Supervised learning involves training a model on labeled data, where the input and output are known, enabling the model to predict outcomes for new data. Unsupervised learning, on the other hand, deals with unlabeled data, identifying patterns or groupings without predefined labels. Supervised learning is used for tasks like classification and regression, while unsupervised learning is ideal for clustering and dimensionality reduction.

Overfitting occurs when a model learns the training data too well, capturing noise and outliers, which harms its generalization. To mitigate this, I use techniques like cross-validation, regularization (e.g., L1/L2), and pruning in decision trees. Additionally, I ensure sufficient training data and employ early stopping during training. Simplifying the model architecture and using dropout in neural networks also help prevent overfitting.

Feature engineering involves selecting, transforming, and creating relevant features to improve model performance. I have experience using techniques like normalization, encoding categorical variables, and extracting meaningful insights from raw data. It is crucial because well-engineered features can significantly enhance a model's accuracy and efficiency, reducing the need for complex algorithms and computational resources.

I evaluate model performance using metrics like accuracy, precision, recall, F1-score, and ROC-AUC for classification tasks, and RMSE or MAE for regression. For imbalanced datasets, I rely on precision-recall curves. Cross-validation ensures robustness, and I also analyze confusion matrices to understand misclassifications. These metrics provide a comprehensive view of the model's effectiveness and areas for improvement.

In a recent project, I developed a recommendation system for an e-commerce platform. I started by collecting and preprocessing user interaction data, then engineered features like purchase history and product ratings. I implemented collaborative filtering and matrix factorization models, evaluated them using RMSE, and deployed the best-performing model using Flask. The solution improved user engagement by 20%, demonstrating the impact of a well-executed AI/ML pipeline.