Home Artificial Intelligence Learn Your Churn, Do Not Guess

Learn Your Churn, Do Not Guess

Learn Your Churn, Do Not Guess

Are you feeling exhausted and disenchanted after a protracted day at work, spent tirelessly attempting to enhance the precision and effectiveness of your data-driven campaigns and churn prediction accuracy? We understand how you are feeling.

Despite some analytic teams believing that a rule-based marketing approach, using indexes and segmentation criteria, can successfully replace churn prediction models, this will not be the case. Clustering algorithms are not any longer effective, and here’s why:

Clumsy algorithms with low accuracy

It’s so yesterday

Nothing is clear anymore

Where the magic is hidden

A well-designed and trained predictive model, based on a high-quality dataset, can take your project to a latest level. The accuracy of a predictive model is determined by many aspects, and the principal one is the choice and quality of input variables or features. Features are the input variables that the model uses to make predictions, and their selection has a major impact on the accuracy of the model.

Feature engineering fastidiously selects variables and preprocesses them to enhance the accuracy of the model. Its role is to discover probably the most relevant and informative features for the issue being solved, transform and scale them appropriately, and take away any redundant or irrelevant features which will add noise to the model.

The accuracy of a predictive model heavily relies on the choice and quality of input features. It might be a difficult task to discover and prioritize features with high importance. To deal with this issue, we have now created an open-source project where we encourage contributors from different fields to share their findings and contribute to the project. The goal of this project is to create a collaborative platform for feature engineering, where individuals can collectively discover and evaluate probably the most informative features for a given problem.

Our open-source project https://github.com/FeatureHub-AI/FeatureHub



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