This document discusses predicting house sale prices using regression algorithms. It analyzes a dataset of 1460 houses with 81 variables like size, age, and sale price. Missing lot size (LotFrontage) values are filled in using neighborhood averages. Highly correlated variables like size metrics are identified. Models like linear regression, KNN, random forest, and decision trees are trained and tested on scaled data, achieving test R-squared scores from 0.71 to 0.85. Linear regression and random forest performed best.

1. House On Sale -
Prediction of Sales Price
Kexin
liu.kexin22@gmail.com
Module II
ML - Algorithm - Regression

2. Inspect Problems
Avg Price : $180k
Top Sale :
June , 2007
2007 2010
Top Drop :
2009 - 2010
↓50%
Best Style : One Story
Best Building Type : Single-family Detached
Mode
Mean

3. Preprocessing

4. Dataset Info
Total R
Total C
Null
Duplicates
1460
81
6965
0

5. Variable Type
Cat
Nominal
Ordinal
'MSZoning', 'Street', 'Alley', 'LotShape', 'LandContour',
'Utilities','LotConfig', 'LandSlope', 'Neighborhood', 'BldgType',
'HouseStyle', 'RoofStyle','RoofMatl', 'Exterior1st', 'Exterior2nd',
'MasVnrType', 'Foundation', 'BsmtExposure','BsmtFinType1',
'BsmtFinType2', 'Heating', 'CentralAir','Electrical', 'BsmtFullBath',
'BsmtHalfBath', 'FullBath', 'HalfBath','BedroomAbvGr',
'KitchenAbvGr','TotRmsAbvGrd',
'Functional', 'Fireplaces', 'GarageType', 'GarageFinish',
'GarageCars', 'PavedDrive', 'Fence', 'MiscFeature', 'SaleType'
'Condition1', 'Condition2','OverallQual', 'OverallCond',
'ExterQual','ExterCond', 'BsmtQual', 'BsmtCond', 'HeatingQC',
'KitchenQual', 'FireplaceQu', 'GarageQual', 'GarageCond',
'PoolQC','SaleCondition'
Num
Continuous
Discrete
'YearBuilt','YearRemodAdd', 'GarageYrBlt', 'MoSold', 'YrSold',
'MSSubClass', 'LotFrontage', 'LotArea', 'MasVnrArea', 'BsmtFinSF1',
'BsmtFinSF2', 'BsmtUnfSF','TotalBsmtSF', '1stFlrSF', '2ndFlrSF',
'LowQualFinSF', 'GrLivArea','GarageArea', 'WoodDeckSF',
'OpenPorchSF','EnclosedPorch', '3SsnPorch', 'ScreenPorch','PoolArea',
'MiscVal','SalePrice'

6. Missing Value
Cat
Num -
LotFrontage
Fill NaN by String ‘NO’ ✔
Fill NaN by Prediction ?
Fill NaN by Mean ? ✔
Drop NaN column ?

7. Missing Value
Num - LotFrontage
Drop NaN column ?
NO
Linear feet of street
connected to property
Image: https://www.concordma.gov/DocumentCenter/View/1385/Section-6-PDF?bidId=

8. Missing Value
Num - LotFrontage
Fill NaN by Prediction ?
NO
EDA - Correlation
Training Scores from
models
'1stFlrSF','LotArea','GrLivArea','TotalBsmtSF','GarageArea',
'MSSubClass'
Predict 'LogFrontage' with KNN - 0.52
Predict 'LogFrontage' with Linear - 0.40
Predict 'LogFrontage' with RandomForest - 0.59

9. Missing Value
Num - LotFrontage
Fill NaN by Mean ?
Groupby Mean 'Neighborhood','YearBuilt'

10. EDA - Correlation

11. Target - SalesPrice

12. Target - SalesPrice
Top 3 Corr
'GrLivArea', 'GarageArea',
'TotalBsmtSF'

13. Feature Selection -
All Numeric Variables
'GrLivArea',
'GarageArea',
'TotalBsmtSF',
'1stFlrSF',
'YearBuilt',
'YearRemodAdd',
'OpenPorchSF',
'LotArea',
'LotFrontage'

14. Feature Selection -
Categorical Variables

15. Outliers

16. Building Models N

17. TrainTest Info
Total R
Total C
1426
80
Linear
Regression
LR
Scaled
LR
Lasso
Train Test

18. TrainTest Info
Total R
Total C
1426
80
KNN
Random
Forest
Decision
Tree
Train Test

19. Building Models C

20. TrainTest Info
Total R
Total C
1426
48
Linear
Regression
LR
Scaled
LR
Lasso
Train Test

21. TrainTest Info
Total R
Total C
1426
48
KNN
Random
Forest
Decision
Tree
Train Test

22. After Log Transform

23. TrainTest Info
Total R
Total C
1426
48
Linear
Regression
LR
Lasso
Train Test
0.88 0.85
0.38 0.84

24. TrainTest Info
Total R
Total C
1426
48
KNN
Random
Forest
Decision
Tree
Train Test
0.74 0.74
0.75 0.71
0.83 0.71

25. Linear
Regression
Train Test
0.88 0.85
KNN
Train Test
0.74 0.74