Resume Matching System
1. Introduction
Objective: Develop an automated system to match resumes with job descriptions
using Natural Language Processing (NLP) techniques.
Purpose: Streamline the hiring process by providing a ranking or score for
resumes against specific job descriptions, ensuring efficient candidate
shortlisting.
2. Project Workflow
1. Problem Definition:
- Automate the process of screening
resumes against job descriptions.
- Key requirements:
- Extract key skills and
qualifications from resumes and job descriptions.
- Score resumes based on their
relevance to job descriptions.
2. Data Collection:
- Source: Sample resumes and job
descriptions (can be manually curated or sourced from datasets).
3. Data Preprocessing:
- Text cleaning, tokenization, and
vectorization of resumes and job descriptions.
4. Model Selection:
- NLP models like TF-IDF, Word2Vec, or
transformer-based models like BERT.
5. Evaluation:
- Use metrics like precision, recall,
and accuracy for the ranking system.
3. Technical Requirements
- Programming Language: Python
- Libraries/Tools:
- NLP: NLTK, SpaCy, Scikit-learn,
Hugging Face Transformers
- Data Handling: Pandas, NumPy
- Visualization: Matplotlib, Seaborn
4. Implementation Steps
Step 1: Setup Environment
Install required libraries:
```
pip install pandas numpy matplotlib seaborn scikit-learn nltk spacy
transformers
```
Download NLP resources:
```
import nltk
nltk.download('punkt')
nltk.download('stopwords')
```
Step 2: Load and Preprocess Data
Load resumes and job descriptions:
```
import pandas as pd
data = pd.read_csv("resumes_and_job_descriptions.csv")
print(data.head())
```
Preprocess the text data:
```
from sklearn.feature_extraction.text import TfidfVectorizer
def preprocess(text):
text = text.lower().replace("
", " ")
return text
data['resume_cleaned'] = data['resume'].apply(preprocess)
data['job_description_cleaned'] = data['job_description'].apply(preprocess)
```
Step 3: Vectorization Using TF-IDF
Generate TF-IDF vectors:
```
vectorizer = TfidfVectorizer()
resume_vectors = vectorizer.fit_transform(data['resume_cleaned'])
job_desc_vectors = vectorizer.transform(data['job_description_cleaned'])
```
Step 4: Cosine Similarity Scoring
Compute similarity scores:
```
from sklearn.metrics.pairwise import cosine_similarity
data['similarity_score'] = [
cosine_similarity(resume,
job_desc).flatten()[0]
for resume, job_desc in
zip(resume_vectors, job_desc_vectors)
]
```
Step 5: Using Transformer Models
Leverage BERT for embeddings:
```
from transformers import pipeline
embedding_model = pipeline("feature-extraction",
model="bert-base-uncased")
data['resume_embedding'] = data['resume_cleaned'].apply(lambda x:
embedding_model(x)[0])
data['job_desc_embedding'] = data['job_description_cleaned'].apply(lambda x:
embedding_model(x)[0])
# Compute similarity with embeddings (average pooling)
import numpy as np
def compute_similarity(embed1, embed2):
return
cosine_similarity(np.mean(embed1, axis=0).reshape(1, -1), np.mean(embed2,
axis=0).reshape(1, -1))[0][0]
data['bert_similarity_score'] = data.apply(
lambda row:
compute_similarity(row['resume_embedding'], row['job_desc_embedding']), axis=1)
```
Step 6: Evaluation
Evaluate scoring system:
```
import matplotlib.pyplot as plt
# Visualize similarity scores
plt.hist(data['similarity_score'], bins=20, alpha=0.7, label='TF-IDF Scores')
plt.hist(data['bert_similarity_score'], bins=20, alpha=0.7, label='BERT
Scores')
plt.legend()
plt.show()
```
5. Expected Outcomes
1. A system capable of ranking resumes based on relevance to job descriptions.
2. Improved efficiency in shortlisting candidates for interviews.
3. Insights into the effectiveness of different NLP techniques for resume
matching.
6. Additional Suggestions
- Integrate the matching system into an HR platform for seamless usage.
- Experiment with fine-tuning transformer models like BERT for domain-specific
requirements.
- Incorporate additional scoring criteria such as years of experience and
location.