LINKEDIN JOBS ANALYSIS

This repository presents an end-to-end analysis of LinkedIn's professional networking platform's Jobs section. The project aims to extract over 500 job details from LinkedIn's website using the Python library, Selenium, and organize the information into specific formats by creating three tables. Additionally, the project involved data cleaning and exploratory data analysis using Pandas and Numpy libraries. MySQL and MS-Excel were utilized to derive insights from the dataset. Power BI was used to visualize the results. Finally, a web page was created to present relevant job information based on the skills listed in the dataset. The page was hosted on the cloud for wider access.

User's Manual

Files	Description
Comapny.csv	The file contain details of company
DASHBOARD_final.xlsx	The file contains of Deshboard
Details.csv	The file contains details of employee
Jobs.csv	The file contains jobs details
Linkedin Job Analysis Script.sql	This script contains insights derived in mysql-workbench
Linkedin_Project.ipynb	This is our Data Scrapping file used to scrape linkedin jobs section
PPT.pdf	This is final PPT of insights

Tools & Technology Used:

Methodology:

1. Implemented web scraping on the LinkedIn jobs section using the Python library Selenium. Leveraged its capabilities to extract and retrieve the following attributes from the job listings:

Attribute	Feature's Meaning
location	The location of the job
designation	The designation of the job
name	Name of the company
industry	Industry in which the company operates
employees_count	Count of employees
linkedin_followers	Number of followers on linkedin
involvement	the nature of involvement in the job, for instance: Full-time, part-time
level	The seniority level like Mid-Senior level
total_applicants	total number of applicants

2. Utilized Pandas to perform data cleaning and exploratory data analysis (EDA), and seamlessly imported the CSV files into MySQL for comprehensive analysis and further insights extraction.

3. Skillfully employed various SQL clauses such as GROUP BY, ORDER BY, HAVING, and more to manipulate the data, enabling in-depth analysis and extraction of valuable insights.

Results/Insights:

:

Conclusions:

1. Most jobs are being posted by small-size Companies but applicants are apprently applying in Large-size Companies.
2. Most applicants are applying in Chennai based companies, but more jobs are there in Mumbai and Bangalore based companies
3. Significantly higher number of applicants in large and small size companies as compared to medium size companies
4. Top three states with maximum job openings are Tamil Nadu, Karnataka and Maharastra.
5. Digital marketing has the most opportunities
6. About half of the total openings are in IT industry
7. AI is most required skills.