Building an ML Model to Scan Resumes with Python and scikit-learn

 Introduction:

In today's competitive job market, organizations receive numerous resumes for each job opening. Manually reviewing all the resumes can be time-consuming and prone to errors. To streamline this process, machine learning techniques can be employed to scan resumes and determine their relevance to specific positions. In this blog post, we will explore an example code that demonstrates how to build an ML model to scan resumes in different formats using Python and the scikit-learn library.

Step 1: Load and Preprocess the Data: The first step is to load and preprocess the resume data. Assuming the resumes are stored in a folder named 'resumes', the code iterates over the files in the folder and extracts the text from each file. Additionally, the code assumes that the label (e.g., 'engineer' or 'not_engineer') is mentioned in the file name or path. You can customize this logic based on your file naming conventions.

Step 2: Convert Text into Numerical Features: To enable machine learning algorithms to process the resume text, we need to convert it into numerical features. In this example, we utilize the TF-IDF (Term Frequency-Inverse Document Frequency) vectorizer from scikit-learn. TF-IDF assigns weights to each term in a document based on its frequency in the document and inverse frequency in the entire corpus. This conversion helps capture the importance of words in the resumes.

Step 3: Train and Evaluate the Model: After converting the resume text into numerical features, we split the data into training and testing sets. The training set is used to train our ML model, while the testing set is used to evaluate its performance. In this example, we employ logistic regression, a widely used algorithm for binary classification tasks. Logistic regression models the relationship between the numerical features and the labels, enabling us to predict whether a resume is relevant to a specific position or not.

Finally, we train the model using the training data and evaluate its performance on the test data. The classification_report function from scikit-learn provides us with key metrics such as precision, recall, and F1-score, which help assess the model's performance in terms of its ability to correctly classify resumes.

Python Code (** Please be aware below is a sample code and will need to be updated as relate to use case)

import os import pandas as pd from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression from sklearn.metrics import classification_report # Step 1: Load and preprocess the data # Assume you have a folder named 'resumes' containing resume files in different formats resume_folder_path = 'resumes' # Create empty lists to hold the resume text and labels resume_text = [] labels = [] # Iterate over files in the resume folder for filename in os.listdir(resume_folder_path): file_path = os.path.join(resume_folder_path, filename) if os.path.isfile(file_path): # Extract the text from each file with open(file_path, 'r', encoding='utf-8') as f: text = f.read() resume_text.append(text) # Assuming the label is mentioned in the file name or path # You can adjust this logic based on your file naming conventions if 'engineer' in filename: labels.append('engineer') else: labels.append('not_engineer') # Step 2: Convert text into numerical features # Create a TF-IDF vectorizer to convert text into numerical features vectorizer = TfidfVectorizer() # Convert the resume text into TF-IDF features X = vectorizer.fit_transform(resume_text) # Create target labels y = labels # Step 3: Train and evaluate the model # Split the data into training and testing sets X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # Create a logistic regression model model = LogisticRegression() # Train the model model.fit(X_train, y_train) # Evaluate the model on the test set y_pred = model.predict(X_test) # Print classification report print(classification_report(y_test, y_pred))

Conclusion: Automating the resume scanning process using machine learning can save valuable time and resources in the hiring process. In this blog post, we examined an example code that demonstrates how to build an ML model to scan resumes and determine their relevance for specific positions. By leveraging Python and the scikit-learn library, we were able to load and preprocess the data, convert text into numerical features using TF-IDF, and train a logistic regression model. Although the example code serves as a starting point, it can be further customized and enhanced based on your specific requirements and dataset format.

Remember that the success of a resume scanner heavily relies on the quality and diversity of the training data, as well as the thoughtful selection of features. Continuous improvement and fine-tuning are essential to enhance the accuracy and relevance of the scanning results.