In today's competitive job market, predicting salaries effectively and evaluating resumes accurately are vital for organizations to streamline and enhance their hiring processes. This project, titled "Resurate Calculation", leverages Machine Learning and Natural Language Processing (NLP) to:
- 🎓 Predict salaries based on candidate profiles
- 📄 Parse and evaluate resumes for job suitability
It brings together powerful regression algorithms, ensemble learning, and NLP techniques to automate and optimize recruitment decisions.
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Forecast salaries based on:
- Job experience
- Skills
- Education
- Geographic location
- Industry and market trends
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Provide intelligent salary benchmarking for companies and professionals
- Extract and evaluate resume details using NLP
- Enable automated candidate shortlisting and ranking
🔧 Algorithms Implemented:
- Linear Regression: For simple trend mapping
- Polynomial Regression: To model non-linear patterns
- Random Forest & Gradient Boosting: For robust ensemble-based predictions
- Deep Learning Models: Neural networks for capturing complex relationships between features
📊 Key Factors Used:
- Years of Experience
- Job Role & Industry
- Degree / Education Level
- Technical Skills
- Location & Region
🔍 NLP Techniques:
- Tokenization: Splitting resumes into structured units
- Named Entity Recognition (NER): Extracting names, skills, institutes, and organizations
- POS Tagging & Lemmatization: Improving language understanding
- Semantic Matching: Comparing resumes against job descriptions
📌 What We Extract:
- 🎓 Education & Certifications
- 💼 Work Experience & Roles
- 🛠 Skills (Technical & Soft)
- 🏆 Projects, Achievements, Awards
📈 Resume Evaluation:
- Match parsed content with job requirements
- Assign resume scores based on relevance, skills fit, and completeness
- Python 3.x 🐍
| Task | Tools & Libraries |
|---|---|
| Machine Learning | scikit-learn, xgboost, tensorflow, keras |
| Data Handling | pandas, numpy |
| Visualization | matplotlib, seaborn, plotly |
| NLP & Text Parsing | nltk, spacy, re, textblob |
| Model Deployment | (Optional) Flask, Streamlit |
📦 Resurate-Calculation/
├── data/ # Raw and cleaned datasets
├── notebooks/ # Jupyter notebooks for analysis and modeling
├── models/ # Saved ML models (Pickle / H5 files)
├── src/ # Source code: ML, NLP, scoring
├── utils/ # Helper functions
├── requirements.txt # Dependency list
└── README.md # Project documentation
- ✅ Integrate resume ranking dashboard using Streamlit or Flask
- 🧠 Explore transformer-based NLP models (BERT, RoBERTa) for resume parsing
- 🌐 Build a REST API for remote resume scoring
- 📊 Incorporate salary prediction for real-time market conditions using APIs