Codecademy Data Science course portfolio project.
The aim of this project was to use data provided on rental properties in New York and produce a model that could predict how much the rent should be for any given property.
Running main.py will use LinearRegression() and train_test_split() from sklearn to produce an OLS model which describes how rent prices are influenced size_sqft and building_age_yrs. These variables can be changed near the top by changing x_value1 and X_value2.
It will also display this data in 3D.
Similarly, running mlr.py will use sklearn to produce a model which describes how rent prices are influenced by all other variables in manhattan.csv (this can be changed to brooklyn.csv or queens.csv near the top). It will then use this model to predict the rent for a chosen property from StreetEasy.com. Additional functions also exist for visualising the model, performing residual analysis, scoring the model, and examining the coefficients.