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Design and implementation of customer churn prediction system

 Department: Computer Science  
 By: usericon olagok01  

 Project ID: 8473
   Rating:  (5.0) votes: 1
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   Price:₦4000
Abstract
Churn prediction is a critical task for businesses looking to retain customers and reduce the impact of customer churn on their bottom line. Machine learning algorithms such as SVM and Random Forest can be used to build predictive models that identify patterns and correlations in customer behavior, allowing businesses to take action to retain customers who are at risk of leaving. A typical churn prediction project involves several steps, including data collection, preprocessing, feature engineering, model selection, hyperparameter tuning, model evaluation, and deployment. The data must be preprocessed and engineered to extract more relevant features, after which the best machine learning model is selected, tuned and evaluated on a holdout dataset. Finally, the model is deployed in a production environment to identify at-risk customers and take action to reduce churn. Churn prediction can help businesses gain valuable insights into their customer base and take proactive steps to reduce churn and improve customer retention. By leveraging machine learning algorithms and advanced analytics techniques, businesses can identify patterns and correlations in customer behavior and take targeted actions to retain customers who are at risk of leaving. Overall, churn prediction is an essential tool for businesses looking to improve customer retention, reduce churn, and maximize revenue....
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