Profile Evaluation Tool
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Historical Data Points
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Frequently Asked Questions
Is this another admit predictor on the market?
Definitely not. Unlike all the admit predictor out there, we go beyond Safe/Ambitious/Moderate by providing admit probability for each college.
How reliable are the admit probabilities for each college?
Pretty reliable. Your profile is computed using insights from thousands of historical data points.
Every model depends upon the quality of data used. What can you tell about your data collection?
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.
What is the interpretation of percentile chart you are showing?
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.
Why does your average acceptance differ from USNews and other websites?
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.
What is class percentage?
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.
How do I interpret metrics such as Median Class percentage, Median GRE Quant, and Median GRE Verbal? Why should I be concerned about them?
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!
Which Machine Learning model you are using to show me admit probabilities?
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.
I know for a fact, that work experience matters a lot. Why are you are not taking work experience into account?
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.
Why are you are not taking research publications into account?
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.
Other sites incorporate SOP and LOR and strength in their profile evaluators. Why are you not including them?
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.
I don't see my UG college included. What can I do?
Please let us know by mailing us at firstname.lastname@example.org, and we'll do our best to include your college as soon as possible.
What about other countries and courses?
We are working on expanding our admit predictor to other countries as well. Please register here to receive an update when that happens.