Event Participation and Performance analysis Prediction
Keywords:
Cultural Event Analysis, Machine Learning, Student Participation, Performance Prediction, Event Data Analytics, Classification Models, Regression Models, Event Planning, Resource Optimisation, Engagement AnalysisAbstract
This study explores trends in participation and performance at PCTE Group of Institutes, Ludhiana, during the “Koshish 2024 Junior” cultural festival. By utilising machine learning methods, we assess student involvement in a variety of cultural activities to pinpoint the top-performing participants and comprehend the factors that influence student engagement. The research uses data gathered from the “Koshish 2024 Junior” festival, including participation logs from numerous events such as Solo Dance, Quiz, Debate, Photography, Rangoli, Group Dance, and others. We implement machine learning techniques, featuring classification models for performance evaluation and regression models for forecasting participation, to examine the connection between event type and overall performance. Additionally, this study investigates how machine learning methods can be utilised to gain insights into participation trends. Recognising these trends is essential for efficient event planning, resource management, and boosting engagement. We review historical participation data to uncover patterns and trends, applying predictive modelling to forecast performance levels. This methodology allows event organisers to make more informed decisions, aiding them in optimising schedules, allocating resources, and enhancing participant outreach strategies. Through this research, we seek to improve the accuracy of predictions related to cultural event participation, ultimately enriching the experience for both students and event organisers.
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