This project aims to implement a detector of similar job descriptions using big data analysis techniques on the LinkedIn Jobs & Skills dataset. By leveraging PySpark, we will uncover insights and patterns in the job summary data to help employers and job seekers better understand the job market.

This Python project involves scraping job offers from a website (LinkedIn) and then visualizing the results. This project likely involves the use of web scraping techniques to extract job offer data from LinkedIn, followed by the use of data analysis and visualization tools to present the results in a clear and meaningful way.

Linkedin trending jobs analysis BI project

This project seeks to analyse the data analyst job listings found on LinkedIn. The aim of the jobs analysis is to find hidden insights about data analyst jobs such as what skills are most in demand by employers.

Selecting the right career is one of the most important decisions students have to make. With the increase in the number of career paths and opportunities, making this decision have become quite difficult. The purpose of this project is to do a job market analysis in Lebanon to provide information about different job trends in Lebanon up-to-date to Lebanese students, so they could form better career choices. Since many Lebanese students don’t know which major to choose and which job position is highly demanded in Lebanon, this project guides them in their career choice. The problem in Lebanon is that there’s no available data about the job market in Lebanon. Thus, I scrapped different websites to get information about job market trends in Lebanon and information about job professions as well. The design and methodology and methodology of this project is to scrape data about all job positions in Lebanon on LinkedIn, then clean and preprocess the data, turn it into a csv file then present it as visual graphs on a dashboard on Streamlit WebApp. Next, scrape data about job professions from O*net website to get information about different job professions. Then, clean and preprocess the data and present it as visual graphs on Streamlit WebApp. The major findings of this project were that the most demanded job position in Lebanon are manager and software engineer, the top hiring company is Toptal and Agoda, the city with the most job openings is Beirut. This project doesn’t only benefit Lebanese students and help them choose the right career for them, but also, it helps Lebanon’s economy. When worker’s skills and qualifications fit demanded jobs in the country then that increases productivity and boosts economic growth.

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AI/ML Job Postings Analysis on the 1.3M LinkedIn Jobs and Skills 2024

Scrape linkedin job posting for applying jobs and analysis of current hot skills for DE

Market-basket analysis is a popular technique used to identify relationships between items purchased together. In this project, we will apply this technique to the LinkedIn Jobs & Skills dataset, focusing on the job skills listed in various job postings.

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1. To know the skills which need for a jobs, 2. To know the jobs providing countries and cities, 3. To know the which skills need to be learn for a specific position, 4. To get connections with high professional employees and experienced employees 5. To know which location providing jobs for our requirement.

Market basket analysis for LinkedIn jobs and skills using FP Growth Algorithm

Analysis of job market insights and skill trends using a LinkedIn jobs and skills dataset. Explore new job titles, regional variations, top skills in demand, seasonal trends, and top companies hiring. Project implemented in Hadoop for scalable data processing

📊 End-to-end LinkedIn Job Post Analysis with 500+ listings. 🕵️‍♂️ Selenium for scraping, 🐍 Pandas/NumPy for cleaning & EDA, 💾 MySQL for querying, 📈 Power BI for dashboards. 🌐 Dynamic webpage shows jobs by skills (cloud-hosted). 🔎 Insights into industries, locations, applicants & top in-demand skills.

This project seeks to analyse the data analyst job listings found on LinkedIn. The aim of the jobs analysis is to find hidden insights about data analyst jobs such as what skills are most in demand by employers. Following that, I attempt to implement unsupervised Machine Learning by building a K-Means Clustering model.