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🚀 Building an ETL Pipeline with Python to Scrape Internship Jobs and Load into Excel
- A full ETL pipeline using Python was built to scrape internship job data, clean and process it, and load it into an Excel file for analysis.
- The project is ideal for those interested in web scraping, data pipelines, or automating tasks with Python and cron.
- Extracting involved scraping internship listings from MyJobMag Kenya, applying filters, and obtaining data for processing.
- Transformation steps included cleaning data using BeautifulSoup for HTML parsing and pandas for manipulation.
- Data cleaning involved removing malformed descriptions, dropping duplicates, and filtering out rows with missing or invalid data.
- Loading the cleaned data into an Excel file, named internships.xlsx, facilitated job search organization, data analysis, and reporting.
- Steps involving extraction, transformation, and loading were explained in detail with code snippets for each phase.
- The 'clean_text' function was introduced to sanitize unwanted characters in the scraped text data.
- Pandas and numpy were utilized to clean the data, explode columns into rows, handle missing values, and prepare for Excel sheet loading.
- The final step involved loading the transformed data into an Excel sheet for improved visualization and user interaction.
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