This document discusses using linear regression to predict house prices based on various parameters. It introduces linear regression and its uses in prediction. It describes collecting a dataset on properties with various attributes and prices to train a linear regression model. The model would then be able to predict house prices for customers based on attributes of the home without requiring an agent. This helps customers avoid being overcharged by agents and invest within their budgets.

1. Predicting House Price
Using
Linear Regression
DIVYA TIWARI
MEIT
TERNA ENGINEERING COLLEGE
Prof. Smita Deshmukh

2. ABSTRACT
• People looking to buy a new home tend to be more conservative with their budgets and market
strategies.
• This project aims to analyze various parameters like avg income, avg area etc. and predict the
house price accordingly.
• For the price prediction we will be using Linear Regression.
• The functioning of this project involves some good amount of dataset on which prediction can be
done.
• This application will help customers to invest in an estate without approaching an agent.
• It also decreases the risk involved in the transaction.

3. INTRODUCTION
• Linear regression is a basic and commonly used type of predictive analysis. The overall idea of
regression is to examine two things:
(1) does a set of predictor variables do a good job in predicting an outcome (dependent) variable?
(2) Which variables in particular are significant predictors of the outcome variable, and in what
way do they–indicated by the magnitude and sign of the beta estimates–impact the outcome variable?
• These regression estimates are used to explain the relationship between one dependent variable and one
or more independent variables.
• Three major uses for regression analysis are:
(1) determining the strength of predictors.
(2) forecasting an effect.
(3) trend forecasting.

4. NEED FOR PROJECT
• To provide a better and fast way of performing operations.
• To provide proper house price to the customers.
• To eliminate need of real estate agent to gain information regarding house prices.
• To provide best price to user without getting cheated.
• To enable user to search home as per the budget.

5. PROBLEM DEFINITION
• It is difficult to estimate the price of a property by manually calculating the affecting parameters
required in estimating the rate of property.
• Customers depending upon the real estate agents get cheated as agent may provide price much
higher than actual price.
• People having budget for buying home are unable to buy because of the difference in prices
provided by agent to them.
• Manual knowledge makes person confused since data may vary person to person.
• In order to eradicate such issues an automated model using Linear Regression is implemented to
provide efficient outcome as per user requirement.

6. PROPOSED SYSTEM
• System includes set of codes that processes on the available dataset to effectively predict the value
of outcome depending upon user input using the concept of Linear Regression.
• Efficient and proper use of the system can eradicate the cases where the customers get cheated by
the real estate agents in terms of house prices.
• Proper usage of model is beneficial to both the customer as well as agents guiding customers.

7. UML DIAGRAMS
• Use case diagrams are used to gather the requirements of a system including internal and external
influences.

8. • Activity diagram is basically a flowchart to represent the flow from one activity to another activity.

9. • Sequence diagram emphasizes on time sequence of messages.

10. • A data-flow diagram (DFD) is a way of representing a flow of a data of a process or a system (usually an
information system). The DFD also provides information about the outputs and inputs of each entity and the
process itself.
DFD level 0(admin)
DFD level 1(admin)

11. DFD level 0(user)
DFD level 1(user)

12. Hardware and Software Requirements
Hardware specification
Processor: i3
RAM: 4 GB or more
Hard disk: 16 GB or more
GPU: 2 GB
Software Specification
Platform: Windows operating system
JupyterLab
Python 3

13. DATA

14. OUTPUT
• Distribution plot: (Data)

15. • Heat map plot:

16. • Value of coefficient:

17. • Scatterplot:

18. CONCLUSION
• Thus, we studied and applied the concept of Linear Regression in real time
implementation so as to ease the life of human.
• Determining the price of property without complete knowledge about the surrounding is
quite riskier for both customer and the seller.
• In order to overcome this problem we have tried to develop application which determines
the price of the property based on various parameters of the surrounding.
• Data provide us with the complete data about the surrounding in the form of dataset.
• Dataset helps to get the insight of the surrounding and machine learning model helps to
predict the price of the property based on the training provided by the dataset.
• We successfully implemented linear regression model to predict the price of the houses.

19. REFERENCES
• Real Estate Price Prediction with Regression and Classification, CS 229 Autumn 2016 Project Final
Report
• Gongzhu Hu, Jinping Wang, and Wenying Feng Multivariate Regression Modelling for Home Value
Estimates with Evaluation using Maximum Information Coefficient
• Byeonghwa Park , Jae Kwon Bae (2015). Using machine learning algorithms for housing price prediction ,
Volume 42, Pages 2928-2934 [4] Douglas C. Montgomery, Elizabeth A. Peck, G. Geoffrey Vining, 2015.
Introduction to Linear Regression Analysis.
• Iain Pardoe, 2008, Modelling Home Prices Using Realtor Data
• Aaron Ng, 2015, Machine Learning for a London Housing Price Prediction Mobile Application
• Wang, X., Wen, J., Zhang, Y.Wang, Y. (2014). Real estate price forecasting based on SVM optimized by
PSO. Optik-International Journal for Light and Electron Optics, 125(3), 14391443.
• Vishal Raman, May 2014. Identifying Customer Interest in Real Estate Using Data Mining.
• http://www.99acres.com/property-rates-and-pricetrendsin-mumbai