Handling Missing Values

Handling missing values is a fundamental step in data preparation. In telecom churn datasets, missing values may occur due to system errors, incomplete customer records, or data entry issues. If ignored, these gaps can lead to biased predictions or model errors.

Before training the model, it is necessary to identify and address missing values appropriately.

Why Missing Values Matter

Missing values can distort statistical summaries and affect feature distributions.
They may cause errors during model training, especially in algorithms that do not handle NaN values.
They reduce the reliability and generalization capability of predictive models.

Common Approaches Used in This Project

Converting incorrect data types (e.g., TotalCharges) to numeric format.
Replacing missing numeric values with appropriate substitutes.
Ensuring categorical features are properly formatted before encoding.

Proper handling of missing values ensures the dataset remains reliable and ready for machine learning.