Authors - Chitraksh Madan Singh, Yash Kumar, Lakshya Gattani, A.Anilet Bala, Harisudha Kuresan Abstract - This study presents an analysis of Instagram reach using Passive Aggressive, Decision Tree, Random Forest, and Linear Regression models. The goal is to predict the impressions generated by posts based on features like likes, saves, comments, shares, profile visits, and follows. Using Instagram data, machine learning algorithms are applied to forecast the post reach, helping marketers optimize content strategies. Quantitative metrics such as Mean Squared Error (MSE) and R-squared (R2) are used to evaluate model performance, with Random Forest showing superior accuracy compared to other models.
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