This repository contains a Jupyter Notebook file named Customer Churn.ipynb, which is dedicated to the prediction of customer churn. Additionally, the dataset data.csv is provided within the repository for convenience. The notebook file entails comprehensive code and explanations detailing the process of customer churn prediction.
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Customer Churn.ipynb: This Jupyter Notebook file contains the code for predicting customer churn. It provides a detailed explanation of the thought process behind the code, including data preprocessing, model selection, and evaluation metrics.
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data.csv: This dataset is utilized for customer churn prediction. It includes relevant features and target variables necessary for training and testing machine learning models.
To utilize the customer churn prediction model:
Open the Customer Churn.ipynb file in a Jupyter Notebook environment. Execute the code cells sequentially to understand the data preprocessing, model training, and evaluation steps.
Jeet Gupta: [email protected]
If you encounter any issues with the code or have suggestions for improvement, please feel free to open an issue or submit a pull request. Contributions are highly welcomed and appreciated.