100k Rf Facebook.xlsx Link

: Predicting personality or "Likes" using ensemble methods.

While the exact "deep paper" for that specific .xlsx file isn't publicly indexed, the following research areas represent the most likely "deep" academic context for such a dataset: 1. Facebook User Behavior & Prediction

: Unlike "black box" deep learning, RF allows for "feature importance" analysis, showing exactly which Facebook metrics (e.g., shares vs. comments) are the strongest predictors. 100K RF FACEBOOK.xlsx

: Optimizing Facebook ad campaigns using Random Forest for ROI prediction.

Knowing the origin will help in finding the specific "deep paper" or documentation you need. : Predicting personality or "Likes" using ensemble methods

: A "100K" dataset might contain performance metrics for 100,000 ad sets. The "RF" would refer to the Random Forest model used to determine which factors (bid price, creative, frequency) lead to the best conversion. 3. Fake News & Bot Detection

Papers in this category often use datasets of 100K+ users to predict psychological traits or engagement. comments) are the strongest predictors

: Identifying 100,000 instances of automated or malicious accounts.