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Natural Language Processing Project: Help in extracting data science job skills needed in the current market. Leverage web scraping for data gathering and Named Entity Recognizer for skill extraction.

This project is part of Eskwelabs Data Science Fellowship Cohort 14 curriculum. This project aims to find critical skills gap of Filipino candidates in data analytics sector jobs through network analysis and keyword extraction techniques.

Built an ATS tool using Gemini 1.5 Flash to match resumes with job descriptions. Used both direct text and byte-based PDF extraction. It gives match % scores, highlights strong/missing keywords, and suggests skill improvements for tech roles like Data Science, Full Stack, and DevOps.

NER and classification models to analyze German data science job postings. Fine-tuned German BERT for skill extraction.

Comparative analysis of TF-IDF and LLM-based skill extraction on data science job postings using Groq (LLaMA 3.1).

A data analysis project to explore trends in Data Science job postings, including salary expectations, skills requirements, and employer presence. Utilizes SQL and Python for data extraction and analysis.

This project analyzes the Data Science job market in the United States to uncover insights related to job demand, salary trends, top industries, hiring companies, required skills, and education impact. Using SQL for data extraction and Power BI for visualization.

#Hackveda #https://bit.ly/3y3zixE #Khadeer Pasha # Advanced-House-Price-Prediction It is a Advanced Problem of Regression which requires advanced techniques of feature engineering, feature selection and extraction, modelling, model evaluation, and Statistics. Start here if... You have some experience with R or Python and machine learning basics. This is a perfect competition for data science students who have completed an online course in machine learning and are looking to expand their skill set before trying a featured competition. # Description Practice Skills Creative feature engineering Advanced regression techniques like random forest and gradient boosting Acknowledgments The Housing dataset. # Goal It is your job to predict the sales price for each house. For each Id in the test set, must predict the value of the SalePrice variable.