: Building decision trees and ensemble models like Random Forests to classify new data points based on learned patterns.
: Using R to determine if the differences between proportions are statistically meaningful. 3. Predictive Modeling and Regression linkedin r essential training part 2: modeling data
: Identifying relationships between variables to see which factors move together. : Building decision trees and ensemble models like
: Using k-means and hierarchical clustering to group similar cases together. linkedin r essential training part 2: modeling data
Build a predictive model to identify users at risk of churning within 30 days. Then, provide a short memo explaining which three features most strongly predict churn and a recommended intervention.