In this project I meticulously clean and engineer features for a Bengaluru housing price dataset. We start with setup and overview, use univariate analysis for insight, address missing values, and employ robust feature engineering, including outlier handling and categorical transformation. Numerical features are normalized, and the dataset is prepared for modeling by dropping unnecessary columns. This comprehensive process ensures the dataset is optimized for accurate machine learning predictions of housing prices in Bengaluru.