Verizon Wireless: Exploring customer churn data to uncover patterns, insights, and key questions.
Start by defining and training a classification model to predict the churn of customers of a telecommunication company. Based on the data analysis, and possibly, the feature importance analysis in the model, suggest strategies that can be used to retain the customers. Simulate your strategies by altering the data and confirm their effectiveness using the model.
The customer churn data is given in the file Telco-Customer-Churn.json. Each row represents a customer, each column contains customer’s attributes described on the column Metadata.
The data set includes information about:
Define, train and evaluate a predictive model that takes as the input the data provided. You may want to split the data into training, testing and validation sets, according to your discretion. Do not use external data for this project. You may use any algorithm of your choice or compare multiple models.
Make sure that the solution reflects your entire thought process - it is more important how the code is structured rather than the final metrics. You are expected to spend no more than 4 hours working on this project.