Unsupervised Learning

Supervised learning is a type of machine learning where models are trained on labeled datasets – that is, data where both the input and the correct output (label) are known. The algorithm learns to map inputs to outputs by minimizing the error between its predictions and the actual labels. Common applications include classification (e.g., spam detection) and regression (e.g., sales forecasting). Supervised learning is widely used in industries such as finance, healthcare, and retail for tasks like fraud detection, diagnosis prediction, and customer segmentation. It requires high-quality labeled data to achieve accurate and reliable results.