Linear Regression model for predicting House Price

1. PREDICTING HOUSE PRICES WITH
LINEAR REGRESSION
Presented By PRIYA ROY CHOWDHURY

2. PROBLEM
STATEMEN
T
From the given dataset, we have to
build a model to predict house price
based on different features. Since
house price is a continues variable
so this is a regression problem and
we will implement Linear
Regression to predict the Target
variable (House Price).

3. DATA DESCRIPTION
 We have 81 columns.
 Our Target Variable is SalePrice.
 Id is just an index that we can drop as it’s not required in prediction.
 There are many missing values in this dataset

4. EXPLORATORY DATA ANALYSIS

5. TARGET VARIABLE
• From these plots we can observe
that Target Variable is right skewed.
So by log transformation this can be
converted into normal distribution

6. NORMAL
DISTRIBUTION
TARGET VARIABLE
• After log
transformation we get
a normal distribution
of Target variable.
This fulfills one of the
assumptions of
Linear Regression
model.
• Skew is
0.1213350622052040
6

7. NUMERIC FEATUE ANALYSIS

8. CORRELATION
MATRIX
HEATMAP
• OverallQual,
• GrLivArea,
• GarageCars,
• GarageArea,
• TotalBsmtSF,
• 1stFlrSF
• These are few
features having
strong positive
correlation with
SalePrice¶

9. CATEGORICAL FEATURE
ANALYSIS

10. CONVERT CATEGORICAL TO NUMERIC
USING ONE HOT ENCODER

11. BUILDING A LINEAR
REGRESSION MODEL

12. BUILDING A
LINEAR
REGRESSIO
N MODEL
• Using MinMax scaler for
scaling the data
• Split the given dataset into
train & test dataset in 80:20
ratio
• Target variable is normally
distributed

13. TRAIN DATA
PREDICTION

14. TEST DATA
PREDICTION

15. MODEL
EVALUATION
USING METRICS
Model MAE RMSE
Linear
Regression
0.0638 0.0913

16. CONCLUSION
• People pay more for better
quality(OverallQual)
• People would pay for the more living
area(GrLivArea).
• It seems that house prices decrease with age,
but we need to be surer(YrSold)
• On this analysis, we took some of the
exploratory variables like
OverallQual,GrLivArea,GarageCars,GarageAr
ea,TotalBsmtSF,1stFlrSF .
• These have linear relationship with target
variable.

17. THANK YOU