E-COMMERCE CUSTOMER CHURN ANALYSIS USING HYPER PARAMETERED TREE ALGORITHM

In this study, we employ the AdaBoost algorithm in conjunction with GridSearchCV to analyze customer churn in e-commerce. AdaBoost is a boosting algorithm that combines multiple weak learners to create a strong predictive model. GridSearchCV is used to optimize the hyperparameters of the AdaBoost algorithm.To conduct the analysis, we utilize a dataset containing customer information, including demographics, purchase history, and engagement metrics. The dataset is divided into training and testing sets, with the training set used for model training and hyper parameter tuning.The GridSearchCV technique allows for an exhaustive search over a predefined set of hyper parameters for the AdaBoost algorithm. By systematically evaluating different combinations of hyper parameters, we aim to identify the optimal configuration that maximizes the performance of the model. Performance evaluation is conducted using various metrics, such as accuracy, precision, recall, and F1-score. These metrics provide insights into the ability of the model to accurately predict customer churn. Overall, this study aims to enhance the understanding of customer churn in e-commerce through the utilization of the AdaBoost algorithm with GridSearchCV. The findings and insights gained from this analysis can help e-commerce businesses identify potential churners and implement effective retention strategies.