guanwencan/California_Housing_Price_Recovery

Datasets Directory

This directory contains the California Housing dataset, a widely-used machine learning dataset for regression tasks. The dataset contains information about housing districts in California based on 1990 census data and is commonly used for testing spatial analysis algorithms, geographic modeling, and machine learning techniques.

Dataset Contents

The California Housing dataset is provided in two different formats to accommodate various analysis requirements and computational preferences.

california_housing.csv

This is the main dataset file containing 506 housing district records in comma-separated values format. Each row represents a housing district with 14 features including geographic coordinates (longitude, latitude), housing characteristics (median age, total rooms, total bedrooms), demographic information (population, households, median income), and the target variable (median house value). The data is preprocessed and ready for immediate use in machine learning pipelines and statistical analysis.

CaliforniaHousing Directory

This subdirectory contains the same dataset in the original format used by machine learning repositories. The cal_housing.data file contains the raw numerical data in space-separated format, while cal_housing.domain provides metadata describing each attribute as continuous variables. This format is particularly useful for researchers who need to understand the original data structure or prefer working with domain-specific file formats.

Data Attributes

The dataset includes nine continuous variables describing various aspects of California housing districts. Longitude and latitude provide precise geographic positioning for spatial analysis and geographic visualization. Housing characteristics include median age of buildings, total number of rooms and bedrooms per district, which help assess property types and density. Demographic features encompass total population and number of households, providing context for housing demand. Median income represents the economic profile of each district, while median house value serves as the primary target variable for predictive modeling.

Usage Recommendations

For most machine learning applications and data analysis tasks, the CSV format provides immediate accessibility and compatibility with popular data science libraries. Researchers conducting geographic analysis or spatial modeling will benefit from the longitude and latitude coordinates for creating geographic visualizations and studying spatial relationships. The dataset's moderate size and clean structure make it ideal for algorithm development, model comparison studies, and educational purposes in machine learning courses.

Data Quality

All data has been preprocessed and cleaned, with no missing values or categorical variables requiring additional encoding. The continuous nature of all variables makes this dataset particularly suitable for regression algorithms, Gaussian processes, and Bayesian modeling approaches. Geographic coordinates are provided in decimal degrees, enabling direct integration with mapping libraries and geographic information systems.