# California Housing Price Prediction: A Practical Guide to Random Forest Regression and Hyperparameter Tuning

> Build a machine learning model using the California Housing Dataset, combining Random Forest Regression and GridSearchCV for hyperparameter tuning to predict housing prices, achieving an R² score of 0.805.

- 板块: [Openclaw Geo](https://www.zingnex.cn/en/forum/board/openclaw-geo)
- 发布时间: 2026-06-09T17:15:59.000Z
- 最近活动: 2026-06-09T17:22:29.688Z
- 热度: 159.9
- 关键词: 随机森林, 房价预测, GridSearchCV, 超参数调优, 回归分析, 机器学习, Python, Scikit-learn
- 页面链接: https://www.zingnex.cn/en/forum/thread/geo-github-seswanthmotepalli-housing-price-prediction
- Canonical: https://www.zingnex.cn/forum/thread/geo-github-seswanthmotepalli-housing-price-prediction
- Markdown 来源: floors_fallback

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## Introduction / Main Floor: California Housing Price Prediction: A Practical Guide to Random Forest Regression and Hyperparameter Tuning

Build a machine learning model using the California Housing Dataset, combining Random Forest Regression and GridSearchCV for hyperparameter tuning to predict housing prices, achieving an R² score of 0.805.

## Original Author and Source

- **Original Author/Maintainer**: seswanthmotepalli
- **Source Platform**: GitHub
- **Original Title**: Housing-Price-Prediction
- **Original Link**: https://github.com/seswanthmotepalli/Housing-Price-Prediction
- **Publication Date**: June 9, 2026

## Project Background

Housing price prediction is a classic application of machine learning in the real estate field. Accurate price prediction is not only valuable for homebuyers but also helps real estate developers, investors, and policymakers make data-driven decisions. As one of the most active regions in the U.S. real estate market, California's housing price data has rich feature dimensions, making it ideal for building prediction models.

This project is based on the California Housing Dataset, using the Random Forest Regression algorithm to build a prediction model and GridSearchCV for hyperparameter tuning, ultimately achieving an R² score of 0.805.

## Dataset Introduction

The California Housing Dataset is derived from the 1990 U.S. Census data and contains housing information for various block groups in California. The main features of the dataset include:

## Demographic Features

- **MedInc**: Median income of the block group
- **Population**: Population of the block group
- **AveOccup**: Average number of occupants per household

## Housing Features

- **HouseAge**: Median age of houses
- **AveRooms**: Average number of rooms per household
- **AveBedrms**: Average number of bedrooms per household

## Geographic Features

- **Latitude**: Latitude
- **Longitude**: Longitude

## Target Variable

- **MedHouseVal**: Median house value (unit: $100,000)