Data-driven intelligence dating platform

Abstract

The study sought to investigate the difficulties associated with finding an ideal life partner through dating apps, particularly in the context of matching horoscopes, personal interests, and preferences. Recognizing that many dating platforms prioritize individual interests, the study highlighted the challenges posed by the confidentiality of personal data, which frequently complicates the matchmaking process. The objective of this study was to create a data-driven model that prioritized the integration of horoscope details alongside user preferences and interests while protecting users' personal information. This model attempted to recommend suitable partners by combining multiple predictive analyses based on these variables. The data collection methodology included both open-access sources and a standardized questionnaire, allowing for a comprehensive approach that incorporated multiple datasets into the model's training process. By combining personal preferences with astrological data, this innovative method aimed to transform the dating landscape by providing tailored recommendations while protecting user privacy. The research project culminated in a systematic investigation of how a datacentric approach could improve partner matching efficacy, filling significant gaps in existing dating apps that frequently overlook astrological compatibility. This abstract captured the essence of a research initiative aimed at developing an advanced predictive model to improve partner selection processes by combining personal interests and astrological insights, resulting in a more personalized and secure online dating experience.