vicmatthews/real_estate_project

Real_Estate_Project

Objective: The task is to build a model that will predict the price of a house based on features provided in the dataset. it is also key to understand which factors are responsible for higher property values.

Data: The data set consists of information on about 22,000 properties. The dataset consisted of historic data of houses sold between May 2014 to May 2015. These are the definitions of data points provided.

Id: Unique identification number for the property. date: date the house was sold. price: price of the house. waterfront: house which has a view to a waterfront. condition: How good the condition is (overall). 1 indicates worn out property and 5 excellent. grade: Overall grade given to the housing unit, based on King County grading system. 1 poor, 13 excellent. Sqft_above: square footage of house apart from basement. Sqft_living15: Living room area in 2015(implies - some renovations). This might or might not have affected the lotsize area. Sqft_lot15: lotSize area in 2015(implies - some renovations).

Exploring the data The data was checked and prepared for modeling. linear regression was used in building the model. various other models were also engaged in the project including KNN, decision trees for regression.