Suppose you have a house. And you want to sell it. Through House Price Prediction project you can predict the price from previous sell history. And we make this prediction using Machine Learning.

1. House Price Prediction
Group Name: Bug Free
9/6/2019 1
Group members:
1.Nahian Ahmed
2.Sajibul Hasan
3.Tariqul Islam
4.Monsur Ahmed
5.Hazera Akter

2. Sajibul Hasan
ID : 151-15-4986

3. Introduction
Suppose you have a house. And you want to sell it.
Through House Price Prediction project you can
predict the price from previous sell history.
And we make this prediction using Machine
Learning.

4. Machine Learning
Machine learning is a subset of AI. Which is
more data oriented and use statistics methods
on the data to discover information.
ML categories:
1.Supervised learning(which we used)
2. Unsupervised learning
3. Reinforcement Learning

5. Supervised learning
In supervised learning, each example is a pair
consisting of an input object (typically a vector)
and a desired output value (also called the
supervisory signal)
*that means we know about the data and its
feature

6. Supervised learning

7. Monsur Ahmed
ID : 151-15-5439

8. Linear Regression
In statistics, linear regression is
a linear approach for modeling the relationship
between a dependent variable y and a more
explanatory (Independent) variables X.
Formula:
Y=b0 +b1X
Y=Dependent Variable
b0=Intercept, b1 =Slope, X=Independent Variable

9. Linear Regression

10. Linear Regression

11. Linear Regression

12. Linear Regression

13. Linear Regression

14. Linear Regression

15. Linear Regression

16. Tariqul Islam
ID : 151-15-5144

17. Linear Regression

18. Linear Regression

19. Linear Regression

20. Linear Regression

21. Linear Regression

22. Linear Regression

23. Nahian Ahmed
ID : 151-15-5137

24. Linear Regression in Python
Import module:
from sklearn import linear_model
Making Object:
reg=linear_model.LinearRegression()
Fit data:
reg.fit(house_size,Price_sft)

25. Linear Regression in Python
Slope:
b1 =reg.coef_[0]
Intercept:
b0=reg.intercept_

26. Linear Regression in Python
X=4500 Sq/ft. and the
slope= 0.082 intercept= 828.63 and the Y will
be :
Y = b0 (828.63)+b1(0.082)*X(4500)
Y=1198.92

27. Hazera Akter
ID : 151-15-4819

28. DATA VISUALIZATION
For Data Visualization we used matplotlib:
pyplot.plot(house_size,Price_sft,'ro')

29. DATA VISUALIZATION
Data point after prediction
pyplot.plot(X,ans,'bs')

30. Limitation
• Less data
• Less feature in data
• Linear Regression( not PCA Dimensionality
Reduction )

31. Project Video

32. Thank You