/House_Price_Prediction

This repository includes a web scraping application, EDA analysis and regression solution for the house prices in Istanbul, Kadıköy.

Primary LanguageJupyter Notebook

House Price Prediction using Regression with Web Scraping BeautifulSoup

This repository includes a web scraping application, EDA analysis and regression solution for the house prices in Istanbul, Kadıköy.

Introduction

Problem Definition: Which features affect the house price and can we make aprice estimation using these features in the Kadıköy?

Solution Recommendation: Developing regression models with the data obtained byusing the web scraping technique from Zingat

Objective: Finding the features that affect the price and the bestpossible regression model based on the importance of thefeatures that affect the house price

Dataset

Webpage: https://www.zingat.com/

ZINGAT_LOGO

Methodology

  • Using the Python BeautifulSoup library, data collection for each post's features using the web scraping method

WEB_SCRAPING

  • Performing various editing and cleaning operations on the obtained data

  • Performing exploratory data analysis on the cleaned data

  • Finding the best regression model for our dataset to reach our aim

Results

According to the analysis, the features that most positively affect the price of ahouse in Kadıköy are;

  • The square meter of the house
  • Number of rooms and living rooms of the house
  • The house is located in Caddebostan or Fenerbahce HOUSE_FEATURES NEIGHBOURHOOD

According to the analysis, the best regression method is shown;

  • Linear Regression REGRESSION