Evaluation of Instagram Performance Metrics on Technology and Innovation Hashtags

In today’s digital era, Instagram has emerged as a pivotal platform for businesses, influencers, and content creators to engage with audiences and amplify their reach. However, understanding the factors driving engagement and optimizing content strategies remains challenging due to dynamic algorithms and user behavior. This work addresses these challenges by leveraging machine learning techniques to analyze Instagram metrics and provide data-driven insights for enhancing engagement. A manually collected dataset comprising of latest new technology, and innovations in Instagram posts was utilized, comprising key metrics such as likes, comments, shares, hashtags, and impressions. The machine learning framework centered on the Passive Aggressive Regressor (PAR), a real-time adaptive model, to predict engagement metrics like reach. Additional experimentation was conducted with algorithms such as logistic regression and decision trees to validate the robustness of predictions. The analysis revealed significant correlations between various engagement metrics, such as a linear relationship between likes and reach, and weaker yet notable impacts of comments and shares. Visualizations provided actionable insights into content strategies, including the effectiveness of specific hashtags and caption styles. The Passive Aggressive Regressor demonstrated adaptability and accuracy, supported by evaluation metrics like R-squared and Mean Absolute Error. The model successfully predicted engagement metrics and offered strategic recommendations for content optimization.