{"id":8175,"date":"2021-04-05T00:17:36","date_gmt":"2021-04-05T00:17:36","guid":{"rendered":"https:\/\/wealthrevelation.com\/data-science\/2021\/04\/05\/predicting-housing-prices-in-ames-iowa\/"},"modified":"2021-04-05T00:17:36","modified_gmt":"2021-04-05T00:17:36","slug":"predicting-housing-prices-in-ames-iowa","status":"publish","type":"post","link":"https:\/\/wealthrevelation.com\/data-science\/2021\/04\/05\/predicting-housing-prices-in-ames-iowa\/","title":{"rendered":"Predicting Housing Prices in Ames, Iowa"},"content":{"rendered":"
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There are many qualities of a residential home that determine its worth and price outside of just the number of rooms and square footage. Taking a closer look at 81 residential variables from Ames, Iowa between 2006 to 2010, we were able to use various machine learning methods to determine features of a home in this region that are important to predict sale prices. In this regression analysis, the R<\/span>2<\/span><\/sup> value across select regression models helped determine which model best fit the dataset and predicted housing prices.<\/span><\/p>\n

The dataset includes several residential features, from ordinal numeric values like OveralQual and GrLivArea to different string variables. In all, after combining the test and training data, over 2900 observations were used for fitting various models.<\/span><\/p>\n

Applying a correlation heatmap to the data reveals which features are most correlated to the sale price (SalePrice) and which are least correlated. The heat map below shows the 10 features most correlated to SalePrice. There’s no great surprise here. The top 10 are the primary features people look for in a home they’re considering buying. So it’s no wonder that they would most likely affect sale prices. Converting the full heatmap to a bar chart, we can also see the features least correlated to the price like BsmtFinSF2, BsmthHalfBath, and MiscVal.<\/span><\/p>\n

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As noted, the overall quality (OverallQual) greatly affects the sale price. The higher the OverallQual, the higher the price, as shown on the box plot below.<\/span><\/p>\n

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Missing Data<\/h2>\n

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A number of features were identified with significant amounts of missing data. The row Id and features with more than 80% missing values, including PoolQC, MiscFeature (with its counterpart, MiscVal), Alley, and Fence were deleted from the dataset.\u00a0<\/span><\/p>\n

Other features with missing values were imputed based on their data type. Categorical features were imputed with \u201cNone.\u201d Numerical features were imputed with zeros. Features including LotFrontage, MSZoning, Utilities, Electrical, KitchenQual, SaleType, Functional, Exterior1st, and Exterior2nd were imputed with the mode.<\/span><\/p>\n

One difference between the simple and advanced dataset include whether GarageCars, GarageArea, and MasVnrArea had a \u201cNone\u201d or zero imputation, as both imputation methods yielded different R<\/span>2<\/span><\/sup> values.<\/span><\/p>\n

To further improve the data set, several features were transformed to yield meaningful insight. For example, several quality and condition string features–including ExterQual, ExterCond, BsmtQual, BsmtCond, HeatingQC, KitchenQual, GarageQual, GarageCond–had values that were converted to ordinal numerical values (e.g. {None: 0, Po: 1, Fa: 2, TA: 3, Gd: 4, Ex: 5}). Other categorical variables were converted to ordinal values as well, as seen below:<\/span><\/p>\n