Predicting Customer Churn with Random Forest

Using past customer data, a Random Forest model predicts customer turnover by separating out customers who are likely to depart (churn) from those who are likely to stay. During training, the Random Forest ensemble learning technique creates several decision trees, each of which is trained on a different subset of the data. The model generates a more precise and reliable prediction for every consumer by integrating the predictions from every tree. Gathering pertinent data, such as account age, usage trends, customer demographics, and previous support interactions, is one of the most important processes. The Random Forest model is trained to find patterns associated with churn following preprocessing, which includes managing missing values and encoding categorical characteristics.