Training Data

Training data is the labeled dataset used to teach a machine learning model the patterns it needs to learn. The model adjusts its parameters during training to minimize the difference between its predictions and the known correct answers in the training data.

The quality, size, and representativeness of training data directly determine model performance. A property valuation model trained only on urban high-rise apartments will perform poorly on suburban houses. Models are as biased as their training data - if historical data reflects past discrimination, the model may perpetuate it.

Datasets from HappyEndpoint - such as sold property transaction records or product price history - can serve as training data for models that predict future prices, estimate property value, or detect pricing anomalies. The key requirements are sufficient volume, accurate labels (the target variable you want to predict), and coverage of the edge cases your model will encounter in production.