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In this NoteBook I detail the machine learning (ML) models I implemented to accurately predict the housing prices in Boston suburbs. The dataset for this experiment is accessed from the UCI Machine Learning repository via https://archive.ics.uci.edu/ml/datasets/Housing. The ipython Notebook is organized in such a way as to demonstrate the entire process right from getting and cleaning the data, to exploratory analysis of the dataset to understand the distribution and importance of various features in influencing the algorithm, to coming with a hypothesis, training ML models, evaluation of the models, etc.