To investigate the factors contributing to customer churn in VI based on the Finance.
To apply various predictive analytics techniques to build a model for predicting customer churn.
To evaluate the effectiveness of the predictive model in identifying potential churners.
Conduct a literature review on customer churn in the finance industry.
Collect and analyze data on customer behavior and characteristics for VI based on the Finance.
Implement predictive analytics techniques such as logistic regression, decision trees, and neural networks to build the churn prediction model.
Evaluate the performance of the model using metrics such as accuracy, precision, recall, and F1 score.
Provide recommendations for VI based on the Finance on how to reduce customer churn based on the insights gained from the predictive analysis.
