This notebook contains EDA and modeling details for housing price prediction on Ames Housing dataset with 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa. This dataset is an incredible alternative for a modernized and expanded version of the often cited Boston Housing dataset. First data is downloaded from Kaggle, missing data is handled, categorical data is encoded and then, some features with very low variances are removed. Next, some basic new features are created or substituted old features. After that, more feature extraction is performed using supervised dimensionality reduction followed by a KNN to produce a new feature for the final XGBOOST model. To have a more stable final model, an forest XGBOOST (of 20 parallel trees at each boosting step) is trained and finally the predictions are submitted to Kaggle platform. At the time of submission, the ranking was in the top 4%.