Predicting House Prices via Regression — ML Model

Predicting House Prices via Regression — ML Model

In this equation, β0 is the y-intercept and each value of x is a predictor variable. For beginner students, one of the most common ways to learn linear regression is by building a model to predict the price of a house based on specific features of the house. For this project, the Ames Housing Data Set was used.

The most preferable way to calculate or predict a house price is through searching for all other houses in the neighborhood. This way of achieving the perfect prediction from the available dataset can be done by Machine Learning’s REGRESSION model. You can train your model by providing a good amount of dataset of available houses along with their different features present in them like bedrooms, bathrooms, floors, square feet, year of built, etc.

In this module, we will be using Python, Jupyter Notebook, and Turi Create.

  1. Download and install Python: https://www.python.org/downloads/.
  2. Download and install Jupyter Notebook: http://jupyter.org/install. Follow the instructions for “Installing Jupyter with pip,” use the commands under the section for Python 3
  3. Download and install Turi Create: https://github.com/apple/turicreate#installation.

Launch Turi Create

import turicreate

Load house sales data

sales = turicreate.SFrame(‘home_data.sframe’)

sales

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