Open AccessRETRACTED: Parameterized Comparison of Regularized Regression Models to Develop Models for Real Estate
Author(s) -
Ankur Chaturvedi,
Akshi Gupta,
Vikram Rajpoot
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1099/1/012016
Subject(s) - lasso (programming language) , computer science , logistic regression , normalization (sociology) , feature (linguistics) , elastic net regularization , parameterized complexity , regression , life expectancy , artificial intelligence , linear regression , machine learning , data mining , statistics , mathematics , feature selection , algorithm , population , linguistics , philosophy , demography , sociology , world wide web , anthropology
This research paper has been made on the data of life expectancy. Data carries two sort of regression tasks in it; one is continuous feature (Life expectancy) while another one is discrete feature (Status). Life expectancy depends on many a thing such as alcohol consumption, polio, infant deaths, etc. Generally, in data there exists two models separately, but research has been made to implement both at once. Research goes in a manner that it also involves the comparison or models accuracy among linear, ridge, and lasso. Visualization, normalization, data cleaning, feature reduction, etc, is also performed so as to increase the accuracy. One always looks for less time to complete task with less workout. Ultimately, research successfully implemented both linear regression and logistic regression both at once with optimized model. It is also stating the importance of the ridge and lasso algorithms for optimization.