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UppuluriKalyani
SimranShaikh20
Customer churn prediction is a crucial aspect of business strategy, particularly in the telecom industry, where customer retention is key to maintaining revenue and competitiveness. By building a deep learning model to predict customer churn, telecom operators can identify high-risk customers and take proactive measures to retain them.
Why Customer Churn Prediction is Important
Customer churn can result in significant revenue loss for telecom operators Identifying high-risk customers allows operators to take targeted retention measures Deep learning models can accurately predict churn behavior based on customer data Building a Deep Learning Model for Customer Churn Prediction
Train a deep learning model using the training data, such as a neural network or gradient boosting model Evaluate the model's performance using metrics such as precision, recall, and F1-score Evaluating Model Performance.
Precision: measures the proportion of true positives (correctly predicted churners) among all predicted churners Recall: measures the proportion of true positives among all actual churners F1-score: measures the harmonic mean of precision and recall, providing a balanced evaluation of the model's performance
Features can be added in this project : data analysis , data cleaning, neural network
@UppuluriKalyani you liked my idea then assign me task so that i can work on it !