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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

Sajibul Hasan
ID : 151-15-4986

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.

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

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

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

Tariqul Islam
ID : 151-15-5144

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)

Slope:
b1 =reg.coef_[0]
Intercept:
b0=reg.intercept_

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

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

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

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

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House Price Prediction An AI Approach.

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