Churn Prediction Model Using EDA

When a customer switches from one telecom service provider to another, it reduces the company's earnings, which makes customer churn a problem for most businesses. We use two main strategies to address this issue: first is determining the primary factors that are responsible for customer churn and the second is evaluating which individuals are most inclined to depart. For the purpose of learning insights from data and forecast the customer churn rate, the customer churn prediction model uses Exploratory Data Analysis (EDA) to analyse historical data from the telecom industry. Nevertheless, in this model, we will use the data of the customers who have already churned to generate the training dataset of the model. Formerly used Customer Churn Prediction models employed data based on the feedback provided by users or churned customers. Future forecasts will also make advantage of this data. It will offer better data from which to create a prediction model.