Predicting Customer Attrition With Machine Learning
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| Formato: | Recurso digital |
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2024
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| _version_ | 1866901261971881984 |
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| author | Sabapathy M S |
| author_facet | Sabapathy M S |
| contents | Customer churn prediction is a vital task in business analytics. It aims at identifying customers who most probably will leave or unsubscribe from a business service. This abstract provides an overview of churn prediction using decision tree utilizing customer's historic data, highlighting its methodology, benefits and implications for business. Decision trees are high- powered machine learning tools that enables the creation of intuitive models. The use of decision trees is to analyse historical customer data and predict which customers are at risk of churning. To build an accurate predictive model various features such as customer demographics, purchase history, customer support interactions, and other relevant data points are considered. The methodology involves data preprocessing, feature selection, and the development of a decision tree model. Techniques such as cross-validation and hyperparameter tuning are also employed to enhance the model's predictive performance. The resulting decision tree provides a transparent view of the factors that contribute to customer churn, making it valuable for business decision-maker |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18146064 |
| institution | Zenodo |
| language | |
| publishDate | 2024 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Predicting Customer Attrition With Machine Learning Sabapathy M S customer churn decision tree cross validation hyperparameter tuning Customer churn prediction is a vital task in business analytics. It aims at identifying customers who most probably will leave or unsubscribe from a business service. This abstract provides an overview of churn prediction using decision tree utilizing customer's historic data, highlighting its methodology, benefits and implications for business. Decision trees are high- powered machine learning tools that enables the creation of intuitive models. The use of decision trees is to analyse historical customer data and predict which customers are at risk of churning. To build an accurate predictive model various features such as customer demographics, purchase history, customer support interactions, and other relevant data points are considered. The methodology involves data preprocessing, feature selection, and the development of a decision tree model. Techniques such as cross-validation and hyperparameter tuning are also employed to enhance the model's predictive performance. The resulting decision tree provides a transparent view of the factors that contribute to customer churn, making it valuable for business decision-maker |
| title | Predicting Customer Attrition With Machine Learning |
| topic | customer churn decision tree cross validation hyperparameter tuning |
| url | https://doi.org/10.5281/zenodo.18146064 |