Task 6: Web Scraping and Analysis of Job Postings

This project is part of my Elevvo Internship (Data Analytics track).
The objective was to scrape job listings from a real website and analyze the data to identify the most common job titles and in-demand skills.

📂 Dataset

• Source: Real Python Fake Jobs Website
• Data includes job details such as title, company, location, and date posted.

🛠️ Tools & Libraries

• Python
• Pandas
• Matplotlib
• Seaborn
• BeautifulSoup
• Requests
• Jupyter Notebook

🔎 Key Steps

1. Web Scraping – Fetch job postings using BeautifulSoup and Requests
2. Data Cleaning – Remove duplicates, extract city from location
3. Analysis – Identify top job titles and most in-demand skills
4. Data Visualization – Create clean bar plots for top job titles and skills
5. Insights & Conclusion – Interpret results

📊 Key Insights

• Most common job title: Python Programmer (Entry-Level)
• Unique companies: 99
• Top skills overall: Python, Django (based on job title keywords)

📈 Visualizations

Top Job Titles

Top Skills Overall

▶️ How to Run

• Install dependencies: pip install -r requirements.txt
• Open the notebook: jupyter notebook Job_Postings_Analysis.ipynb