You are a Data Analyst working at a Real Estate Investment Trust. The Trust would like to start investing in Residential real estate. You are tasked with determining the market price of a house given a set of features. You will analyze and predict housing prices using attributes or features such as square footage, number of bedrooms, number of floors, and so on.
You will be using Jupyter Notebook through IBM Watson Studio for the final project and will be required to share the link to your notebook. If you are not familiar with IBM Watson Studio, instructions on how to get started have been provided for you.
This dataset contains house sale prices for King County, which includes Seattle. It includes homes sold between May 2014 and May 2015. It was taken from here. It was also slightly modified for the purposes of this course. Here is the description of the data:
Variable Description
- id A notation for a house
- date Date house was sold
- price Price is the prediction target
- bedrooms Number of bedrooms
- bathrooms Number of bathrooms
- sqft_living Square footage of the home
- sqft_lot Square footage of the lot
- floors Total floors (levels) in house
- waterfront House which has a view of a waterfront
- View Has been viewed
- condition How good the condition is overall
- grade overall grade given to the housing unit, based on the King County grading system
- sqft_above Square footage of house apart from the basement
- sqft_basement Square footage of the basement
- yr_built Built Year
- yr_renovated Year when the house was renovated
- zipcode Zip code
- lat Latitude coordinate
- long Longitude coordinate
- sqft_living15 Living room area in 2015(implies-- some renovations) This might or might not have affected the lot size area
- sqft_lot15 LotSize area in 2015(implies-- some renovations)
Copy the link to the notebook and paste it into IBM Watson Studio: https://cocl.us/da0101en_coursera_labb
You will be required to submit a link to your notebook for peer grading.
- Have you reproduced the correct information using the functions?
- Have you created the appropriate graphs?
- Did you properly fit a regression model?
- Have you shared the link to your Notebook?
Your English language, including spelling or grammatical mistakes. The content of any text or image(s) or where a link is hyperlinked to.
Hunter Sparrow