Introduction of the Project

In recent years, Machine Learning has transformed how businesses make decisions by enabling systems to learn patterns from historical data and generate predictions automatically. One of the most practical applications of Machine Learning is predicting house prices based on property characteristics.

The value of a house depends on several factors, including location, crime rate, tax rate, environmental conditions, and neighborhood characteristics. Traditionally, estimating a property's value required expert analysis and market research. However, machine learning models can analyze historical housing data and predict house prices with remarkable accuracy.

In this project, we will build a House Price Prediction System using the California Housing Dataset and the XGBoost Regressor algorithm. The project covers the complete machine learning workflow, from understanding the problem statement and exploring the dataset to training a model and evaluating its performance.

Before implementing the model, it is important to understand the core concepts that make this prediction system possible.