A Comprehensive Dataset for Price Forecasting with 13 key Features.

House Price Prediction Predicting house prices using machine learning based on factors such as size, number of bedrooms and bathrooms, lot size, and location (zip code). This project aims to develop accurate models to forecast house prices, leveraging data analysis and machine learning algorithms for insightful predictions.

Zillow Home Value Index (ZHVI): A measure of the typical home value and market changes across a given region and housing type. It reflects the typical value for homes in the 35th to 65th percentile range. Available as a smoothed, seasonally adjusted measure and as a raw measure.

Comprehensive Dataset for Predicting House Prices Based on Location, Features.

Overview: This dataset was collected and curated to support research on predicting real estate prices using machine learning algorithms, specifically Support Vector Regression (SVR) and Gradient Boosting Machine (GBM).

Feb 14, 2017 · This notebook explores the housing dataset from Kaggle to predict Sales Prices of housing using advanced regression techniques such as feature engineering and gradient boosting. Kaggle House Prices: Advanced Regression Techniques.Public Leaderboard Score …

Description: This is a benchmark dataset for houses prices that contains both visual and textual information. Each house is represened by four images for bedroom, bathroom, kitchen and a frontal image of the house.