Build a resume analyzer using keyword matching

Here’s a Python-based implementation of a Resume Analyzer using keyword matching. This tool reads resumes (in .txt format or extracted text from PDF), matches them against a set of predefined keywords (based on job descriptions or required skills), and scores them based on relevance.

python
CopyEdit
import os
import re
from typing import List, Dict

class ResumeAnalyzer:
    def __init__(self, keywords: List[str]):
        self.keywords = [kw.lower() for kw in keywords]

    def clean_text(self, text: str) -> str:
        text = text.lower()
        text = re.sub(r'[^ws]', '', text)
        return text

    def extract_keywords(self, text: str) -> Dict[str, int]:
        text = self.clean_text(text)
        words = text.split()
        keyword_freq = {kw: words.count(kw) for kw in self.keywords}
        return keyword_freq

    def score_resume(self, keyword_freq: Dict[str, int]) -> int:
        return sum(keyword_freq.values())

    def analyze_resume(self, text: str) -> Dict[str, any]:
        keyword_freq = self.extract_keywords(text)
        score = self.score_resume(keyword_freq)
        return {
            "keyword_frequency": keyword_freq,
            "total_score": score
        }

# Example Usage
if __name__ == "__main__":
    keywords = [
        "python", "machine learning", "data analysis", "sql", "tensorflow",
        "communication", "leadership", "project management", "cloud", "aws"
    ]
    
    # Example resume text (normally you'd read from a file or PDF extractor)
    resume_text = """
    Experienced data scientist with strong Python and SQL skills. Proficient in data analysis and machine learning. 
    Worked on cloud deployment using AWS. Excellent communication and leadership abilities.
    """

    analyzer = ResumeAnalyzer(keywords)
    result = analyzer.analyze_resume(resume_text)

    print("Resume Analysis Results:")
    print(f"Keyword Frequency: {result['keyword_frequency']}")
    print(f"Total Relevance Score: {result['total_score']}")

Features:

• Keyword Frequency Counting: Counts how often each keyword appears.

• Scoring: Adds up the keyword matches to generate a relevance score.

• Extensible: You can plug in keyword lists for different job descriptions.

Extension Ideas:

• Use PDF resume parsing with libraries like PyPDF2 or pdfplumber.

• Integrate with NLTK or spaCy for more advanced NLP.

• Create a simple web interface with Flask for uploading and scoring resumes.

Let me know if you want a web-based version or a feature to compare multiple resumes.