THE INFLUENCER PRICING PROGNOSTICATION ON SOCIAL MEDIA DYNAMICS AN ADVANCED EXAMINATION OF LINEAR REGRESSION 2 POLY DEGREE ALGORITHM & NEURAL NETWORK AN ADVANCED EXAMINATION OF LINEAR REGRESSION 2 POLY DEGREE ALGORITHM & NEURAL NETWORK

Abstract

The pervasive influence of social media has spawned the influencer profession, a potent force shaping audience interest in promoted products and services. Unlike traditional media, the impact of influencer promotion is quantifiable, with rates typically determined by factors such as follower count, engagement, and reach. However, the absence of a standardized reference for rate determination poses a potential risk of losses for both influencers and clients. This study seeks to address this challenge through the development of an advanced machine learning-based deep learning predictive model, incorporating Linear Regression with a second-degree polynomial algorithm and a neural network to enhance accuracy. This research underscores the potential of machine learning, including advanced regression algorithms and neural networks, in providing a robust framework for predicting influencer rates. The developed model serves as a significant step toward minimizing adverse effects on both influencers and clients by offering a more nuanced and accurate reference for rate determination in the dynamic landscape of social media promotion The Model Evaluation based on Mean Absolute Error (MAE) metrics reveals that the Keras Neural Network outperformed both Simple Linear Regression (10.612) and Linear Regression with a 2nd-degree polynomial (10.089) in predicting influencer rates. With a substantially lower MAE of 7.952, the neural network demonstrated superior accuracy, leveraging its capacity to capture intricate data relationships and learn non-linear patterns. In conclusion, the Keras Neural Network emerges as the most effective model for influencer rate prediction.