Resume Screening with ML – IT and Computer Engineering Guide
1. Project Overview
Objective: Develop a machine learning model to screen
resumes and predict job-role fit based on their content.
Scope: Automate and improve the efficiency of the recruitment process by
matching candidates to job roles.
2. Prerequisites
Knowledge: Basics of Python programming, Natural Language
Processing (NLP), and classification models.
Tools: Python, Scikit-learn, Pandas, NumPy, NLTK, TfidfVectorizer, and
Flask/Django for deployment.
Dataset: Collect resumes and job descriptions from public datasets or create
synthetic data.
3. Project Workflow
- Data Collection: Obtain or synthesize a dataset of resumes and corresponding job roles.
- Data Preprocessing: Clean the text data, tokenize, remove stop words, and normalize text.
- Feature Extraction: Use techniques like TF-IDF or word embeddings to convert text into numerical format.
- Model Development: Train classification models like Logistic Regression, SVM, or Neural Networks.
- Model Evaluation: Use metrics such as accuracy, precision, recall, and F1-score.
- Optimization: Fine-tune hyperparameters and validate using cross-validation techniques.
- Deployment: Deploy the model as a web-based application or API.
4. Technical Implementation
Step 1: Import Libraries
import pandas as pd
import numpy as np
import re
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report, confusion_matrix,
accuracy_score
Step 2: Load and Preprocess the Dataset
# Example for loading a CSV dataset
data = pd.read_csv('resumes.csv')
# Text cleaning function
def clean_text(text):
text = re.sub(r'[^a-zA-Z\s]', '',
text)
text = text.lower()
return text
# Apply cleaning
data['resume'] = data['resume'].apply(clean_text)
Step 3: Feature Extraction
# Using TF-IDF vectorization
tfidf = TfidfVectorizer(max_features=5000)
X = tfidf.fit_transform(data['resume']).toarray()
y = data['job_role']
Step 4: Split the Dataset
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,
random_state=42)
Step 5: Train and Evaluate Models
# Example using Logistic Regression
lr_model = LogisticRegression()
lr_model.fit(X_train, y_train)
y_pred = lr_model.predict(X_test)
# Evaluation
print(classification_report(y_test, y_pred))
print(f"Accuracy Score: {accuracy_score(y_test, y_pred)}")
5. Results and Visualization
Visualize the confusion matrix and evaluate the model's performance across different job roles.
6. Challenges and Mitigation
Data Quality: Ensure the resumes and job descriptions are
well-structured and representative.
Imbalanced Data: Address class imbalance using oversampling or class weights.
7. Future Enhancements
Incorporate advanced NLP models like BERT or GPT for better
text understanding.
Add semantic similarity matching for deeper analysis of resume and job-role
fit.
8. Conclusion
The Resume Screening with ML project streamlines recruitment
by leveraging machine learning to match resumes with job roles.
It demonstrates the application of NLP and predictive analytics in HR
technology.