California Housing Price Prediction

The California Housing Price prediction problem is a supervised learning problem where the goal is to predict the median house value in California based on various features such as median income, median age, average rooms, etc.

One common solution to this problem is to use regression analysis, where a model is trained to predict the median housing value based on the input features. The most commonly used regression algorithms for this problem are linear regression and decision tree regression. The model is trained using historical data and then used to predict the median housing value for new data.

In order to improve the accuracy of the model, various feature engineering techniques such as normalization, polynomial expansion, and interaction terms can be used. Additionally, cross-validation techniques can be used to tune the hyperparameters of the model, such as the learning rate and the regularization coefficient.

Once the model is trained and tuned, it can be used to make predictions on new data and evaluate its performance using metrics such as mean squared error, mean absolute error, or R-squared.