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Overview: Customer churn is the percentage of customers who stopped using a company's product or service within a certain time frame. In today's highly competitive business environment, retaining customers is crucial for the success of any company. A stable customer base is essential for a business to thrive, as acquiring new customers can be ten times more expensive than retaining existing ones. Thus, companies use churn rate as a metric to evaluate their performance, and reducing churn rate to close to 0% is always the goal.

Data analytics can help a company understand the reasons why customers leave and develop strategies to increase customer retention. In this analysis, we will use a dataset from Kaggle provided by a telecommunications company (Telcom) that predicts customer churn based on demographics, usage behavior, and accounts.

Data Description: The dataset includes 7,043 unique customers, each represented by a row of data with 21 columns. The dataset contains 19 features and 1 target feature (Churn), including both numeric and categorical features.

Usage: The Telcom Customer Churn Dataset can be used for various purposes, such as:

• Conduct an analysis of the customer churn rate to develop a strategy aimed at reducing this factor in the future.
• Compare the impact of each factor, easily come up with solutions
• Train machine learning models related to customer data.

Limitations: The leaving customer data set has a few limitations that should be considered before use:

• The data set may appear imbalanced affecting the analysis process.
• The data may not be completely accurate as it is collected from various sources and may contain errors or omissions.