Admissions-Predictions

This projecct describes the process whether the students are eligible for admission criteria based on the random data created with the help of python

Here in this code the admission criteria is set to be this :-

admission_prob = 1 / (1 + np.exp(-(0.1X1 + 0.2X2 - 10))) // this line indicates the selection process for the admission of the students

this is a small piece of code in python as an example to this formula

import numpy as np

Given values

X1 = 54.88135039 X2 = 59.28802708

Calculate linear combination

z = 0.1 * X1 + 0.2 * X2 - 10

Calculate admission probability

admission_prob = 1 / (1 + np.exp(-z))

print("Linear combination (z):", z) print("Admission probability:", admission_prob)

output to this code will result to

Linear combination (z): 7.345740455 Admission probability: 0.999357686

So, for this student with an entrance exam score of 54.88135039 and a percentage of 59.28802708, the calculated probability of admission is approximately 0.999354, or 99.94%.

This high probability indicates that the student is very likely to be admitted according to the logistic regression model.

likewise the auto generated dataset is used to predict with output of actual values and after model implementation values i.e predicted output.