Using different regression methods on the dataset of House sales with 20k observations, owner or customer is most likely to calculate the approximate value of a particular property based on various factors. A pattern is generated which shows how changes in one feature will affect other features and will also affect the predicted value.
Tools: numpy, pandas, sklearn, python, seaborn, jupyter notebook