ML for Resume-to-Job Matching System

 ML for Resume-to-Job Matching System – IT and Computer Engineering Guide

1. Project Overview

Objective: Build a machine learning system that uses natural language processing (NLP) to rank resumes based on their relevance to a specific job description.
Scope: Assist recruiters by automating the initial screening and ranking of candidate resumes.

2. Prerequisites

Knowledge: Basics of natural language processing, text processing, and machine learning.
Tools: Python, libraries like NLTK, SpaCy, and Scikit-learn, and a dataset of resumes and job descriptions.
Hardware: A system capable of handling text processing and ML model training.

3. Project Workflow

- Data Collection: Collect resumes and corresponding job descriptions.

- Preprocessing: Clean and tokenize the text data, and extract key features.

- Model Design: Build an NLP-based model to compute similarity scores or relevance rankings.

- Training: Train the model using a labeled dataset with relevance scores.

- Evaluation: Test the model on new resumes and job descriptions to measure its ranking accuracy.

- Deployment: Deploy the model as a web or desktop application for recruiters.

4. Technical Implementation

Step 1: Data Preprocessing


from sklearn.feature_extraction.text import TfidfVectorizer
import pandas as pd

# Load dataset
resumes = pd.read_csv('resumes.csv')['text']
job_description = pd.read_csv('job_description.csv')['text'][0]

# TF-IDF Vectorization
vectorizer = TfidfVectorizer(stop_words='english')
tfidf_matrix = vectorizer.fit_transform([job_description] + resumes.tolist())

# Separate vectors
job_vector = tfidf_matrix[0]
resume_vectors = tfidf_matrix[1:]

Step 2: Compute Similarity Scores


from sklearn.metrics.pairwise import cosine_similarity

# Compute cosine similarity
scores = cosine_similarity(job_vector, resume_vectors)

# Rank resumes based on scores
ranked_indices = scores.argsort()[0][::-1]

Step 3: Build and Train a Ranking Model


from sklearn.ensemble import GradientBoostingRegressor

# Example training data (features and labels)
features = [[0.8, 0.2], [0.5, 0.7], [0.3, 0.9]]  # Dummy feature vectors
labels = [1, 0, 1]  # Relevance labels

# Train a regression model
model = GradientBoostingRegressor()
model.fit(features, labels)

Step 4: Deploy the Model


import flask

app = flask.Flask(__name__)

@app.route('/match', methods=['POST'])
def match():
    data = flask.request.json
    resume = data['resume']
    score = model.predict(vectorizer.transform([resume]))
    return {"relevance_score": score.tolist()}

if __name__ == '__main__':
    app.run(debug=True)

5. Results and Insights

Evaluate the system's ranking accuracy using metrics like precision, recall, and Mean Reciprocal Rank (MRR). Analyze its performance across various job descriptions.

6. Challenges and Mitigation

Data Quality: Use techniques like entity recognition to enhance data preprocessing.
Ambiguity: Incorporate domain-specific ontologies or embeddings like BERT for better understanding.

7. Future Enhancements

Expand the system to handle multilingual resumes and job descriptions.
Integrate sentiment analysis to gauge candidate tone or intent.

8. Conclusion

The ML-based Resume-to-Job Matching System project showcases the potential of NLP and ML in automating the recruitment process, making it faster and more efficient.