This project predicts customer churn in a bank using Principal Component Analysis (PCA). This can be helpful for churn prediction, as it can help to identify the most important features that contribute to customer churn.
The problem statement for this project is to build a machine learning model to predict customer churn in a bank. The model should be able to make accurate predictions based on customer demographics, account information, and product usage.
The solution approach for this project is to use PCA to reduce the dimensionality of the bank data, and then build a machine learning model to predict customer churn. The PCA step will help to identify the most important features that contribute to customer churn, and the machine learning model will use these features to make predictions.
The observations from this project are that the most important features for churn prediction are related to customer demographics, account information, and product usage. These features can be used to identify customers who are at risk of churning, and targeted interventions can be used to prevent these customers from churning.
The findings of this project are that PCA can be a helpful tool for churn prediction. The PCA step helped to identify the most important features that contribute to customer churn, and the machine learning model was able to make accurate predictions based on these features.
The insights from this project are that PCA can be a valuable tool for churn prediction. By identifying the most important features that contribute to customer churn, PCA can help to improve the accuracy of churn prediction models.