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AI-powered match analysis
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RankRecruit uses a custom-trained Gradient Boosting model with an R² score of 0.93 — meaning it explains 93% of variance in matching scores. Trained on 3,000+ synthetic resume-job pairs across 12 job templates and 5 match tiers, with hyperparameter tuning via GridSearchCV.
Most screeners just do keyword matching. RankRecruit goes deeper with TF-IDF vectorization, cosine similarity, and category-aware skill grouping — understanding that React and Vue belong to the same family, and that "3 years" means mid-level experience.
Get a comprehensive breakdown: skill match percentage, category coverage radar, experience alignment, and personalized recommendations. Know exactly which skills are matched, which are missing, and what to improve.
Resume text is extracted from PDF/DOCX using intelligent parsers that read tables, columns, and sections
NLP identifies 200+ technical skills, experience level, education, and categorizes them into 8 groups
Our ML model + heuristic engine blends 8 feature signals to produce an accurate, explainable match score
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