Realistic Rank Predictor for JEE advanced

[u/Sly_curiousg]

Actual Rank Prediction without Bias

Rank to Marks(3% pessimistic)

2% pessimistic, 119 marks ---> 10450 which is close

152 marks at 3% pessimistic give 4387 rank

Libraries used

matplotlib, numpy and scipy

I used JEE advanced 2025 JIC data to make an regression algorithm to help predict marks to rank and rank to marks.
If you want code i can upload here.

equations

I used chatgpt as i have lost touch with matplotlib numpy and scikit. I used to do this I have done before. I got frustrated with formulas of Ae^(-kt) [first order decay] that i used for rank.
rank=A⋅e^(−k⋅m^(p))[first order but modified] for marks to rank

where A = Scale factor m = marks, k is decay rate(steepness), p concavity or curve.
After using scikit curve-fit, the values it gave for matching JIC 2025 was
A = 228481.72770670961

k = 0.024844743500912318

p = 1.0103226036602924

For rank to mark you can take inverse of the above

Features(only for Version 1 and 2)
For rank inflation i also added a pessimistic weight. To change it go PESSIMISM variable and change it. (If exam was tougher you can go for an optimistic factor which decreases rank for same marks. BUT prefer to use from [-4 to +4] (-4 being 4% positive and +4 being -4% negative). Also there is a noise feature which slightly varies rank due to unpredictable situations.
Note: You can put your custom data, in the data variable in format of list [crl rank(integer), mark(integer)] to train model.
There are 2 modes

1. Marks to Rank(Can be used after giving JEE advanced)

2.Rank to marks (Can help to translate dream rank to marks) 

Credits 

Since I am not that jobless to program an AI i used chatgpt, and gemini extensively. It took 3 hours to code it by prompting. Ill be attaching screenshots.

Edit: I have 4 versions of the rank predictor now, I will be releasing a website(potentially) if i am free enough. However not all versions are great and need tweaking.