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This is an EDA and Logistic regression to predict whether a customer will churn.

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Churn-data

The purpose of this project is to use the bank chun data set to conduct EDA and logistic regression in order to predict whether or not a customer will chun. To put it another way, I'm going to do out EDA and logistic regression using the data set that has been provided to me (which is the bank churn data set) in order to determine whether or not a client would leave the organization.

The process of collecting data

During data collection I began by importing all of the necessary libraries, such as Pandas, Seaborn, and NumPy, among others. This was the first step. The following is an example of the code that I used to import and read the data: data is read from the Churn Modelling.csv file using the pd.read csv function.

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This is an EDA and Logistic regression to predict whether a customer will churn.

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