Data Cleaning

Data cleaning is the process of preparing raw data so it can be used reliably for analysis and modeling. Real-world telecom datasets often contain missing values, inconsistent entries, incorrect data types, or formatting issues that can lead to inaccurate predictions if not handled properly.

In this project, data cleaning is essential because customer-related information such as TotalCharges, tenure, or service subscriptions may contain missing or improperly formatted values. Cleaning ensures that the dataset is consistent and suitable for analysis and machine learning.

Improving Data Quality: Removes inconsistencies that could distort churn predictions.

Preventing Modeling Errors: Ensures calculations and model training run without failure.

Preparing Data for Machine Learning: Structures the dataset properly before encoding and model building.

Data cleaning is always the first step before performing meaningful analysis or training a predictive model.