We try to explore the sklearn library and apply linear regression on the Boston Housing Data. We also did some EDA in order to explore the insights of data.
This Housing dataset contains information about different houses in Boston. This data was originally a part of UCI Machine Learning Repository and has been removed now.
There are 506 samples and 13 feature variables in this dataset. The objective is to predict the value of prices of the house using the given features. The dataset itself is available https://archive.ics.uci.edu/ml/datasets/Housing . However, because we are going to use scikit-learn, we can import it right away from the scikit-learn itself.