed following an Agile iterative model, ensuring flexibility and continuous refinement. Its robust architecture is based on the MVVM (Model-View-ViewModel) pattern, with a Kotlin/XML frontend and a CodeIgniter/MySQL backend. This setup facilitates seamless data processing, rapid response times, and scalability. A central innovation is the application's "advanced algorithm," which processes comprehensive user-specific astrological data, including precise birth time, place, and planetary alignments, to generate a "percentage-based prediction model". This predictive analytics engine forms the foundation of its AI-driven personalization. The paper highlights unique features like "Cosmo Vibes" for quantifiable opportunities and threats, "Star Power" for favorable and unfavorable sentiments, and the "CV Planner" for categorizing predictions into actionable life dimensions. These features differentiate "MB App" from generalized horoscope applications by providing deep personalization and measurable insights. Initial feedback from beta users indicates strong reception, high user engagement, and perceived accuracy due to the personalized nature of the insights. This research demonstrates a novel application of predictive analytics in digital wellness, offering a new paradigm for personalized guidance in the mobile app market},
                  keywords = {Personalized Predictions, Artificial Intelligence, Predictive Analytics, Mobile Application, Digital Wellness, Human-Computer Interaction},
                  month = {July}
        }