An Improvised Learning Model with Multi-Layered Cnn for Customized Astrological Prediction System
Keywords:
Astrology, horoscope, charts, houses, zodiac, classification, multi-layered CNN, philosophies and planetsAbstract
Numerous applications in the real world require previous prediction and analyses so that crucial decisions can be made to optimise resources, money and time. The ultimate prediction method is astrology, which uses a person's date, time, and place of birth to create a birth chart and reveal the planets' positions. Applications based on astrology serve as a prime illustration of classification strategies. By creating machine learning-based categorization approaches in this work, we solve the issue with traditional astrological tools and forecast the likelihood of occurrence for significant life events like marriage and employment. Then, we put forth an approach that uses a multi-layered convolutional neural network model to recommend events, with an accuracy of greater than 95% and a time required for one epoch of about 1.3 seconds. The suggested approach uses WEKA for processing, which makes the data loaders run considerably more quickly. Consequently, the benchmark dataset used to construct this application was unavailable thus data are collected from diverse people as detailed in the data set generation section and the model was trained accordingly for accurate learning results. Following model training and user interaction validation, the user is prompted for his name, birth date, and sun sign type before receiving an accurate forecast of future occurrences.
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