Abstract :

Government Welfare Programs Are Created To Support People From Different Sections Of Society. However, Many Individuals Find It Difficult To Identify Which Scheme Is Most Suitable For Them Due To The Lack Of Proper Guidance Systems. To Address This Issue, This Project Presents A Web-based Application That Uses Demographic Information Along With Basic Machine Learning Techniques To Suggest Relevant Financial Schemes. The System Works With District-level Population Data And Applies The K-Means Clustering Algorithm To Group People Into Categories Such As Youth, Women, Children, And Senior Citizens.Based On These Groupings, The Application Provides Scheme Recommendations Through A Simple And Easy-to-use Interface. It Also Checks Whether A User Is Eligible By Using Inputs Like Age And Gender, So That The Suggestions Are More Relevant. In Cases Where Users Are Not Eligible For Certain Schemes, A Complaint Module Is Provided To Allow Them To Raise Requests Or Seek Support.In This Project, Django Is Used For Backend Development, While MySQL Is Used To Store And Manage The Data. Visualization Tools Are Included To Better Understand Population Distribution And Patterns. The Results Show That This Approach Helps Users Find Suitable Schemes More Easily And Reduces The Effort Required To Search Manually.The System Can Be Further Improved By Adding Updated Datasets And Better Models In The Future. Overall, This Application Makes It Easier For Users To Access Information About Government Schemes And Helps Connect People With The Benefits They Are Eligible For In A More Organized Way.

Published:

16-6-2026

Issue:

Vol. 26 No. 6 (2026)

Page Nos:

1113-1119

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to Cite