Machine Learning Modeling for Predicting Child Mortality (Record no. 16570)

MARC details
000 -LEADER
fixed length control field 01257nam a22001697a 4500
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.3
Item number S531M
100 ## - MAIN ENTRY--AUTHOR NAME
Personal name Sharma, Nikhil
245 ## - TITLE STATEMENT
Title Machine Learning Modeling for Predicting Child Mortality
Statement of responsibility, etc by Nikhil Sharma
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication IIT Jodhpur
Name of publisher Department of Computer Science and Technology
Year of publication 2023
300 ## - PHYSICAL DESCRIPTION
Number of Pages vii, 15p.
Other physical details HB
500 ## - GENERAL NOTE
General note Child mortality is a very acute problem faced by mankind from time immemorial, and demographers have used traditional methods to derive inferences. However, with advances in computing and thereby in the machine learning space, new avenues have been opened to determine the various causal factors at a faster pace. This project is another step in that direction by employing machine learning models to traditional sampled data based out of India and including the socio-economic and medical variables together.<br/><br/>The objective is that the generated insights can provide input to policymakers to determine the best ways to formulate socio-welfare schemes and thereby reduce the child mortality count in India.
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical Term Department of Computer Science and Technology
Topical Term Child Mortality
Topical Term MTech Theses
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Brahma, Dweepobotee
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Thesis
Holdings
Withdrawn status Lost status Damaged status Not for loan Permanent Location Current Location Shelving location Date acquired Source of acquisition Full call number Accession Number Price effective from Koha item type
        S. R. Ranganathan Learning Hub S. R. Ranganathan Learning Hub Reference 2024-06-28 Office of Academics 006.3 S531M TM00505 2024-06-28 Thesis