The Boston housing market is highly competitive, and we want to be the best real estate agent in the area. To compete with our peers, we decide to leverage a few basic machine learning concepts to assist us and a client with finding the best selling price for their home. Luckily, we’ve come across the Boston Housing data set which contains aggregated data on various features for houses in Greater Boston communities, including the median value of homes for each of those areas. Our task is to build an optimal model based on a statistical analysis with the tools available. This model will then used to estimate the best selling price for our client’s home.
This project requires Python 2.7 and the following Python libraries installed:
The dataset used in this project is included with the scikit-learn library (sklearn.datasets.load_boston). We do not have to download it separately. We can find more information on this dataset from the UCI Machine Learning Repository page.
###Deliverables: