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This model is designed to automatically analyze resumes submitted by students. It uses Natural Language Processing (NLP) to extract key skills and a Decision Tree machine learning algorithm trained on TF-IDF features to classify each resume into one of four categories: Web Developer, Data Analyst, AI/ML Specialist, or Core Engineer. In addition to predicting the category, the model also identifies and highlights the top three most relevant skills from the resume. This system helps recruiters and placement cells quickly sort and evaluate talent based on skills and job-fit."
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This model is designed to automatically analyze resumes submitted by students. It uses Natural Language Processing (NLP) to extract key skills and a Decision Tree machine learning algorithm trained on TF-IDF features to classify each resume into one of four categories: Web Developer, Data Analyst, AI/ML Specialist, or Core Engineer. In addition to predicting the category, the model also identifies and highlights the top three most relevant skills from the resume. This system helps recruiters and placement cells quickly sort and evaluate talent based on skills and job-fit."
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You said:
Verified signatureAi resume classifer ChatGPT said: Got it 👍 You’re asking about an AI résumé classifier — basically a system that automatically classifies or ranks résumés (CVs) based on given criteria like job role, skills, or relevance. Here’s a breakdown for you: 🔹 What an AI Résumé Classifier Does Parses résumé text (PDF, Word, etc.) into structured data. Extracts features like name, education, experience, skills, certifications. Classifies or ranks candidates based on job requirements (e.g., Python developer, ML engineer). Can be rule-based (keywords, regex, scoring) or ML-based (NLP + ML model). 🔹 Approaches 1. Rule-Based Classifier Simple keyword matching & scoring. Example: If job requires "Python", add +1 score if found in résumé. Pros: Easy to implement. Cons: Not intelligent, ignores context. 2. Machine Learning (ML)-Based Use classification models (Logistic Regression, SVM, Random Forest). Input: résumé text → feature vectors (TF-IDF, BERT embeddings). Output: Job category /…21cb660 -
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