Job Postings Web Scraping

Project Overview

The Job Postings Web Scraping project involves extracting more than 10,000 job postings from the Instahyre website using Selenium, a powerful web scraping library. The extracted data is then processed and cleaned using Pandas, resulting in a structured and clean dataset ready for further analysis.

Tools Used

• Selenium: For web scraping to automate the extraction of job postings.
• Pandas: For data manipulation and cleaning.

Project Steps

1. Web Scraping Setup

• Installed and configured Selenium, including the appropriate WebDriver for the browser.
• Wrote scripts to navigate the Instahyre website, locate job postings, and extract relevant information such as job titles, companies, locations, and descriptions.

2. Data Extraction

• Developed a scraping script to iteratively collect data from multiple pages.
• Ensured efficient and respectful scraping by implementing appropriate delays between requests to avoid overloading the server.

3. Data Storage

• Stored the extracted data in a structured format using a Pandas DataFrame.
• Saved interim data to CSV files to ensure data persistence in case of interruptions.

4. Data Cleaning

• Loaded the extracted data into Pandas for cleaning.
• Performed data cleaning tasks including:
  • Removing duplicates.
  • Handling missing values.
  • Standardizing formats (e.g., date formats, text casing).
• Validated the data to ensure consistency and accuracy.

5. Data Analysis (Optional)

• Conducted preliminary analysis to gain insights into the job postings.
• Explored key metrics such as the number of job postings by company, location, and job title.

Repository Structure

• scraping/: Contains the Selenium scripts for web scraping.
• data/: Stores raw and cleaned data files.
• notebooks/: Includes any Jupyter notebooks used for data cleaning and analysis.
• README.md: Project description and overview.

How to Use

1. Clone the repository to your local machine.
2. Run the python file selenium craping script located in the Web Scraping Project 1.0/ folder:
3. Load the extracted data into a Pandas DataFrame and perform any additional data cleaning as needed.

Conclusion

This project successfully demonstrates the use of Selenium for web scraping and Pandas for data cleaning. By extracting a substantial number of job postings from Instahyre and processing them into a clean dataset, this project provides a valuable resource for job market analysis and insights.

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Feel free to contribute to this project by suggesting improvements, reporting issues, or submitting pull requests. Happy scraping!