PREDICTIVE ANALYTICS FOR ARTIST CAREER DEVELOPMENT - Hemalatha BS; Kanchi, Sucheta; Manhas, Rashmi; Saraswat, Vivek; Dhingra, Lovish; Deepthi S (ShodhKosh: Journal of Visual and Performing Arts, 2025)
The use of data analytics and artificial intelligence in tandem has led to changes in the talent management processes in the entertainment industry. By looking at measured signs of success, predictive analytics provides us with a data-driven method of understanding and predicting how an artist's work will go. This research suggests a complete way to guess how an artist's career will develop using many different types of data, such as action on social media, live numbers, patterns of cooperation and how involved the audience is. The study is based on entertainment data models that already exist and expands them by using advanced machine learning techniques. The method comprises gathering information in a pre-planned manner from a large number of people who practice their religion on public and digital platforms, including the number of followers, engagement metrics, streaming statistics, and genre preferences. These characteristics are inputted into prediction models such as regression analysis, decision trees, and ensemble learning algorithms. These models attempt to identify the factors that are most influential on an artist's development. Strong preparation, feature engineering and cross-validation methods are emphasised in the suggested framework to make sure model stability and reduce overfitting. A case study is carried out on a few artists of different styles to evaluate the applicability of the model to real life. Revisiting historical data provides us with a way to determine how well we are doing at predicting, and it also gives us insights into the management of artists and planning for the future.