The purpose of this data science project is determine the best model for predicting sales prices of houses in Ames, Iowa, and to find the features that best correlate to predicted sales price.This is done through the investigation and utilization of the Ames housing dataset with over 70 columns of different features relating to house.
The process we go through in this notebook is as follows:
- Data Cleaning and Encoding. Filling missing values, removing outliers, and transforming categorical features into dummified features.
- Data Visualization and Correlation. Using seaborn and matplotlib to investigate relationships in the data.
- Feature Engineering. Engineering new features through manipulation and combinations of existing data. We use orbitalencoder to investigate scoring metrics by applying mathematical operations to correlated encoded data.
- Modelling. Using GridSearchCV to find the best model, using the cross-validated R2 score as our scoring metric. We will compare and test the following models
| feature | dype | Description |
|---|---|---|
| MS SubClass | int64 | |
| MS Zoning | object | Identifies the general zoning classification of the sale. |
| Lot Frontage | float64 | Linear feet of street connected to property |
| Lot Area | int64 | Lot size in square feet |
| Street | object | Type of road access to property |
| Lot Shape | object | General shape of property |
| Land Contour | object | Flatness of the property |
| Neighborhood | object | Physical locations within Ames city limits |
| Condition 1 | object | Proximity to main road or railroad |
| Condition 2 | object | Proximity to main road or railroad (if a second is present) |
| Bldg Type | object | Type of dwelling |
| House Style | object | Style of dwelling |
| Overall Qual | int64 | Overall material and finish quality |
| Overall Cond | int64 | Overall condition rating |
| 10 Very Excellent | ||
| Year Built | int64 | Original construction date |
| Year Remod/Add | int64 | Remodel date (same as construction date if no remodeling or additions) |
| Roof Style | object | Type of roof |
| Flat Flat | ||
| Roof Matl | object | Roof material |
| Exterior 1st | object | Exterior covering on house |
| Exterior 2nd | object | Exterior covering on house (if more than one material) |
| Mas Vnr Type | object | Masonry veneer type |
| Mas Vnr Area | float64 | Masonry veneer area in square feet |
| Exter Qual | object | Exterior material quality |
| Exter Cond | object | Present condition of the material on the exterior |
| Foundation | object | Type of foundation |
| Bsmt Qual | object | Height of the basement |
| Bsmt Cond | object | General condition of the basement |
| Bsmt Exposure | object | Walkout or garden level basement walls |
| BsmtFin Type 1 | object | Quality of basement finished area |
| BsmtFin Type 2 | object | Quality of second finished area (if present) |
| Total Bsmt SF | float64 | Total square feet of basement area |
| Heating | object | Type of heating |
| Heating QC | object | Heating quality and condition |
| Central Air | object | Central air conditioning |
| Electrical | object | Electrical system |
| Gr Liv Area | int64 | Above grade (ground) living area square feet |
| Bsmt Full Bath | float64 | Basement full bathrooms |
| Bsmt Half Bath | float64 | Basement half bathrooms |
| Full Bath | int64 | Full bathrooms above grade |
| Half Bath | int64 | Half baths above grade |
| Bedroom AbvGr | int64 | Number of bedrooms above basement level |
| Kitchen AbvGr | int64 | Number of kitchens |
| Kitchen Qual | object | Kitchen quality |
| TotRms AbvGrd | int64 | Total rooms above grade (does not include bathrooms) |
| Functional | object | Home functionality rating |
| Fireplaces | int64 | Number of fireplaces |
| Fireplace Qu | object | Fireplace quality |
| Garage Type | object | Garage location |
| Garage Yr Blt | float64 | Year garage was built |
| Garage Finish | object | Interior finish of the garage Fin Finished |
| Garage Cars | float64 | Size of garage in car capacity |
| Garage Area | float64 | Size of garage in square feet |
| Garage Qual | object | Garage quality |
| Garage Cond | object | Garage condition |
| Paved Drive | object | Paved driveway |
| Wood Deck SF | int64 | Wood deck area in square feet |
| Open Porch SF | int64 | Open porch area in square feet |
| Enclosed Porch | int64 | Enclosed porch area in square feet |
| 3Ssn Porch | int64 | Three season porch area in square feet |
| Screen Porch | int64 | Screen porch area in square feet |
| Pool Area | int64 | Pool area in square feet |
| Mo Sold | int64 | Month Sold |
| Yr Sold | int64 | Year Sold |
| Sale Type | object | Type of sale |
| SalePrice | int64 | the property's sale price in dollars |
We were able to achieve a 0.882 score using ElasticNet with parameters chosen by GridSearchCV. The scoring metric was a cross-validated R2_score of 10 K-folds. Furthermore, the performance of the model benefitted from our data cleaning and encoding operations. Certain features that were engineered were able to enchance the signal of the data by a significant degree.