{"repo":"ridhima183/resume-classifier---RoleRadar-Ai","free":true,"listed":false,"github":"https://github.com/ridhima183/resume-classifier---RoleRadar-Ai","clone":"git clone https://github.com/ridhima183/resume-classifier---RoleRadar-Ai.git","description":"RoleRadar AI is a full-stack AI-powered resume classification platform that predicts job roles using NLP and machine learning. Built with React, FastAPI, PostgreSQL, and Scikit-learn, it features JWT authentication, resume parsing, LinkedIn profile import, skill analysis, confidence scores, feedback, and an admin dashboard.","language":"JavaScript","stars":10,"topics":[],"license":null,"category":"machine-learning","readme_excerpt":"AI Resume Classifier (RoleRadar) RoleRadar is a production-ready, full-stack web application that leverages natural language processing (NLP) and machine learning to analyze resumes, extract text data, and automatically recommend the most suitable job role with calculated confidence metrics. --- 🚀 Key Features & Performance - Calibrated Multi-Class ML Engine: Evaluates text data distributions across Linear SVM, Random Forest, and Naive Bayes architectures utilizing probability-calibration models. - Optimized NLP Text Parsing: Processes raw unstructured string inputs through a custom TF-IDF unigram and bigram tokenization vectorizer, capping feature vectors at 25,000 top-tier dimensions to mitigate matrix sparsity. - Granular Confidence Labeling: Returns localized probability metrics paired with clear algorithmic tiers ( Very High, High, Moderate, Low ) to ensure auditable predictions. - Robust Text Extraction: Fully supports processing raw text alongside multiple document formats including .pdf , .docx , and image/text formats through robust parsing abstractions. --- 📊 Core Machine Learning Metrics The underlying classification engine benchmarks and evaluates its decision boundaries using a strict 4-metric framework consisting of Accuracy, Macro-Averaged F1-Score, Confusion Matrix distributions, and ROC-AUC curves: Metric Value --- --- Classification Accuracy 81.3% achieved via the optimized production classifier Data Split Distribution 80/20 Stratified Train-Test split acr","default_branch":null,"files":null,"tree":[],"storefront":"/r/ridhima183","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/ridhima183/resume-classifier---RoleRadar-Ai/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}