Building the House Price Prediction Model Using XGBoost

After preparing the dataset and creating training and testing sets, the next stage is building the machine learning model.

The choice of algorithm plays a significant role in determining prediction accuracy. Different machine learning algorithms have different strengths and weaknesses. For house price prediction, algorithms capable of learning complex relationships between housing features and prices often produce better results.

In this project, we use the XGBoost Regressor, a powerful ensemble learning algorithm that combines multiple decision trees to generate highly accurate predictions.

In this module, we will understand how XGBoost works, train the model using the California Housing Dataset, and learn how machine learning algorithms identify patterns from historical data.