An advanced project predicting Instagram post impressions using machine learning. Employing Multilinear Regression, Passive Aggressive Regression, and Random Forest, the model assesses features like likes, saves, comments, shares, profile visits, and follows.
We employed a diverse set of machine learning models in our analysis, each chosen for its specific strengths and capabilities. The models include Passive Aggressive Regression, known for its adaptability to changing data streams, Multi Linear Regression, which explores linear relationships among multiple features, and Random Forest, a versatile ensemble method. Each model underwent rigorous testing and tuning to ensure optimal performance in predicting Instagram impressions.
Passive Aggressive Regression excels in scenarios where data distribution may change over time, making it suitable for dynamic social media platforms like Instagram. Multi Linear Regression, on the other hand, provides insights into how different features collectively influence impression metrics. The Random Forest model, being an ensemble of decision trees, brings robustness and the ability to capture non-linear relationships within the data.
Our approach involves leveraging the strengths of each model to create a more comprehensive understanding of Instagram reach dynamics. By combining the predictive power of these models, we aim to assist content creators in making informed decisions to enhance the effectiveness of their Instagram content strategy
