This Zillow House Price Project is to analyze US house price data from 1966 to 2018 and create a California house pricing prediction model (Time Series model) based on time and house size rank.

Time Series projections for Zillow housing data for San Francisco - Flatiron School Data Science Immersive (Phase 4) - a collaborative project between Jonathan Silverman and Wei Alexander Xin

Advanced Housing Price Predictor is an ML project that forecasts housing prices using Zillow, California, and Kaggle datasets with multiple models, time-series forecasting, and interactive visualizations.

This project uses data processing, visualization, and predictive modeling techniques using tools learned throughout the semester. The analysis focuses on California housing price trends using Zillow housing data, incorporating data cleaning, time series analysis, statistical modeling, and visualization to better understand housing growth patterns.

This project explores and forecasts housing market trends using Zillow data for a selected U.S. metro area. Using Python and time series modeling, we analyze historical price patterns and predict future changes in home values.

This project, completed for my time series modeling course, used R to create multiple linear regression, boosted regression, ARIMA, Artificial Neural Networks, and XGBoost for Kaggle's Zillow Housing competition. Decision trees and ggmap were used to impute missing values.

This project mainly aims to produce reliable forecasts of time series data utilizing median signal family home price data for San Francisco from 1996 to 2018, provided by Zillow datasets. Two prediction models for housing prices are build, simple time series forecast model and simple linear regression model. The performances of two models are compared.