Enhancing Non-Profit Project Outcomes through Predictive Modelling and Real-Time Data Analysis

Aims: To discuss the applications of predictive modelling and real-time data analysis to non-profit project outcomes. The procedures and relevant algorithms are to be examined together with relevant case studies. Future research gaps that should be bridged are also discussed. Problem Statement: Numerous advantages attached to project outcomes have been identified. However, there are series of steps and procedures that are supposed to be taken which are limited with the application of computer programs to enhance their effectiveness.  Significance of Study: The use of advanced predictive modeling and real-time data analysis are required to enhance non-profit project outcomes which entails using data analysis techniques and sophisticated algorithms. The use of these advanced techniques can lead to better resource allocation, improved decision-making, and enhanced overall project success rates in the non-profit sector. Methodology: The method used in writing this review involved consultation of recent literatures in the area of non-profit project outcomes and applications of predictive modelling and real-time data analysis. Discussion: The use of predictive modeling techniques in project outcomes is necessary in order to improve their efficiencies. The article addresses different predictive modeling approaches, such as decision trees and statistical models, and how to use them to improve project results in the fields of health and education. Predictive real-time data analytics models and models for designing predictive analytics algorithms in enhancing non-profit project outcomes were discussed to include statistical models, linear regression models, multiple regression models, multivariate regression model and decision tree. Health and education were used as case studies of predictive modelling and real-time data analysis application in non-profit project outcomes. This literature review article revealed some gaps that are needed to be bridged in order to improve the efficiency of predictive modelling and real-time data analysis an enhancers to non-profit project outcomes. Conclusion: Predictive real-time data analytics and algorithms are crucial instruments for improving the results of non-profit projects. When used in case studies, they exhibited notable outcomes.