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Regression_Project

Riyadh Housing Price Prediction using Linear Regression

By:

  • Naif Albader
  • Yazeed Musallam

Background:

Company info:

iAQAR for real-estate anaylsis.

Problem statement:

Many of real-estate buyers unsure about the value of the land after years, hence; our company iAQAR propose a solution to make predection using regression techinques.

Value of the company and recommendations.

The primarily goal of project to answer the following questions/needs:

  • What is the Housing price in riyadh in the next 10 years?
  • Is the price increasing each year?
  • What is the price growth for each year?

Data Description:

  • Datasets with description:
    This project based on the data availabile on the ministy of justice website:

The land prices are scrapped from: https://www.moj.gov.sa/ar/opendata/bi/birealestate/Dashboards/100_kpiDistrict/101_Monthly/kpi101_04.aspx.

Scope of the work

Sample size:

11 years scrapped (2010 to 2021) worth of data will be used for the analysis, and the reason is to make a robust model for the future predections.

Only riyadh city will be involved in this project.

Description for the datset as num of rows, number of features/columns, names of columns with description:

Description of scrapped data:
The dataset represent the sales deals of lands in riyadh in last 11 years

Number of features: 8 features/Columns

Number of rows: Approx.: 600K rows

Names of columns with description and type:

Field Name Description
Neighborhood Neighborhoods of Riyadh
Scheme Specific Scheme inside a Neighborhood
Land Specific Land inside a Scheme
Date Date of a land sale
Id Unique id for a specific sale
Price (SAR) The total price of a land in SAR
Area (m2) The area of a land in m^2
Price (m2) The price of a land for each m^2

The main technologies and libraries that will be used are: Technologies:

  • Python
  • Jupyter Notebook
  • HTML/CSS
  • Flask

Libraries:

  • Pandas
  • pickle
  • BeautifulSoup and selenium
  • OS
  • Matplotlib
  • Seaborn
  • NumPy
  • Sklearn
  • Category Encoders
  • Tensorflow/keras
  • Plotly

Processing tools:

Google Colab

Note:

During the project analysis, some additional tools may be used.