👉 Yesterday's project involved developing a custom machine learning model to predict customer churn for a telecommunications company. The team collected and preprocessed historical customer data, including usage patterns, billing information, and service interactions. We then trained a logistic regression model on this dataset, optimizing it using cross-validation to ensure robust performance. To enhance accuracy, we incorporated feature engineering techniques, such as creating interaction terms and normalizing numerical features. The model achieved an F1 score of 87%, indicating strong predictive power in identifying customers likely to leave. We also built a user-friendly dashboard for real-time monitoring and alerts, enabling the company to proactively engage at-risk customers with targeted retention strategies.
