Predicting House Prices Using Linear Regression
Introduction
Regression analysis is a statistical technique used to describe relationships among variables. The simplest case to examine is one in which a variable Y, referred to as the dependent or target variable, may be related to one variable X, called an independent or explanatory variable, or simply a regressor. If the relationship between Y and X is believed to be linear, then the equation for a line may be appropriate: Y = β1 + β2.X , where β1 is an intercept term and β2 is a slope coefficient.
Predicting House Prices
We here take a dataset 'home_data.gl' and convert it into SFrame using graphlab library. Then an exploratory data analysis is done to find relationship between the target variable 'price' and other variables inorder to find variables that have higher correlation with target variable 'price'. A linear regression model is applied to predict the house prices only using a single feature variable 'sqft_living'. Then another linear regression model is developed using more feature variables like 'sqft_living', 'bedrooms','bathrooms', 'zipcode', 'floors', and 'sqft_lot'. Then its observed that RMSE error decreased after adding more feature variables.