Building an ML Model to Scan Resumes with Python and scikit-learn
Introduction: In today's competitive job market, organizations receive numerous resumes for each job opening. Manually reviewing all the resumes can be time-consuming and prone to errors. To streamline this process, machine learning techniques can be employed to scan resumes and determine their relevance to specific positions. In this blog post, we will explore an example code that demonstrates how to build an ML model to scan resumes in different formats using Python and the scikit-learn library. Step 1: Load and Preprocess the Data: The first step is to load and preprocess the resume data. Assuming the resumes are stored in a folder named 'resumes', the code iterates over the files in the folder and extracts the text from each file. Additionally, the code assumes that the label (e.g., 'engineer' or 'not_engineer') is mentioned in the file name or path. You can customize this logic based on your file naming conventions. Step 2: Convert Text into Numerical...