Abstract
In Many parts of the country the life of people in rural as well as in urban is quite miserable and below average. Government has initiated many reforms and schemes for the upliftment of the citizen of the country. One such scheme is Members of Parliament Local Area Development Scheme (MPLADS). Ministry of Statistic and Programme Implementation (MoSPI) is entrusted with the responsibility of implementation of MPLAD Scheme in the entire country. Under the scheme, each Member of Parliament of Lok Sabha and Rajya Sabha has the choice to suggest works, listed as per the guidelines of the scheme to the District Collector in their elected constituency or State. The motive behind this research is to analyze the selection of scheme in each sector by MP’s during 16th Lok Sabha tenure i.e. year (2014 to 2019) and priorities the work using Machine Language. Different Machine Learning (ML) Models were implemented to find the best Classification Models which can be used to find the high and low priority Sector/Subsectors implemented in state. It was further analyzed that allocation of funds in sectors is not equally distributed among all states. This study try to improvise the Work Management System (WMS) by forecasting the undermine sectors and schemes in each state so that uniform distribution of fund can take place in the entire sector which may optimize the benefit of the MPLAD scheme. For this purpose data of 16th Lok Sabha present on the MPLADS portal in public domain is used.
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Sharma, A., Paliwal, D. (2021). Fund Utilization Under Parliament Local Development Scheme: Machine Learning Base Approach. In: Singh, M., Tyagi, V., Gupta, P.K., Flusser, J., Ören, T., Sonawane, V.R. (eds) Advances in Computing and Data Sciences. ICACDS 2021. Communications in Computer and Information Science, vol 1440. Springer, Cham. https://doi.org/10.1007/978-3-030-81462-5_2
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