Predictive Modeling of Customer Churn Using Machine Learning

To analyze and understand factors influencing customer churn in the education sector.

To build a predictive model using machine learning algorithms to identify customers at risk of churning.

To evaluate the performance of the predictive model and identify areas for improvement.

Collect and preprocess relevant data on customer churn and education sector variables.

Explore and analyze the data to identify key factors influencing customer churn.

Select and implement appropriate machine learning algorithms for predictive modeling.

Train and evaluate the predictive model using performance metrics such as accuracy, precision, and recall.

Interpret the results and provide recommendations for reducing customer churn in the education sector.