In my previous role, I worked on a project to predict customer churn for a telecommunications company. I utilized a combination of feature engineering, including customer demographics and usage patterns, and implemented a gradient boosting model to predict churn with high accuracy. The model's insights were used to develop targeted retention strategies, resulting in a 15% reduction in churn rates.

I regularly attend industry conferences, participate in online courses, and follow leading research journals and blogs. Additionally, I engage in online communities like Kaggle and GitHub to collaborate on projects and stay informed about emerging trends and techniques. This continuous learning approach ensures that my skills remain current and relevant.

Supervised learning involves training models on labeled data, where the desired output is known, allowing the model to learn a mapping from inputs to outputs. Unsupervised learning, on the other hand, deals with unlabeled data and aims to find hidden patterns or intrinsic structures in the input data. Common techniques include clustering and dimensionality reduction.

I prioritize metrics that align with the business objectives of the project. For classification tasks, I focus on accuracy, precision, recall, and F1-score. For regression, I consider Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). Additionally, I perform cross-validation to ensure the model's robustness and avoid overfitting.

I employ several strategies, including oversampling the minority class, undersampling the majority class, and using synthetic data generation techniques like SMOTE. I also adjust class weights in the model to give more importance to the minority class. Additionally, I evaluate models using metrics like AUC-ROC and precision-recall curves that are more sensitive to class imbalance.