CV ANALYSIS USING MACHINE LEARNING

Overview

CV analysis using machine learning involves leveraging algorithms to extract meaningful insights from resumes to aid in the recruitment process. This process typically includes several key steps:

1. Data Extraction: Machine learning models are employed to automatically extract relevant information from CVs, such as education history, work experience, skills, and contact details. Natural Language Processing (NLP) techniques are often used to parse and structure unstructured text.

2. Feature Engineering: Once data is extracted, features are engineered to represent important attributes of candidates, such as years of experience, specific skills, or industry expertise. This step is crucial for preparing the data for machine learning algorithms.

3. Classification or Ranking: ML models can classify CVs into categories (e.g., qualified vs. unqualified) or rank them based on suitability for specific roles. Algorithms like Support Vector Machines (SVM), Random Forests, or Neural Networks are commonly used for this purpose.

4. Keyword Matching: NLP models can match keywords from job descriptions with those on CVs to identify relevant skills or experiences. This helps in determining how well a candidate matches a job role.

5. Bias Detection: Machine learning can also be used to detect biases in recruitment processes by analyzing patterns in CV selection. This helps organizations ensure fairness and diversity in their hiring practices.

6. Performance Evaluation: The effectiveness of CV analysis models is assessed using metrics like precision, recall, and F1-score, comparing predicted outcomes with actual hiring decisions.

7. Continuous Learning: ML models can learn from past hiring decisions and feedback to improve their accuracy over time, adapting to changes in job market requirements or company preferences.