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.