Introduction to House Price Prediction: Building a Complete Project with Three Machine Learning Algorithms

This project is a classic practice for machine learning beginners. Using the California Housing Dataset, it compares three mainstream regression algorithms—linear regression, decision trees, and random forests—covering the complete workflow of data exploration, feature engineering, model training, and evaluation. It helps learners understand the characteristics, applicable scenarios, and trade-offs in model selection of different algorithms, making it an ideal starting point for establishing a systematic understanding of machine learning.

The California Housing Dataset comes from the 1990 California census block groups. Each sample represents a block group and includes 8 features: MedInc (median income), HouseAge (median house age), AveRooms (average number of rooms), AveBedrms (average number of bedrooms), Population (population), AveOccup (average number of occupants per household), Latitude (latitude), and Longitude (longitude). The target variable is MedHouseVal (median house value, capped at $500,000). This dataset has moderate feature dimensions, high quality, and includes geographic information, making it suitable for beginners to understand regression problems and the importance of feature engineering.

1. Linear Regression: A basic algorithm that assumes a linear relationship between the target and features. Advantages: fast training, strong interpretability, low data volume requirements. Limitations: can only capture linear relationships, sensitive to outliers, need to avoid multicollinearity.
2. Decision Tree: Uses a tree structure to split data. Advantages: can capture non-linear relationships and feature interactions, no need for feature scaling, robust to outliers, interpretable. Limitations: prone to overfitting, sensitive to data changes, discontinuous prediction results.
3. Random Forest: An ensemble method of decision trees. It builds multiple trees through Bootstrap sampling and random feature selection, then takes the average. Advantages: reduces overfitting risk, higher accuracy, can evaluate feature importance. Limitations: time-consuming training, poor interpretability.

Evaluation Metrics: Uses RMSE (Root Mean Squared Error, reflects the size of prediction errors), MAE (Mean Absolute Error, insensitive to outliers), and R² score (proportion of variance explained by the model). Visualization: Scatter plot of predicted vs. actual values (intuitively checks accuracy), residual distribution plot (checks systematic bias), feature importance plot (feature contribution of tree models), learning curve (judges data volume requirements).

1. Importance of Feature Engineering: Creating new features (e.g., room/bedroom ratio), feature transformation (taking logarithm of income), and geocoding (converting latitude/longitude to distance) can improve prediction quality.
2. Trade-offs in Model Selection: There is no optimal algorithm; choose based on needs (linear regression is simple and fast, random forest has high accuracy). Complex models are not necessarily better, and ensemble methods are usually effective.
3. Avoiding Data Leakage: The test set should not participate in the training process (including feature scaling and selection) to prevent optimistic evaluation results.

Algorithm Level: Try gradient boosting trees (XGBoost/LightGBM), SVR, neural networks, or hyperparameter tuning (grid/random search, Bayesian optimization). Data Level: Add features like school ratings and crime rates, use Kaggle house price competition data, and handle time series trends. Engineering Level: Build a machine learning pipeline, implement model version management and A/B testing, and deploy as a web service to provide prediction APIs.

The house price prediction project is of moderate scale and close to real life, covering core machine learning concepts. Through practical algorithm comparison, learners not only master tools but also cultivate intuition in algorithm selection and rigor in evaluation. The real value of the project lies in establishing a systematic understanding, helping to transition from a 'tool user' to a data scientist.