Abstract: Standing in the early 21st century, the world has experienced various regression analysis such as Simple Linear regression, Multiple Linear regression, Logistic regression, Multiple Logistic regression etc. Multiple Logistic regression (MLR) or we can say multiple regression is one of them. A widely used statistical technique that allow predictions of systems with multiple explanatory(independent) variables.
In this paper, we collected the final year placement data of a university. Our main objective is to select the explanatory variables for predicting the placement results. Data that has been used in this research were taken from Kaggle website based on the college placements data compiled over 2 years.
Then the data will be analysed by using step by step multiple regression techniques. Here, we used train_test_split and 10_fold_cross_validation in our model.
Reference: - https://www.kaggle.com/tejashvi14/engineering-placements-prediction

Keywords: Multiple Logistic Regression, Placement predictor, Classification, Dataset, Machine Learning.

PDF | DOI: 10.17148/IJARCCE.2022.11337

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