Housing Prices Predictor

Files

• Project created to enter Kaggle competition to predict housing prices for listings in Ames, IA using a variety of metrics and descriptive factors
• The 01-cleaning.ipynb Jupyter Notebook cleans the raw housing data taken from Kaggle
• The 02-exploration_numeric_variables.ipynb and 03-exploration_categorical_variables.ipynb Jupyter Notebooks explore the variables in the dataset and those variables' relationships with housing prices
• The 04-single_variable_regression.ipynb Jupyter Notebook produces a single variable linear regression model that demonstrates the relationship between housing prices and home square feet
• The 05-multiple_variable_regression.ipynb Jupyter Notebook produces a multiple variable linear regression model that demonstrates the relationship between housing prices and home square feet, home quality, exterior material quality, kitchen quality, garage size, number of bathrooms, number of bedrooms, home age, and neighborhood
• The 06-random_forest.ipynb Jupyter Notebook produces a random forest regression to model home prices

Final Product

• See published presentation/dashboard that details the analysis here
• Findings are summarized in the slides below: