Discover if you will have a love marriage or arrange marriage using the power of Vedic Astrology & AI/ML
Our Love Marriage Prediction service uses Vedic Astrology and AI/ML to predict if you will have a love or arranged marriage with an accuracy rate of 90%.
5 |
|
63% |
4 |
|
30% |
3 |
|
6% |
2 |
|
0% |
1 |
|
0% |
Prediction | |
---|---|
Accuracy | |
Readability |
Our Love Marriage Prediction service uses Vedic Astrology and AI/ML to predict if you will have a love or arranged marriage with an accuracy rate of 90%.
You will get answers to the following -
It can be quite a challenge to predict if your marriage will be based on love. The dynamics of romantic relationships and marriages are influenced by various unpredictable factors. We often invest significant time in a relationship, but sometimes, despite our efforts, it never materializes into marriage. Other times, we may reject a marriage proposal from our parents due to trivial reasons and end up marrying someone who is not a perfect life partner. The Love Marriage Prediction service aims to solve this problem using Vedic Astrology and AI/ML capability that the AstroNidan team has developed in the last 4-5 years.
Classical astrology books such as Brihat Parashara Hora Shastra, Uttara Kaalamrit, Bhavarth Ratnakar, Sarvarth Chintamani, Laghu Parashari, and Madhya Parashari provide various principles for predicting your marriage type. However, using the human mind to apply these principles can lead to many errors. This is because thousands of hypotheses and conditions are now mentioned in those books, which are sometimes very complex, tricky & error prone.
In AstroNidan, we have programmed thousands of logical statements in Python language and trained and validated them through multiple AI/ML models over 100,000 Kundalis for best profession prediction.
Some of the salient features are -
We have a team of Data Scientists, ML Scientists, Data Engineers, Sanskrit Scholars, Astrology Researchers, and Python Programmers engaged from top universities (IIT, IIM, JNU & BHU), who have worked tirelessly to build this product.
We have prepared this report after factoring in -
We have used semi-supervised learning methods to explore hidden patterns & combinations and evaluated numerous ML models such as Neural networks, SVM, Random Forest, and gradient Boosting algorithms. We shortlisted the best-performing ML models and fine-tuned them further through our robust feature selection process.