https://github.com/codehard8/web-scrapping
In this repository we have provide a web scrapping project through beautifulSoup and related files
https://github.com/codehard8/web-scrapping
beutifulsoup data houses-for-sale python3 requests-library-python webscraping
Last synced: 3 months ago
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In this repository we have provide a web scrapping project through beautifulSoup and related files
- Host: GitHub
- URL: https://github.com/codehard8/web-scrapping
- Owner: codehard8
- Created: 2025-02-25T18:07:47.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2025-02-25T18:21:16.000Z (7 months ago)
- Last Synced: 2025-02-25T19:24:14.018Z (7 months ago)
- Topics: beutifulsoup, data, houses-for-sale, python3, requests-library-python, webscraping
- Language: Python
- Homepage:
- Size: 77.1 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
---
# Web Scrapping Project
This repository contains a web scraping project that utilizes BeautifulSoup to extract data. The repository includes the following key files:
## Files
- **`housing_dta_scrapping.py`**: This script is intended for scraping housing data from the web using BeautifulSoup.
- **`houses_for_sale_US.csv`**: This CSV file contains data of houses for sale in the US, including details such as link, address, dealer, price, beds, baths, and area.
## Project Description
This project demonstrates how to scrape web data using BeautifulSoup in Python. The primary focus is on extracting real estate data and saving it in a structured format for further analysis.
## Requirements
- Python 3.x
- BeautifulSoup
- Requests
## Usage
1. Clone the repository:
```bash
git clone https://github.com/codehard8/Web-Scrapping.git
cd Web-Scrapping
```
2. Install the required packages:
```bash
pip install -r requirements.txt
```
3. Run the scraping script (assuming the script name is `housing_dta_scrapping.py`):
```bash
python housing_dta_scrapping.py
```
4. The results will be saved in `houses_for_sale_US.csv`.
## Data Fields in `houses_for_sale_US.csv`
- **link**: URL of the house listing.
- **address**: Physical address of the house.
- **dealer**: Real estate dealer or agent.
- **price**: Listing price of the house.
- **beds**: Number of bedrooms.
- **baths**: Number of bathrooms.
- **area**: Area of the house in square feet.
## License
This project is licensed under the MIT License.
---