Zillow Home Value (Zestimate) Prediction in ML

Zillow Home Value (Zestimate) prediction involves building a regression model to estimate house prices using machine learning. This process includes data cleaning to handle null values, outliers, and high correlations, followed by exploratory data analysis (EDA) to identify key patterns and trends. The data is prepared by encoding categorical features, normalizing numeric data, and removing irrelevant columns. Various machine learning models like Linear Regression, XGBoost, Lasso, Random Forest, and Ridge are trained and evaluated using metrics like Mean Absolute Error (MAE) on validation datasets. Features and target values are scaled appropriately to ensure stable and accurate model performance.

The target variable is defined as the logarithmic difference between actual and predicted house prices, ensuring values remain in a feasible range of -1 to 1. After model evaluation, Ridge regression demonstrated the best performance with minimal error. This workflow showcases the potential of machine learning in revolutionizing real estate analytics through precise property valuation. 

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