Creating a Predictive Model for Home Prices that Anyone Can Use

Creating a Predictive Model for Home Prices that Anyone Can Use

My team and I was given the task to create a predictive model for the sales prices of homes in the Seattle, Washington area, more specifically, King County.

My team and I was given the task to create a predictive model for the sales prices of homes in the Seattle, Washington area, more specifically, King County. I personally wanted to take a deeper dive and conduct an analysis on how home condition, the year the home was built, and zip code affected home prices. I conducted a multiple linear regression model and created a predictive model of home prices specifically geared around those three features. I know there are so many ways you can construct a predictive model, some more complexed and complicated than others, but I found this method to be pretty simple and it also gives you the ability to create a user friendly interface that anyone can use simply by plugging in different feature values.

Let us first talk about exactly what Linear Regression is. Linear Regression is the modeling technique used to estimate the strength and direction of the relationship between two (or more) variables. You will use a dependent variable, also called the target variable, and independent variables, which are also called features or predictors. Linear Regression uses only one independent and one dependent variable to determine the correlation between the two and Multiple Linear Regression uses multiple independent variables and one dependent variable. Regression is a parametric technique, which means that it uses parameters learned from data and it is also considered the beginning concept of machine learning. Some assumptions that must be made when conducting a linear regression model is that the data must possess:

  1. Linearity
  2. Multicollinearity
  3. Normality
  4. Homoscedasticity

linear-regression data-scientist data analysis

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