Data Mining Application

Supervised Data Mining Techniques

The algorithm learns from labeled data, where both the input (features) and output (target) are provided during training.

Unsupervised Data Mining Techniques

The algorithm works with unlabeled data and tries to find hidden patterns or relationships.

Reinforcement Learning Techniques

Learning where an agent interacts with an environment and learns through rewards and penalties

Supervised learning is widely used for

Predictive modeling in industries like finance, healthcare, marketing and retail. Can deliver powerful predictions and help organizations make data-driven decisions.

Labeled Data

Input data comes with known outputs (target labels).

Learning from Example

The algorithm learns to map inputs to outputs based on provided examples.

Prediction-Focused

Once trained, the model predicts labels or continuous values for new, unseen data

Finance

Detect fraudulent transactions