House Price Prediction

image.png Photo: Scott Webb, pexels.com

Ames Dataset

The Ames Housing Dataset contains information on residential property sales in Ames, Iowa, USA, covering the period from 2006 to 2010.

The data consists of three files: train.csv, test.csv, and data_description.txt. The train.csv file includes the target variable SalePrice, while the test.csv file contains the same feature set without the target variable. The data_description.txt file provides detailed explanations of all variables and is included at the end of this notebook to help readers understand the dataset.

Train_ds:

MSSubClass MSZoning LotFrontage LotArea Street Alley LotShape LandContour Utilities LotConfig ... PoolArea PoolQC Fence MiscFeature MiscVal MoSold YrSold SaleType SaleCondition SalePrice
Id
1 60 RL 65.0 8450 Pave NaN Reg Lvl AllPub Inside ... 0 NaN NaN NaN 0 2 2008 WD Normal 208500
2 20 RL 80.0 9600 Pave NaN Reg Lvl AllPub FR2 ... 0 NaN NaN NaN 0 5 2007 WD Normal 181500
3 60 RL 68.0 11250 Pave NaN IR1 Lvl AllPub Inside ... 0 NaN NaN NaN 0 9 2008 WD Normal 223500
4 70 RL 60.0 9550 Pave NaN IR1 Lvl AllPub Corner ... 0 NaN NaN NaN 0 2 2006 WD Abnorml 140000
5 60 RL 84.0 14260 Pave NaN IR1 Lvl AllPub FR2 ... 0 NaN NaN NaN 0 12 2008 WD Normal 250000

5 rows × 80 columns

Test_ds:

MSSubClass MSZoning LotFrontage LotArea Street Alley LotShape LandContour Utilities LotConfig ... PoolArea PoolQC Fence MiscFeature MiscVal MoSold YrSold SaleType SaleCondition SalePrice
Id
1461 20 RH 80.0 11622 Pave NaN Reg Lvl AllPub Inside ... 0 NaN MnPrv NaN 0 6 2010 WD Normal 113431.348154
1462 20 RL 81.0 14267 Pave NaN IR1 Lvl AllPub Corner ... 0 NaN NaN Gar2 12500 6 2010 WD Normal 156069.039245
1463 60 RL 74.0 13830 Pave NaN IR1 Lvl AllPub Inside ... 0 NaN MnPrv NaN 0 3 2010 WD Normal 180724.011907
1464 60 RL 78.0 9978 Pave NaN IR1 Lvl AllPub Inside ... 0 NaN NaN NaN 0 6 2010 WD Normal 195835.483736
1465 120 RL 43.0 5005 Pave NaN IR1 HLS AllPub Inside ... 0 NaN NaN NaN 0 1 2010 WD Normal 198040.287389

5 rows × 80 columns

Information about Dataset

Train_ds:

<class 'pandas.core.frame.DataFrame'>
Int64Index: 1460 entries, 1 to 1460
Data columns (total 80 columns):
 #   Column         Non-Null Count  Dtype  
---  ------         --------------  -----  
 0   MSSubClass     1460 non-null   int64  
 1   MSZoning       1460 non-null   object 
 2   LotFrontage    1201 non-null   float64
 3   LotArea        1460 non-null   int64  
 4   Street         1460 non-null   object 
 5   Alley          91 non-null     object 
 6   LotShape       1460 non-null   object 
 7   LandContour    1460 non-null   object 
 8   Utilities      1460 non-null   object 
 9   LotConfig      1460 non-null   object 
 10  LandSlope      1460 non-null   object 
 11  Neighborhood   1460 non-null   object 
 12  Condition1     1460 non-null   object 
 13  Condition2     1460 non-null   object 
 14  BldgType       1460 non-null   object 
 15  HouseStyle     1460 non-null   object 
 16  OverallQual    1460 non-null   int64  
 17  OverallCond    1460 non-null   int64  
 18  YearBuilt      1460 non-null   int64  
 19  YearRemodAdd   1460 non-null   int64  
 20  RoofStyle      1460 non-null   object 
 21  RoofMatl       1460 non-null   object 
 22  Exterior1st    1460 non-null   object 
 23  Exterior2nd    1460 non-null   object 
 24  MasVnrType     1452 non-null   object 
 25  MasVnrArea     1452 non-null   float64
 26  ExterQual      1460 non-null   object 
 27  ExterCond      1460 non-null   object 
 28  Foundation     1460 non-null   object 
 29  BsmtQual       1423 non-null   object 
 30  BsmtCond       1423 non-null   object 
 31  BsmtExposure   1422 non-null   object 
 32  BsmtFinType1   1423 non-null   object 
 33  BsmtFinSF1     1460 non-null   int64  
 34  BsmtFinType2   1422 non-null   object 
 35  BsmtFinSF2     1460 non-null   int64  
 36  BsmtUnfSF      1460 non-null   int64  
 37  TotalBsmtSF    1460 non-null   int64  
 38  Heating        1460 non-null   object 
 39  HeatingQC      1460 non-null   object 
 40  CentralAir     1460 non-null   object 
 41  Electrical     1459 non-null   object 
 42  1stFlrSF       1460 non-null   int64  
 43  2ndFlrSF       1460 non-null   int64  
 44  LowQualFinSF   1460 non-null   int64  
 45  GrLivArea      1460 non-null   int64  
 46  BsmtFullBath   1460 non-null   int64  
 47  BsmtHalfBath   1460 non-null   int64  
 48  FullBath       1460 non-null   int64  
 49  HalfBath       1460 non-null   int64  
 50  BedroomAbvGr   1460 non-null   int64  
 51  KitchenAbvGr   1460 non-null   int64  
 52  KitchenQual    1460 non-null   object 
 53  TotRmsAbvGrd   1460 non-null   int64  
 54  Functional     1460 non-null   object 
 55  Fireplaces     1460 non-null   int64  
 56  FireplaceQu    770 non-null    object 
 57  GarageType     1379 non-null   object 
 58  GarageYrBlt    1379 non-null   float64
 59  GarageFinish   1379 non-null   object 
 60  GarageCars     1460 non-null   int64  
 61  GarageArea     1460 non-null   int64  
 62  GarageQual     1379 non-null   object 
 63  GarageCond     1379 non-null   object 
 64  PavedDrive     1460 non-null   object 
 65  WoodDeckSF     1460 non-null   int64  
 66  OpenPorchSF    1460 non-null   int64  
 67  EnclosedPorch  1460 non-null   int64  
 68  3SsnPorch      1460 non-null   int64  
 69  ScreenPorch    1460 non-null   int64  
 70  PoolArea       1460 non-null   int64  
 71  PoolQC         7 non-null      object 
 72  Fence          281 non-null    object 
 73  MiscFeature    54 non-null     object 
 74  MiscVal        1460 non-null   int64  
 75  MoSold         1460 non-null   int64  
 76  YrSold         1460 non-null   int64  
 77  SaleType       1460 non-null   object 
 78  SaleCondition  1460 non-null   object 
 79  SalePrice      1460 non-null   int64  
dtypes: float64(3), int64(34), object(43)
memory usage: 923.9+ KB

Test_ds:

<class 'pandas.core.frame.DataFrame'>
Int64Index: 1459 entries, 1461 to 2919
Data columns (total 80 columns):
 #   Column         Non-Null Count  Dtype  
---  ------         --------------  -----  
 0   MSSubClass     1459 non-null   int64  
 1   MSZoning       1455 non-null   object 
 2   LotFrontage    1232 non-null   float64
 3   LotArea        1459 non-null   int64  
 4   Street         1459 non-null   object 
 5   Alley          107 non-null    object 
 6   LotShape       1459 non-null   object 
 7   LandContour    1459 non-null   object 
 8   Utilities      1457 non-null   object 
 9   LotConfig      1459 non-null   object 
 10  LandSlope      1459 non-null   object 
 11  Neighborhood   1459 non-null   object 
 12  Condition1     1459 non-null   object 
 13  Condition2     1459 non-null   object 
 14  BldgType       1459 non-null   object 
 15  HouseStyle     1459 non-null   object 
 16  OverallQual    1459 non-null   int64  
 17  OverallCond    1459 non-null   int64  
 18  YearBuilt      1459 non-null   int64  
 19  YearRemodAdd   1459 non-null   int64  
 20  RoofStyle      1459 non-null   object 
 21  RoofMatl       1459 non-null   object 
 22  Exterior1st    1458 non-null   object 
 23  Exterior2nd    1458 non-null   object 
 24  MasVnrType     1443 non-null   object 
 25  MasVnrArea     1444 non-null   float64
 26  ExterQual      1459 non-null   object 
 27  ExterCond      1459 non-null   object 
 28  Foundation     1459 non-null   object 
 29  BsmtQual       1415 non-null   object 
 30  BsmtCond       1414 non-null   object 
 31  BsmtExposure   1415 non-null   object 
 32  BsmtFinType1   1417 non-null   object 
 33  BsmtFinSF1     1458 non-null   float64
 34  BsmtFinType2   1417 non-null   object 
 35  BsmtFinSF2     1458 non-null   float64
 36  BsmtUnfSF      1458 non-null   float64
 37  TotalBsmtSF    1458 non-null   float64
 38  Heating        1459 non-null   object 
 39  HeatingQC      1459 non-null   object 
 40  CentralAir     1459 non-null   object 
 41  Electrical     1459 non-null   object 
 42  1stFlrSF       1459 non-null   int64  
 43  2ndFlrSF       1459 non-null   int64  
 44  LowQualFinSF   1459 non-null   int64  
 45  GrLivArea      1459 non-null   int64  
 46  BsmtFullBath   1457 non-null   float64
 47  BsmtHalfBath   1457 non-null   float64
 48  FullBath       1459 non-null   int64  
 49  HalfBath       1459 non-null   int64  
 50  BedroomAbvGr   1459 non-null   int64  
 51  KitchenAbvGr   1459 non-null   int64  
 52  KitchenQual    1458 non-null   object 
 53  TotRmsAbvGrd   1459 non-null   int64  
 54  Functional     1457 non-null   object 
 55  Fireplaces     1459 non-null   int64  
 56  FireplaceQu    729 non-null    object 
 57  GarageType     1383 non-null   object 
 58  GarageYrBlt    1381 non-null   float64
 59  GarageFinish   1381 non-null   object 
 60  GarageCars     1458 non-null   float64
 61  GarageArea     1458 non-null   float64
 62  GarageQual     1381 non-null   object 
 63  GarageCond     1381 non-null   object 
 64  PavedDrive     1459 non-null   object 
 65  WoodDeckSF     1459 non-null   int64  
 66  OpenPorchSF    1459 non-null   int64  
 67  EnclosedPorch  1459 non-null   int64  
 68  3SsnPorch      1459 non-null   int64  
 69  ScreenPorch    1459 non-null   int64  
 70  PoolArea       1459 non-null   int64  
 71  PoolQC         3 non-null      object 
 72  Fence          290 non-null    object 
 73  MiscFeature    51 non-null     object 
 74  MiscVal        1459 non-null   int64  
 75  MoSold         1459 non-null   int64  
 76  YrSold         1459 non-null   int64  
 77  SaleType       1458 non-null   object 
 78  SaleCondition  1459 non-null   object 
 79  SalePrice      1459 non-null   float64
dtypes: float64(12), int64(25), object(43)
memory usage: 923.3+ KB

Train_ds contains 1460 rows (instances) and 80 columns (variables), including the target row ‘SalePrice’, while test_ds contains 1459 rows (instances) and 79 columns (not the target column ‘SalePrice’). Both datasets cover the same period of the years 2006-2010.

'Train_ds Sold Houses'
2009    338
2007    329
2006    314
2008    304
2010    175
Name: YrSold, dtype: int64
'Test_ds Sold Houses'
2007    363
2008    318
2009    309
2006    305
2010    164
Name: YrSold, dtype: int64

EDA

Exploratory Data Analysis (EDA) is conducted to gain insight into the distributions and characteristics of the dataset prior to modeling. Visualization techniques play a key role in identifying skewness, sparsity, outliers, and potential data quality issues.

For this purpose, histograms are used to analyze numerical variables, and bar plots are used to examine categorical variables.

The following histograms summarize the distributions of the numerical features and highlight key patterns relevant for feature engineering and model selection.

Histograms

Summary:

LotFrontage LotArea YearBuilt YearRemodAdd MasVnrArea BsmtFinSF1 BsmtFinSF2 BsmtUnfSF TotalBsmtSF 1stFlrSF 2ndFlrSF LowQualFinSF GrLivArea GarageYrBlt GarageArea WoodDeckSF OpenPorchSF EnclosedPorch 3SsnPorch ScreenPorch PoolArea MiscVal YrSold
count 2919.000000 2919.000000 2919.000000 2919.000000 2919.000000 2919.000000 2919.000000 2919.000000 2919.000000 2919.000000 2919.000000 2919.000000 2919.000000 2919.000000 2919.000000 2919.000000 2919.000000 2919.000000 2919.000000 2919.000000 2919.000000 2919.000000 2919.000000
mean 69.088386 10168.114080 1971.312778 1984.264474 101.396026 441.398082 49.565262 560.739979 1051.756423 1159.581706 336.483727 4.694416 1500.759849 1978.161699 472.877013 93.709832 47.486811 23.098321 2.602261 16.062350 2.251799 50.825968 2007.792737
std 21.317898 7886.996359 30.291442 20.894344 178.854579 455.534777 169.179104 439.471764 440.692210 392.362079 428.701456 46.396825 506.051045 24.868576 215.357944 126.526589 67.575493 64.244246 25.188169 56.184365 35.663946 567.402211 1.314964
min 21.000000 1300.000000 1872.000000 1950.000000 0.000000 0.000000 0.000000 0.000000 0.000000 334.000000 0.000000 0.000000 334.000000 1895.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 2006.000000
25% 60.000000 7478.000000 1953.500000 1965.000000 0.000000 0.000000 0.000000 220.000000 793.000000 876.000000 0.000000 0.000000 1126.000000 1961.500000 320.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 2007.000000
50% 68.000000 9453.000000 1973.000000 1993.000000 0.000000 368.000000 0.000000 467.000000 990.000000 1082.000000 0.000000 0.000000 1444.000000 1979.000000 480.000000 0.000000 26.000000 0.000000 0.000000 0.000000 0.000000 0.000000 2008.000000
75% 78.000000 11570.000000 2001.000000 2004.000000 163.500000 733.000000 0.000000 805.000000 1302.000000 1387.500000 704.000000 0.000000 1743.500000 2001.000000 576.000000 168.000000 70.000000 0.000000 0.000000 0.000000 0.000000 0.000000 2009.000000
max 313.000000 215245.000000 2010.000000 2010.000000 1600.000000 5644.000000 1526.000000 2336.000000 6110.000000 5095.000000 2065.000000 1064.000000 5642.000000 2207.000000 1488.000000 1424.000000 742.000000 1012.000000 508.000000 576.000000 800.000000 17000.000000 2010.000000

The dataset contains houses constructed between 1872 and 2010, with a significant proportion built after 2000. Most remodeling activities were concentrated around 2004. The median house age at the time of sale is approximately 35 years.
Sale prices range from $34,900 to $755,000, with a median price of $163,000, indicating substantial variability and the presence of high-end properties.
The median above-ground living area (GrLivArea) is 1,464 square feet, though the distribution exhibits strong variability with several large outliers. Many houses lack certain basement features, resulting in zero-inflated distributions for some basement-related variables. Outdoor and luxury amenities such as PoolArea, 3SsnPorch, EnclosedPorch, and MiscVal are relatively sparse, as they are present only in a small subset of properties.

Barplots

MSSubClass MSZoning Street Alley LotShape LandContour Utilities LotConfig LandSlope Neighborhood Condition1 Condition2 BldgType HouseStyle OverallQual OverallCond RoofStyle RoofMatl Exterior1st Exterior2nd MasVnrType ExterQual ExterCond Foundation BsmtQual BsmtCond BsmtExposure BsmtFinType1 BsmtFinType2 Heating HeatingQC CentralAir Electrical BsmtFullBath BsmtHalfBath FullBath HalfBath BedroomAbvGr KitchenAbvGr KitchenQual TotRmsAbvGrd Functional Fireplaces FireplaceQu GarageType GarageFinish GarageCars GarageQual GarageCond PavedDrive PoolQC Fence MiscFeature MoSold SaleType SaleCondition
count 2919 2919 2919 2919 2919 2919 2919 2919 2919 2919 2919 2919 2919 2919 2919 2919 2919 2919 2919 2919 2919 2919 2919 2919 2919 2919 2919 2919 2919 2919 2919 2919 2919 2919 2919 2919 2919 2919 2919 2919 2919 2919 2919 2919 2919 2919 2919 2919 2919 2919 2919 2919 2919 2919 2919 2919
unique 16 5 2 2 4 4 2 5 3 25 9 8 5 8 10 9 6 8 15 16 5 4 5 6 4 4 4 6 6 6 5 2 5 4 3 5 3 8 4 4 14 7 5 5 6 3 6 5 5 3 3 4 4 12 9 6
top 20 RL Pave Grvl Reg Lvl AllPub Inside Gtl NAmes Norm Norm 1Fam 1Story 5 5 Gable CompShg VinylSd VinylSd None TA TA PConc TA TA No Unf Unf GasA Ex Y SBrkr 0 0 2 0 3 1 TA 6 Typ 0 Gd Attchd Unf 2 TA TA Y Ex MnPrv Shed 6 WD Normal
freq 1079 2269 2907 2841 1859 2622 2918 2133 2778 443 2511 2889 2425 1471 825 1645 2310 2876 1026 1015 1742 1798 2538 1308 1364 2688 1986 930 2573 2874 1493 2723 2672 1707 2744 1530 1834 1596 2785 1493 844 2719 1420 2164 1880 1389 1595 2763 2813 2641 2913 2677 2909 503 2526 2402

A typical house in the dataset is located in a Residential Low Density (RL) zone and is a single-family (1Fam) property. Most homes are one-story structures with a gable roof and a poured concrete foundation. Heating is typically provided by a gas forced warm air furnace (GasA), and the majority of houses are equipped with central air conditioning. Nearly all properties have paved road access, are connected to public utilities and sewage systems (AllPub), and are situated on flat or gently sloped terrain.

Average Sale Price and Total Amount of Sales for Every Year

Average prices increased from 2006 to a peak in 2007, followed by a sharp decline in 2008. A partial recovery is observed in 2009, with prices decreasing again in 2010.

The total number of sales increases from 2006 to 2007, declines in 2008, and then reaches its highest level in 2009. A substantial drop in the number of sales is observed in 2010.

This noticeable volatility is timed closely with the global economic crisis of 2008.

Correlation of Variables with SalePrice

To explore the relationship between numerical features and the target variable SalePrice, scatterplots are used to visualize potential correlations. These plots allow the identification of linear or nonlinear trends between each numerical predictor and the sale price.
Positive or negative correlations may. be observed as diagonally. increasing or decreasing patterns in the scatter plots, while weak or no correlation is indicated by a more diffuse or random distribution of points.

Scatterplots:

Variables ‘LotFrontage’, ‘BSmtFinSF1’, ‘TotalBsmtSF’, ‘1stFlrSF’, ‘2ndFlrSF’, ‘GrLivArea’, ‘GarageArea’ seem to have considerable correlation.

I am going to use median house prices in correlation with ‘year’ variables - ‘YearBuilt’, ‘YearRemodAdd’, ‘GarageYrBlt’ to reduce the influence of extreme values and outliers.

Median sales for ‘year’ variables:

All ‘year’ variables showed a good, positive correlation with SalePrice.

I used box plots to examine the relationship between categorical variables and the target variable ‘SalePrice’. Boxplots provide a concise summary of the distribution of sale prices within each category, allowing for comparison of medians, variability, and the presence of outliers across groups.

Boxplots:

The box plots indicated that variables related to material quality, overall condition, number of rooms, number of fireplaces, and garage capacity are strongly associated with ‘SalePrice’.

Feature Selection

Features chosen by analysis of supposed low correlation in plots: ‘Alley’, ‘LotShape’, ‘LandContour’, ‘Utilities’, ‘LotConfig’, ‘LandSlope’, ‘Condition2’, ‘BldgType’, ‘HouseStyle’, ‘RoofStyle’, ‘BedroomAbvGr’, ‘KitchenAbvGr’, ‘BsmtFinType2’, ‘BsmtFinSF2’, ‘BsmtUnfSF’, ‘LowQualFinSF’, ‘Exterior1st’, ‘Exterior2nd’, ‘BsmtFullBath’, ‘BsmtHalfBath’,‘Electrical’, ‘Functional’, ‘WoodDeckSF’, ‘OpenPorchSF’, ‘EnclosedPorch’, ‘3SsnPorch’, ‘ScreenPorch’, ‘PoolArea’, ‘PoolQC’, ‘Fence’, ‘MiscVal’, ‘MoSold’. These features were tested by the model to prove their ability to lower the correction of the prediction model.

Feature selection:

['Alley',
 'LotShape',
 'LandContour',
 'Utilities',
 'LotConfig',
 'LandSlope',
 'Condition2',
 'BldgType',
 'HouseStyle',
 'RoofStyle',
 'BedroomAbvGr',
 'KitchenAbvGr',
 'Functional',
 'Exterior1st',
 'Exterior2nd',
 'Fence',
 'MiscVal',
 'MiscFeature',
 'MoSold']

Why these features increase or decrease model accuracy?

Feature selection was performed iteratively, removing low-signal variables and validating each change using cross-validation. Features were retained only if their removal improved or maintained generalization performance. Features such as ‘Alley’, ‘Fence’, ‘MiscFeature’, and ‘MiscVal’ contain a high proportion of missing values, were found to be weak predictors. Similarly, features including ‘LotShape’, ‘LandContour’, ‘LotConfig’, ‘LandSlope’, and ‘Condition2’ seem to be rather irrelevant for prediction. Several structurally relevant variables, such as ‘BldgType’, ‘HouseStyle’, ‘BedroomAbvGr’, ‘KitchenAbvGr’, ‘Utilities’, ‘Functional’, ‘Exterior1st’, and ‘Exterior2nd’, also demonstrate limited contribution. Their predictive effect appears to be largely captured by stronger correlated variables such as ‘GrLivArea’, ‘TotRmsAbvGrd’, and ‘OverallQual’. ‘MoSold’ shows only a weak seasonal influence on pricing. In contrast, variables (‘BsmtFinType2’, ‘BsmtFinSF2’, ‘BsmtUnfSF’, ‘LowQualFinSF’, ‘BsmtFullBath’, ‘BsmtHalfBath’) provide meaningful information about usable living space and structural value. Outdoor and amenity features (‘WoodDeckSF’, ‘OpenPorchSF’, ‘EnclosedPorch’, ‘3SsnPorch’, ‘ScreenPorch’, ‘PoolArea’, ‘PoolQC’), although sparse, are associated with higher-end properties and contributed positively to predictive performance.

Ridge Model

I implemented a Ridge regression model using a fully pipeline-based preprocessing workflow, including log transformation of the target, imputation, scaling, one-hot encoding, skew correction, and custom feature engineering.
Ridge regression is the regular linear regression with a built-in “brake pedal.” When a model tries too hard to perfectly fit the training data, it can give some features very large weights. That often leads to overfitting — great performance on training data, worse performance on new data.
Ridge adds a penalty for large weights. It gently pushes all coefficients to be smaller, making the model more stable and less sensitive to noise.

Fitting model:

Pipeline(steps=[('fe', FeatureEngineer()),
                ('skew',
                 SkewLog1p(exclude=('YearBuilt', 'YearRemodAdd', 'GarageYrBlt',
                                    'YrSold', 'MoSold'))),
                ('preprocessor',
                 ColumnTransformer(transformers=[('num',
                                                  Pipeline(steps=[('imputer',
                                                                   SimpleImputer(strategy='median')),
                                                                  ('scaler',
                                                                   StandardScaler())]),
                                                  <sklearn.compose._column_transformer.make_column_selector object at 0x0000027F001F8D50>),
                                                 ('cat',
                                                  Pipeline(steps=[('imputer',
                                                                   SimpleImputer(strategy='most_frequent')),
                                                                  ('onehot',
                                                                   OneHotEncoder(handle_unknown='ignore'))]),
                                                  <sklearn.compose._column_transformer.make_column_selector object at 0x0000027F77B37050>)])),
                ('regressor', Ridge(alpha=0.001))])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

The model is evaluated on the validation set using RMSE and R² to measure prediction accuracy and explanatory power. RMSE measures the average size of prediction errors. R² measures how much of the target variance the model explains.

RMSE and R²:

Validation RMSE (log scale): 0.117803
Validation R²: 0.925634

RMSE ≈ 0.118
R² ≈ 0.925

This means: 1. Errors are relatively small 2. The model explains most price variation 3. Strong linear baseline performance

Final Project Summary:

The goal of this project was to predict house sale prices of the Ames Housing dataset, applying preprocessing, EDA, feature pruning, feature engineering, regularization, and cross-validation to build a stable and interpretable regression model.
The final model achieves performance close to the practical ceiling for regularized linear models on this dataset. However, the results highlight that careful feature examination, domain understanding, and thoughtful feature selection remain critical for maximizing predictive performance.

End

Data Description

MSSubClass: Identifies the type of dwelling involved in the sale.

    20  1-STORY 1946 & NEWER ALL STYLES
    30  1-STORY 1945 & OLDER
    40  1-STORY W/FINISHED ATTIC ALL AGES
    45  1-1/2 STORY - UNFINISHED ALL AGES
    50  1-1/2 STORY FINISHED ALL AGES
    60  2-STORY 1946 & NEWER
    70  2-STORY 1945 & OLDER
    75  2-1/2 STORY ALL AGES
    80  SPLIT OR MULTI-LEVEL
    85  SPLIT FOYER
    90  DUPLEX - ALL STYLES AND AGES
   120  1-STORY PUD (Planned Unit Development) - 1946 & NEWER
   150  1-1/2 STORY PUD - ALL AGES
   160  2-STORY PUD - 1946 & NEWER
   180  PUD - MULTILEVEL - INCL SPLIT LEV/FOYER
   190  2 FAMILY CONVERSION - ALL STYLES AND AGES

MSZoning: Identifies the general zoning classification of the sale.

   A    Agriculture
   C    Commercial
   FV   Floating Village Residential
   I    Industrial
   RH   Residential High Density
   RL   Residential Low Density
   RP   Residential Low Density Park 
   RM   Residential Medium Density

LotFrontage: Linear feet of street connected to property

LotArea: Lot size in square feet

Street: Type of road access to property

   Grvl Gravel  
   Pave Paved

Alley: Type of alley access to property

   Grvl Gravel
   Pave Paved
   NA   No alley access

LotShape: General shape of property

   Reg  Regular 
   IR1  Slightly irregular
   IR2  Moderately Irregular
   IR3  Irregular

LandContour: Flatness of the property

   Lvl  Near Flat/Level 
   Bnk  Banked - Quick and significant rise from street grade to building
   HLS  Hillside - Significant slope from side to side
   Low  Depression

Utilities: Type of utilities available

   AllPub   All public Utilities (E,G,W,& S)    
   NoSewr   Electricity, Gas, and Water (Septic Tank)
   NoSeWa   Electricity and Gas Only
   ELO  Electricity only

LotConfig: Lot configuration

   Inside   Inside lot
   Corner   Corner lot
   CulDSac  Cul-de-sac
   FR2  Frontage on 2 sides of property
   FR3  Frontage on 3 sides of property

LandSlope: Slope of property

   Gtl  Gentle slope
   Mod  Moderate Slope  
   Sev  Severe Slope

Neighborhood: Physical locations within Ames city limits

   Blmngtn  Bloomington Heights
   Blueste  Bluestem
   BrDale   Briardale
   BrkSide  Brookside
   ClearCr  Clear Creek
   CollgCr  College Creek
   Crawfor  Crawford
   Edwards  Edwards
   Gilbert  Gilbert
   IDOTRR   Iowa DOT and Rail Road
   MeadowV  Meadow Village
   Mitchel  Mitchell
   Names    North Ames
   NoRidge  Northridge
   NPkVill  Northpark Villa
   NridgHt  Northridge Heights
   NWAmes   Northwest Ames
   OldTown  Old Town
   SWISU    South & West of Iowa State University
   Sawyer   Sawyer
   SawyerW  Sawyer West
   Somerst  Somerset
   StoneBr  Stone Brook
   Timber   Timberland
   Veenker  Veenker

Condition1: Proximity to various conditions

   Artery   Adjacent to arterial street
   Feedr    Adjacent to feeder street   
   Norm Normal  
   RRNn Within 200' of North-South Railroad
   RRAn Adjacent to North-South Railroad
   PosN Near positive off-site feature--park, greenbelt, etc.
   PosA Adjacent to postive off-site feature
   RRNe Within 200' of East-West Railroad
   RRAe Adjacent to East-West Railroad

Condition2: Proximity to various conditions (if more than one is present)

   Artery   Adjacent to arterial street
   Feedr    Adjacent to feeder street   
   Norm Normal  
   RRNn Within 200' of North-South Railroad
   RRAn Adjacent to North-South Railroad
   PosN Near positive off-site feature--park, greenbelt, etc.
   PosA Adjacent to postive off-site feature
   RRNe Within 200' of East-West Railroad
   RRAe Adjacent to East-West Railroad

BldgType: Type of dwelling

   1Fam Single-family Detached  
   2FmCon   Two-family Conversion; originally built as one-family dwelling
   Duplx    Duplex
   TwnhsE   Townhouse End Unit
   TwnhsI   Townhouse Inside Unit

HouseStyle: Style of dwelling

   1Story   One story
   1.5Fin   One and one-half story: 2nd level finished
   1.5Unf   One and one-half story: 2nd level unfinished
   2Story   Two story
   2.5Fin   Two and one-half story: 2nd level finished
   2.5Unf   Two and one-half story: 2nd level unfinished
   SFoyer   Split Foyer
   SLvl Split Level

OverallQual: Rates the overall material and finish of the house

   10   Very Excellent
   9    Excellent
   8    Very Good
   7    Good
   6    Above Average
   5    Average
   4    Below Average
   3    Fair
   2    Poor
   1    Very Poor

OverallCond: Rates the overall condition of the house

   10   Very Excellent
   9    Excellent
   8    Very Good
   7    Good
   6    Above Average   
   5    Average
   4    Below Average   
   3    Fair
   2    Poor
   1    Very Poor

YearBuilt: Original construction date

YearRemodAdd: Remodel date (same as construction date if no remodeling or additions)

RoofStyle: Type of roof

   Flat Flat
   Gable    Gable
   Gambrel  Gabrel (Barn)
   Hip  Hip
   Mansard  Mansard
   Shed Shed

RoofMatl: Roof material

   ClyTile  Clay or Tile
   CompShg  Standard (Composite) Shingle
   Membran  Membrane
   Metal    Metal
   Roll Roll
   Tar&Grv  Gravel & Tar
   WdShake  Wood Shakes
   WdShngl  Wood Shingles

Exterior1st: Exterior covering on house

   AsbShng  Asbestos Shingles
   AsphShn  Asphalt Shingles
   BrkComm  Brick Common
   BrkFace  Brick Face
   CBlock   Cinder Block
   CemntBd  Cement Board
   HdBoard  Hard Board
   ImStucc  Imitation Stucco
   MetalSd  Metal Siding
   Other    Other
   Plywood  Plywood
   PreCast  PreCast 
   Stone    Stone
   Stucco   Stucco
   VinylSd  Vinyl Siding
   Wd Sdng  Wood Siding
   WdShing  Wood Shingles

Exterior2nd: Exterior covering on house (if more than one material)

   AsbShng  Asbestos Shingles
   AsphShn  Asphalt Shingles
   BrkComm  Brick Common
   BrkFace  Brick Face
   CBlock   Cinder Block
   CemntBd  Cement Board
   HdBoard  Hard Board
   ImStucc  Imitation Stucco
   MetalSd  Metal Siding
   Other    Other
   Plywood  Plywood
   PreCast  PreCast
   Stone    Stone
   Stucco   Stucco
   VinylSd  Vinyl Siding
   Wd Sdng  Wood Siding
   WdShing  Wood Shingles

MasVnrType: Masonry veneer type

   BrkCmn   Brick Common
   BrkFace  Brick Face
   CBlock   Cinder Block
   None None
   Stone    Stone

MasVnrArea: Masonry veneer area in square feet

ExterQual: Evaluates the quality of the material on the exterior

   Ex   Excellent
   Gd   Good
   TA   Average/Typical
   Fa   Fair
   Po   Poor

ExterCond: Evaluates the present condition of the material on the exterior

   Ex   Excellent
   Gd   Good
   TA   Average/Typical
   Fa   Fair
   Po   Poor

Foundation: Type of foundation

   BrkTil   Brick & Tile
   CBlock   Cinder Block
   PConc    Poured Contrete 
   Slab Slab
   Stone    Stone
   Wood Wood

BsmtQual: Evaluates the height of the basement

   Ex   Excellent (100+ inches) 
   Gd   Good (90-99 inches)
   TA   Typical (80-89 inches)
   Fa   Fair (70-79 inches)
   Po   Poor (<70 inches
   NA   No Basement

BsmtCond: Evaluates the general condition of the basement

   Ex   Excellent
   Gd   Good
   TA   Typical - slight dampness allowed
   Fa   Fair - dampness or some cracking or settling
   Po   Poor - Severe cracking, settling, or wetness
   NA   No Basement

BsmtExposure: Refers to walkout or garden level walls

   Gd   Good Exposure
   Av   Average Exposure (split levels or foyers typically score average or above)  
   Mn   Mimimum Exposure
   No   No Exposure
   NA   No Basement

BsmtFinType1: Rating of basement finished area

   GLQ  Good Living Quarters
   ALQ  Average Living Quarters
   BLQ  Below Average Living Quarters   
   Rec  Average Rec Room
   LwQ  Low Quality
   Unf  Unfinshed
   NA   No Basement

BsmtFinSF1: Type 1 finished square feet

BsmtFinType2: Rating of basement finished area (if multiple types)

   GLQ  Good Living Quarters
   ALQ  Average Living Quarters
   BLQ  Below Average Living Quarters   
   Rec  Average Rec Room
   LwQ  Low Quality
   Unf  Unfinshed
   NA   No Basement

BsmtFinSF2: Type 2 finished square feet

BsmtUnfSF: Unfinished square feet of basement area

TotalBsmtSF: Total square feet of basement area

Heating: Type of heating

   Floor    Floor Furnace
   GasA Gas forced warm air furnace
   GasW Gas hot water or steam heat
   Grav Gravity furnace 
   OthW Hot water or steam heat other than gas
   Wall Wall furnace

HeatingQC: Heating quality and condition

   Ex   Excellent
   Gd   Good
   TA   Average/Typical
   Fa   Fair
   Po   Poor

CentralAir: Central air conditioning

   N    No
   Y    Yes

Electrical: Electrical system

   SBrkr    Standard Circuit Breakers & Romex
   FuseA    Fuse Box over 60 AMP and all Romex wiring (Average) 
   FuseF    60 AMP Fuse Box and mostly Romex wiring (Fair)
   FuseP    60 AMP Fuse Box and mostly knob & tube wiring (poor)
   Mix  Mixed

1stFlrSF: First Floor square feet

2ndFlrSF: Second floor square feet

LowQualFinSF: Low quality finished square feet (all floors)

GrLivArea: Above grade (ground) living area square feet

BsmtFullBath: Basement full bathrooms

BsmtHalfBath: Basement half bathrooms

FullBath: Full bathrooms above grade

HalfBath: Half baths above grade

Bedroom: Bedrooms above grade (does NOT include basement bedrooms)

Kitchen: Kitchens above grade

KitchenQual: Kitchen quality

   Ex   Excellent
   Gd   Good
   TA   Typical/Average
   Fa   Fair
   Po   Poor

TotRmsAbvGrd: Total rooms above grade (does not include bathrooms)

Functional: Home functionality (Assume typical unless deductions are warranted)

   Typ  Typical Functionality
   Min1 Minor Deductions 1
   Min2 Minor Deductions 2
   Mod  Moderate Deductions
   Maj1 Major Deductions 1
   Maj2 Major Deductions 2
   Sev  Severely Damaged
   Sal  Salvage only

Fireplaces: Number of fireplaces

FireplaceQu: Fireplace quality

   Ex   Excellent - Exceptional Masonry Fireplace
   Gd   Good - Masonry Fireplace in main level
   TA   Average - Prefabricated Fireplace in main living area or Masonry Fireplace in basement
   Fa   Fair - Prefabricated Fireplace in basement
   Po   Poor - Ben Franklin Stove
   NA   No Fireplace

GarageType: Garage location

   2Types   More than one type of garage
   Attchd   Attached to home
   Basment  Basement Garage
   BuiltIn  Built-In (Garage part of house - typically has room above garage)
   CarPort  Car Port
   Detchd   Detached from home
   NA   No Garage

GarageYrBlt: Year garage was built

GarageFinish: Interior finish of the garage

   Fin  Finished
   RFn  Rough Finished  
   Unf  Unfinished
   NA   No Garage

GarageCars: Size of garage in car capacity

GarageArea: Size of garage in square feet

GarageQual: Garage quality

   Ex   Excellent
   Gd   Good
   TA   Typical/Average
   Fa   Fair
   Po   Poor
   NA   No Garage

GarageCond: Garage condition

   Ex   Excellent
   Gd   Good
   TA   Typical/Average
   Fa   Fair
   Po   Poor
   NA   No Garage

PavedDrive: Paved driveway

   Y    Paved 
   P    Partial Pavement
   N    Dirt/Gravel

WoodDeckSF: Wood deck area in square feet

OpenPorchSF: Open porch area in square feet

EnclosedPorch: Enclosed porch area in square feet

3SsnPorch: Three season porch area in square feet

ScreenPorch: Screen porch area in square feet

PoolArea: Pool area in square feet

PoolQC: Pool quality

   Ex   Excellent
   Gd   Good
   TA   Average/Typical
   Fa   Fair
   NA   No Pool

Fence: Fence quality

   GdPrv    Good Privacy
   MnPrv    Minimum Privacy
   GdWo Good Wood
   MnWw Minimum Wood/Wire
   NA   No Fence

MiscFeature: Miscellaneous feature not covered in other categories

   Elev Elevator
   Gar2 2nd Garage (if not described in garage section)
   Othr Other
   Shed Shed (over 100 SF)
   TenC Tennis Court
   NA   None

MiscVal: $Value of miscellaneous feature

MoSold: Month Sold (MM)

YrSold: Year Sold (YYYY)

SaleType: Type of sale

   WD   Warranty Deed - Conventional
   CWD  Warranty Deed - Cash
   VWD  Warranty Deed - VA Loan
   New  Home just constructed and sold
   COD  Court Officer Deed/Estate
   Con  Contract 15% Down payment regular terms
   ConLw    Contract Low Down payment and low interest
   ConLI    Contract Low Interest
   ConLD    Contract Low Down
   Oth  Other

SaleCondition: Condition of sale

   Normal   Normal Sale
   Abnorml  Abnormal Sale -  trade, foreclosure, short sale
   AdjLand  Adjoining Land Purchase
   Alloca   Allocation - two linked properties with separate deeds, typically condo with a garage unit  
   Family   Sale between family members
   Partial  Home was not completed when last assessed (associated with New Homes)