| dc.contributor.author | Shruti Kapoor, 20SCSE1290083 | |
| dc.contributor.author | Arnab Srivastava, 20SCSE1290002 | |
| dc.date.accessioned | 2024-09-18T06:35:54Z | |
| dc.date.available | 2024-09-18T06:35:54Z | |
| dc.date.issued | 2024-04 | |
| dc.identifier.uri | http://10.10.11.6/handle/1/18101 | |
| dc.description | SCHOOL OF COMPUTING SCIENCE AND ENGINEERING DEPARTMENT
OF COMPUTER SCIENCE AND ENGINEERING
GALGOTIAS UNIVERSITY, GREATER NOIDA
INDIA | en_US |
| dc.description.abstract | The distribution and composition of populations vary geographically, which has an impact on corporate
development, government changes, urban development, and other areas. Even though these kinds of
statistics are widely applicable and significant, it can be difficult to obtain local census estimates in a
timely and accurate manner due to the dynamic nature of population counts, their political content, and
logistical and administrative difficulties. Given these difficulties, the main goal is to use data science
approaches to close the knowledge gap between the dynamic demographic picture and wise decision making | en_US |
| dc.language.iso | en_US | en_US |
| dc.publisher | Galgotias University | en_US |
| dc.subject | Data Science | en_US |
| dc.subject | Area and Population | en_US |
| dc.title | Data Science in Area and Population | en_US |
| dc.type | Technical Report | en_US |
