Using the King County House Sales dataset, we will predict the sale price of houses as accurately as possible based on all the values we are given. We will be using the OSEMN process to clean, normalize, and model the data. The columns we will be analizing in this project
Student name: Joey Husney Student pace: full time Scheduled project review date/time: 10/01/2020 Instructor name: James Irving
The data we will be using is from the kings county house sales dataset. The main datapoints we will be using to predict the sale price are the square footage of the house, the basement square footage, and the condition of the house.
- Obtain
- Scrub
- Explore
- Model
- Interpret
The goal of this project is to predict certain important details about the homes in kings county based on the data given to us. Although we are very limited due to a small sample size, we will try to make predictions that are sensible. After modelling and predicting the data, we will use the information obtained to create a presentation where any non-technical audience would be able to easily understand.
It seems that the most important aspects of these houses are square footage, home quality, and we would assume that better areas would increase the price as well but that was not actually analyzed.
Sellers in the kings county can increase the value of their homes by ensuring that the condition of their home is in good shape. Another useful thing to be considered would be to extend their home if possible because that as well affects the sale price