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Prediction Model: Polynomial Regression
About GMS Prediction
What is GMS (Gross mean salary) ?
While "GMS" is not an official NIRF parameter acronym, it is likely used here to refer to a metric related to college outcomes influenced by factors like median salary. Median salary is a significant indicator within the Graduation Outcomes (GO) parameter in the NIRF ranking, reflecting the success of graduates in securing well-paying jobs.
This predictor aims to estimate a "value" or "score" associated with a college's performance, primarily driven by the median salary of its graduates.
About the Prediction Model
Statistical modeling helps us understand and predict relationships between different data points. Polynomial Regression is a method that can be used to model the relationship between an input variable (like median salary) and an output variable (like a performance score) by fitting a curved line to the data. This allows it to capture non-linear patterns that a simple straight line model (Linear Regression) might miss.
This GMS predictor utilizes a Polynomial Regression model, specifically trained on a dataset containing median salary information and corresponding scores, to estimate the likely score based on the median salary you input.
Note: This is a predictive tool based on a specific model and data. Actual scores or outcomes can vary.