final report database systems topic database systems for boston house price prediction

Group 9: Database Systems for Boston House Price prediction11/01/2023VIETNAM NATIONAL UNIVERSITY, HANOIINTERNATIONAL SCHOOL---*****---FINAL REPORT: DATABASE SYSTEMSTopic: Database System

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Group 9: Database Systems for Boston House Price prediction11/01/2023

VIETNAM NATIONAL UNIVERSITY, HANOIINTERNATIONAL SCHOOL -***** -

FINAL REPORT: DATABASE SYSTEMS

Topic: Database Systems for Boston House Price Prediction

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Member whose contributions each member of the group :

Relational schema,Retrieving the database,

Make 10 question

Relational schema,Insert real sample data

Inserting real sampledata

Create physicaldatabase

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Table of contents

I Introduction 4

II Data dictionary 4

III Analyzing and draw the ERD diagram 6

IV Relational Schema 8

V Build a database using SQL Sever 9

VI Business Questions 14

VII Conclusion: 18

VIII Reference 18

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Housing is one of the most basic demands of human life, along with food, water, andother necessities As people's living circumstances improved, demand for housing increased rapidly Housing markets have a favorable impact on a country's currency, whichis a significant factor in the national economy Numerous factors influence housing sales prices, including the size of the property, its location, the materials used in construction, the age of the property, the number of bedrooms and garages, and so on.

A house-price prediction model can provide numerous benefits to home purchasers, property investors, and home builders This model may provide a wealth of information and expertise to home purchasers, property investors, and home builders, such as the valuation of current market house prices, which will assist them in determining house pricing Meanwhile, this model can assist potential purchasers in determining the features of a property that are appropriate for their budget.

In this project, we will develop and evaluate the performance and the predictive power of a model trained and tested on data collected from houses in Boston’s suburbs Once we get a good fit, we will use this model to predict the monetary value of a house located at the Boston’s area A model like this would be very valuable for a real state agentwho could make use of the information provided on a daily basis.

II.Data dictionary:

The dataset used in this project comes from the UCI Machine Learning Repository This data was collected in 1978 and each of the 506 entries represents aggregate information about 13 features of homes from various suburbs located in Bostonand some data about buyer, seller and investor information is generated by us from collecting information on the internet And here is 13 detailed attribute information can befound below:

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Attribute Information:

- CRIM: Per capita crime rate by town

- ZN: Proportion of residential land zoned for lots over 25,000 sq.ft.

- INDUS: Proportion of non-retail business acres per town

- CHAS: Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)

- NOX: Nitric Oxide concentration (parts per 10 million)

- RM: The average number of rooms per dwelling

- AGE: Proportion of owner-occupied units built before 1940

- DIS: Weighted distances to five Boston employment centers

- RAD: Index of accessibility to radial highways

- TAX: Full-value property-tax rate per 10,000 dollars

- PTRATIO: Pupil-teacher ratio by town

- LSTAT: % lower status of the population

- MEDV: Median value of owner-occupied homes in 1000 dollars

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III.Analyzing and draw the ERD diagram

The entity relationship diagram can be thought of as the database's design sketch ERD provides visualization for database design, hence it serves the following functions:

- Supports in the definition of information system requirements across the organization and assists users in planning how to organize data It facilitates planningbefore beginning to build the tables.

- The ERD diagram can be used as a document to help others comprehend the database's core.

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- The ERD diagram depicts the database's logical structure so that users canunderstand it.

- Once the relational database has been deployed, the ERD can still be used as a reference point if the debug or business process needs to be re-established later.

Analyzing the entities:

+ Property: the property table includes the address, number of floors, year of construction, area of 1 property, and the ID attached to each property.

+ Person: the person table plays the role of managing the properties, through the propertyID and it is divided into 3 main categories (Seller, Customer, Investor) through the ID of the table role

+ Roles: role table for information about types of people (Seller, Customer,Investor)

+ Status: status table to view the status of the property based on the ID of the table status (sold, on sale, fixing)

+ HousePrices: house price list for sale date and original selling price of the property via propertyID

+ MarketData: provides information about the real estate market by address and date and at each time there is a main keyword, MarketDataID

+ Prediction: provide the property's predicted date and price via the propertyID and the MarketData table influenced prediction table via the MarketDataID

+ PropertyType: this table for found property's classification (Villa, Apartment, Cabin, Penthouse) via TypeID

+ Interior: this table shows the interior of each property based on the PropertyID.

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IV.Relational Schema:

The Relational Schema is generated from the ERD, displaying the table elements that correspond to the entities and providing table designers in SQL server with a more detailed perspective of the table implementation.

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V.Build a database using SQL Sever1 Create Database:

- Create and Using database:

- Create table Role

Create table Role 1

CREATE TABLE Role (

roleID int IDENTITY( ,) NOT NULL, set roleID as PK

roleName varchar(30) NOT NULL,

CONSTRAINT [PK_Role] PRIMARY KEY CLUSTERED set roleID as PK

- Create table Status

Create table Status 2

CREATE TABLE Status (

statusID int IDENTITY( ,) NOT NULL, set statusID as PK

statusName varchar(30) NOT NULL,

CONSTRAINT [PK_Status] PRIMARY KEY CLUSTERED set personID as PK

- Create table PropertyType 3

Create table PropertyType 3

CREATE TABLE PropertyType (

typeID int IDENTITY( ,) NOT NULL, set typeID as PK

typeName varchar(30) NOT NULL

CONSTRAINT [PK_PropertyType] PRIMARY KEY CLUSTERED set personID as PK

(

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)WITH PAD_INDEX (= OFF, STATISTICS_NORECOMPUTE = OFF, IGNORE_DUP_KEY= OFF, ALLOW_ROW_LOCKS = ON, ALLOW_PAGE_LOCKS = ON) ON [PRIMARY]

- Create table MarketData 4

Create table MartketData 4

CREATE TABLE MarketData (

marketDataID int IDENTITY( ,) NOT NULL,date date NOT NULL,

address varchar(100) NOT NULL,

CRIM float NOT NULL,

ZN float NOT NULL,

INDUS float NOT NULL,

CHAS bit NOT NULL,

NOX float NOT NULL,

RM float NOT NULL,

AGE float NOT NULL,

DIS float NOT NULL,

RAD int NOT NULL,

TAX int NOT NULL,

PTRATIO float NOT NULL,

LSTAT float NOT NULL,

MEDV float NOT NULL,

CONSTRAINT [PK_MarketData] PRIMARY KEY CLUSTERED set marketDataIDas PK

- Create table Person

Create table Person 5

CREATE TABLE Person (

personID int IDENTITY( ,) NOT NULL,name varchar(50) NOT NULL,

phone varchar(11) NOT NULL, address varchar(100) NOT NULL,

gender bit NOT NULL,

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- Create table Property

Create table Property 6

CREATE TABLE Property (

propertyID int IDENTITY( ,) NOT NULL, set propertyID as PK

squareFootage decimal(10 4,) NOT NULL,

floor int NOT NULL,

yearBuilt date NOT NULL,address varchar(100) NOT NULL,

saleDate date NOT NULL,

salePrice decimal( ,) NOT NULL,

typeID int FOREIGN KEY REFERENCES PropertyType typeID() NOT NULL,

statusID int FOREIGN KEY REFERENCES Status(statusID) NOT NULL,

personID int FOREIGN KEY REFERENCES Person personID() NOT NULL, CONSTRAINT [PK_Property] PRIMARY KEY CLUSTERED set personID as PK

-Create table Interior

Create table Interior 7

CREATE TABLE Interior (

propertyID int IDENTITY( ,) NOT NULL, set propertyID as PK

numbedRooms int NOT NULL,

numBathrooms int NOT NULL,

kitchen bit NOT NULL,pool bit NOT NULL,

garden bit NOT NULL,

garage bit NOT NULL,

CONSTRAINT FK_Property FOREIGN KEY (propertyID) REFERENCES

Property propertyID(), set propertyID as FK for table Property(propertyID)

CONSTRAINT [PK_Interior] PRIMARY KEY CLUSTERED set personID as PK

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- Create table Prediction

Create table Prediction 8

CREATE TABLE Prediction (

propertyID int NOT NULL, set propertyID as PK

marketDataID int NOT NULL set marketDataID as PK

CONSTRAINT FK_MarketData FOREIGN KEY (marketDataID) REFERENCES

MarketData marketDataID(), set marketDataID as FK for table MarketData(marktDataID),

CONSTRAINT FK_Prediction FOREIGN KEY (propertyID) REFERENCES

Property propertyID(), set marketDataID as FK for table MarketData(marktDataID),

predictionDate date NOT NULL,

predictionPrice decimal( ,) NOT NULL,

CONSTRAINT [PK_Perediction] PRIMARY KEY CLUSTERED set personID asPK

propertyID, marketDataID ASC

2 Insert Table :

-INSERT INTO Role

INSERT INTO Role VALUES('Seller');INSERT INTO Role VALUES('Customer');INSERT INTO Role VALUES('Investor');GO

-INSERT INTO Status

INSERT INTO Status VALUES('On Sale');INSERT INTO Status VALUES('Sold');INSERT INTO Status VALUES('Fixing');

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-INSERT INTO PropertyType

INSERT INTO PropertyType VALUES('Villa'); INSERT INTO PropertyType VALUES('Cabin'); INSERT INTO PropertyType VALUES('Apartment');INSERT INTO PropertyType VALUES('Villa');GO

-INSERT INTO Person

INSERT INTO Person VALUES('Andrew Garfield', '08172645781', '408 5thAve, Brooklyn, United States', , 01);

INSERT INTO Person VALUES('Charlie Puth', '04716284625', '3548 SJefferson St #52, Falls Church, United States', , 02);

INSERT INTO Person VALUES('Selena Gomez', '0751827458', '1455 S LambBlvd, Las Vegas, United States', , 03);

-INSERT INTO Property

INSERT INTO Property VALUES(5000, 2, '04/11/2018', '974 Blue HillAvenue, Boston, United States', '01/09/2023', 3012, , , ); 111

INSERT INTO Property VALUES(3670, 1, '06/20/2012', '521 WashingtonSt, Boston, United States', '10/10/2019', 5921, , , 111);

INSERT INTO Property VALUES(2830, 3, '07/10/2015', '415 AmericanLegion Hwy, Boston, United States', '01/07/2020', 5921, , , 221);

-INSERT INTO Interior

INSERT INTO Interior VALUES( , , , , , );3 2 1 1 0 1

-INSERT INTO MarketData

INSERT INTO MarketData VALUES('01/01/2023' 'Blue Hill Avenue, Boston,,United States', 0.00632, 18, 2.31, , 00.538, 6.575, 65.2, 4.09, ,1296, 15.3, 4.98, 24);

INSERT INTO MarketData VALUES('03/01/2023' 'Blue Hill Avenue, Boston,,United States', 0.02731, , 07.07, , 00.469, 6.421, 78.9, 4.9671, ,2

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United States', 0.02729, , 07.07, , 00.469, 7.185, 61.1, 4.9671, ,2242, 17.8, 4.03, 34.7);

-INSERT INTO Prediction

INSERT INTO Prediction VALUES( , ,1 1'01/01/2023', 9992.213); INSERT INTO Prediction VALUES( , ,1 2'03/01/2023', 10000.324);INSERT INTO Prediction VALUES( , ,1 3'06/01/2023', 8823.534);

VI.Business Questions:1.Display all houses in Boston ?

SELECT *FROM Property

2. Print out the houses with the smallest principal amount?

WHERE salePrice = (SELECT MIN(salePrice) FROM Property)

3.Print out houses located in the Adams ?

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WHERE Property.address like '%' + 'Adams' + '%'

4. Find and print the homes with the lowest principal on Blue Hill Avenue ?

WHERE Property.address like '%'+'Blue Hill Avenue'+'%'ORDER BY Property salePrice.

5. Find 10 homes with the lowest predicted price in June 2023?

SELECT TOP 10 *FROM Property

INNER JOIN Prediction ON Property.propertyID = Prediction propertyID.WHERE MONTH(predictionDate) = 6

ORDER BY predictionPrice

6. Find the 10 homes with the lowest predicted prices between September andDecember 2023?

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7.Find homes by status?

CREATE PROCEDURE PropertyStatus @statusName nvarchar(30)AS

INNER JOIN Status ON Property.statusID = Status.statusID

WHERE Status.statusName = @statusName

EXEC PropertyStatus @statusName = 'On Sale'

8.Print out the predicted prices of homes currently for sale in September?

INNER JOIN Status ON Status.statusID = Property statusID.

INNER JOIN Prediction ON Prediction.propertyID = Property propertyID.WHERE Status.statusID = 1 AND MONTH(Prediction predictionDate.) = 9

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9.Print owners of more than 2 property?

SELECT Person.name AS Name, COUNT(propertyID) AS NumberOfProperties

10.Use trigger-tran to suppress unwanted inserts?

CREATE TRIGGER CheckInsert

ON Person

FOR INSERT, UPDATE

ASBEGIN

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VII. Conclusion:

The task of this database system is to help the store data and information in a consistent manner, avoiding redundancy in each specific category By building a database system, customers can easily look up house prices in Boston (quantity, condition, ).

In I, we summarize the importance of housing and the housing market impacts in relation to these important factors In II, we generate Boston housing data to bringthem into the ERD diagram in III From ERD we created Relational Schema and then used SQL statement to build database by using SQL Sever Finally made up 10 questions which can be answered by retrieving in the information from the database.

VIII.Reference

Boston house price prediction | Kaggle

Boston House Price Prediction Using Machine Learning

Machine Learning Project: Predicting Boston House Prices With Regression | by Victor Roman | Towards Data Science

Machine_Leaning_Engineer_Udacity_NanoDegree/projects/boston_housing at master· rromanss23/Machine_Leaning_Engineer_Udacity_NanoDegree · GitHub

Boston Home Prices Prediction and Evaluation | Machine Learning, Deep Learning, and Computer Vision

Boston Housing - Price Prediction

The End