Customer Churn Analysis

[MrNICEForBonusWinner]

New Script Sample: Customer Churn Analysis

This Pull Request introduces a new Python-based script sample for Customer Churn Analysis.

Purpose and Value:

• This sample provides a predictive model to identify customers at high risk of churning (leaving the service/company).
• Its value lies in enabling businesses to proactively implement retention strategies, thereby reducing customer acquisition costs and improving overall customer lifetime value.

What it Does:

• It performs data loading, exploratory data analysis, feature engineering, and builds a machine learning model to predict churn.
• The primary output is a predictive model that flags high-risk customers based on their historical data.

How to Use / Prerequisites:

• The main component is a Jupyter Notebook (churning.ipynb).
• Requires Python (e.g., 3.x) and standard data science libraries such as Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn, and XGBoost.
• Users can run the notebook step-by-step to understand the data preparation, model training, and evaluation process.

Files Included:

• customer-churn/churning.ipynb: The main Jupyter Notebook for the analysis.
• customer-churn/WA_Fn-UseC_-Telco-Customer-Churn.csv: The dataset used for the analysis.
• customer-churn/README.md: A dedicated README file for this specific sample, providing more detailed information.