Project Overview

The Boston housing market is highly competitive, and you want to be the best real estate agent in the area. To compete with your peers, you decide to leverage a few basic machine learning concepts to assist you and a client with finding the best selling price for their home. Luckily, you’ve come across the Boston Housing dataset which contains aggregated data on various features for houses in Greater Boston communities, including the median value of homes for each of those areas. Your task is to build an optimal model based on a statistical analysis with the tools available. This model will then be used to estimate the best selling price for your clients' homes.

Project Highlights

This project is designed to get you acquainted with the many techniques for training, testing, evaluating, and optimizing models, available in sklearn.

Things you will learn by completing this project:

How to explore data and observe features.
How to train and test models.
How to identify potential problems, such as errors due to bias or variance.
How to apply techniques to improve the model, such as cross-validation and grid search.

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