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Text Extraction with POS Tagging and Deep Learning(LSTMs)

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Data Science Job Detective

Getting your dream Data Science Job is a great motivation for developing a Data Science Roadmap. How do you develop a Roadmap without knowing the relevant skills and tools to Learn. In this project, I will explore over 800 Data Science Job postings in Canada. The data used was collected from postings on Glassdoor and Indeed in early June, 2021.

Link to [Medium] - (https://medium.com/@Olohireme/job-skills-extraction-from-data-science-job-posts-38fd58b94675)

Table of contents

Installation

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pip install requirements.txt

Usage

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  • Stage 1: Scraping Data using selenium - Run glassdoor.py and indeed.py to scrape jobs.
  • Stage 2: Performed Exploratory Data analysis in EDA.ipynb
  • Stage 3: Performed Rule Based Skill Extraction using Spacy in skill_extraction.ipynb
  • Stage 4: Training and testing skill extraction LSTM model in job_skills_prediction.ipynb
  • Stage 5: Use saved LSTM and Rule-Based model to predict unseen text (i.e. Text that was not in the dataset) in job_skills_extraction_pipeline.ipynb
  • Stage 6: Streamlit deployment code in deploy.py

To run locally - streamlit run deploy.py

Demo

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You can find the Demo here.

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