GyanDhan's Profile Evaluation Tool
Frequently Asked Questions
Definitely not. Unlike all the admit predictor out there, we go beyond Safe/Ambitious/Moderate by providing admit probability for each college.
Pretty reliable. Your profile is computed using insights from thousands of historical data points.
Several students contributed to this initiative by sharing their info through surveys. In total, we reached out to 15,000 applicants in the GyanDhan community. Many students gave us access to Google spreadsheets, where past students had voluntarily entered their admit-related information.
Our percentile chart shows how your rank amongst the past successful applicants. For eg: an 80% percentile for ASU MS in CS means that you're in the top 20% of all past admits to MS in CS @ ASU.
USNews shows average acceptance of all students globally. Most profile evaluators are not specialized for Indian students. We have computed acceptance ratio for Indian students from our data and from ASEE.
Different universities in India have different grading norms resulting in a wide variation in median scores. US universities are aware of this, and treat grades from a given Indian university relative to the topper's grade. Class Percentage is your relative grade: (your GPA)/(Your univ's topper's GPA). Note that topper's GPA may be computed from our historical data and not necessarily from the info you have provided.
In the US, different colleges have different criteria for admission. Some college gives more weightage to high grades, others to high GRE quant scores etc. If your scores are higher than the medians of past admits, then you're in good shape - just ace your SOP and you're in!
We tried different Machine learning models, after training our model and evaluating it on different test datasets, we concluded that a Hybrid Model (of Logistic regression and Random Forest) was outperforming the others.
As mentioned in our previous post about the importance of work experience, relevant work experience in the field you are applying increases your chances of getting admission. We have not been able to find reliable data on relevant work experience yet. We’re on the look-out and hope to add this feature to our model.
We could not find enough data to find a correlation between admission and research publication. We are still trying to gather data to include publication in our admit model.
LORs provided by recommenders is not shared with anyone (not even the student) except for university. Students who provided us their admit info were reluctant to share their SOPs. Further, any rating given by students to their own work is not objective.