Churn Prediction using machine learning techniques such as Random Forest Classifier and Decision Tree Classifier, a model for predicting churn for telecommunication businesses is proposed. On the supplied dataset, a comparison of the algorithm's efficiency is made.
The dataset is an IBM Sample Data Sets that Kaggle created. This sample data module tracks the customer attrition of a hypothetical telco company based on a variety of parameters. The churn column shows if a customer left within the previous month. Gender, dependents, monthly charges, and several columns with information on the types of services each customer has are among the other columns.
The following stages are used to implement the proposed Classification data analysis model:
• Data collection
• Data preprocessing
• Model training using diverse Classifiers
• Results visualization