1. Handling Missing Values:

There are several ways to handle missing values, depending on the nature of the data and the missing values themselves. Here are some common methods:

• Imputation: Use statistical methods like mean, median, or mode to fill in missing values.
• Deletion: Remove rows or columns with significant missing values, but only if the data loss is acceptable.
• Encoding: Create binary features indicating the presence or absence of missing values.

2. Divide and Train the Model:

Divide the “BankChurnDataset” into training and testing sets using methods like stratified sampling to ensure the datasets represent the overall population. Train a machine learning model like Logistic Regression, Decision Tree, or Random Forest on the training set. Evaluate the model’s performance on the testing set using metrics like accuracy, sensitivity, and specificity.

3. Predicting for New Customers:

Use the trained model to predict churn probability for each customer in the “NewCustomerDataset” based on their features. Analyze the predicted probabilities to identify potential churners and target them with retention strategies.

Remember, choosing the best approach depends on your specific data and goals. For more accurate predictions, consider implementing feature engineering techniques like scaling numerical features and handling categorical features appropriately.