Resume Matching System

 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.