Marriage Compatibility Prediction

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Discover your perfect match with AI-ML powered Vedic astrology for accurate marriage compatibility predictions.
Boasts a 90-95% accuracy through deep learning based on 100K+ Kundali Matchings researched and validated by AstroNidan team.

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Discover your perfect match with AI-ML powered Vedic astrology for accurate marriage compatibility predictions.

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24.99 Purchase

Boasts a 90-95% accuracy through deep learning based on 100K+ Kundali Matchings researched and validated by AstroNidan team.

Last seven days summary

152
Total Orders
127
Total Customers
126
Positive Feedback
1
Negative Feedback

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Product Description

Details, features & methodology of the product

Boasts a 90-95% accuracy through deep learning based on 100K+ Kundali Matchings researched and validated by AstroNidan team.

Why should you buy this product?

You will get answers to the following -

  1. Should I go ahead with marriage with this person?
  2. How strong is my compatibility with him/her?
  3. What are the favorable and unfavorable findings in the Kundali Matching?
  4. Does the compatibility consider all factors like planets & houses etc.?
  5. What remedies should I perform before proceeding to marriage?
  6. Is this union compatible for my son or daughter?

Problem Background

The traditional Ashtkoota milan or Guna milan method, based on 36 points, has often been insufficient for accurately predicting the success or failure of a marriage. This approach has resulted in the rejection of many good matches and the acceptance of bad ones. Our marriage compatibility product addresses this issue. Thanks to advancements in AI and advanced data analytics, we have developed a holistic model that takes into account planetary positions, houses, doshas, Ashtkoota, and their exceptions to make decisions about Kundali Matches. Our system provides decisions at three levels: 'Good to Proceed,' 'Proceed with Remedies,' and 'Try to Avoid,' along with a score from 0 to 10 for easy comparison.

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 compatibility. However, applying these principles using the human mind can lead to a lot of errors. For the sake of simplicity, only the Ashtakoota Match has been very popular, which has in turn led to many errors in Kundali Matching.

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.

Salient Featurers

Some of the salient features are -

  1. Greater than 90% accuracy
  2. Validated over 100,000 Kundali Matchings
  3. Considers nine planets, twelve houses, doshas, and exceptions for Ashtkoota matchings
  4. Presents a visual representation of scores using a gauge chart and three simple categories
  5. Provides favorable and unfavorable points of Kundali Matching along with remedial measures
  6. Generates a report unique to your Kundali Matching details
  7. Demonstrates a high level of specificity and sensitivity
  8. Does not suffer from the problem of repeatability and reproducibility found in human astrologers

Approach/Methodology

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 -

  1. All permutations and combinations of Planet, Rashis & Nakshatras in D1 and D9 charts of both Kundalis
  2. Study of Ashtkoota matching in detail along with their exceptions
  3. Detailed study and analysis of Bhakoot, Nadi and Mangal doshas.
  4. Evaluation of normalized score based on clustering derived from deep learning models.

Application of AI-ML Technology

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.