Resume Text Classifier using Classical NLP

2026-02-16 · Source: NLP on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Intermediate, quick

Summary

This project details the creation of a resume text classification system utilizing classical Natural Language Processing (NLP) techniques and a Logistic Regression model. The system is designed to automatically categorize resume text into "Data Science", "Web Development", or "Marketing". The workflow involves loading a resume dataset, performing data cleaning to remove noise, and preprocessing text through tokenization and lowercasing. Feature extraction is achieved using TF-IDF to convert text into numerical vectors. The dataset is then split 80:20 for training and testing, followed by training the Logistic Regression model and evaluating its performance with Precision, Recall, and F1-score metrics. The project structure includes dedicated directories for data, logs, and source code, with a public GitHub repository for the dataset and full code.

Key takeaway

For NLP Engineers building resume classification systems, this project demonstrates a practical approach using classical NLP and Logistic Regression. You should consider this workflow for its simplicity and effectiveness in categorizing resumes into predefined job roles. Implementing similar data cleaning and TF-IDF feature extraction steps can significantly improve model performance and interpretability, providing a solid foundation for your own classification tasks.

Key insights

Classical NLP and Logistic Regression can effectively classify resume text into job categories.

Principles

• Text preprocessing is crucial for classification accuracy.
• TF-IDF is effective for text feature extraction.

Method

The method involves data loading, cleaning, tokenization, lowercasing, TF-IDF feature extraction, an 80:20 train-test split, Logistic Regression model training, and evaluation using Precision, Recall, and F1-score.

In practice

• Use Logistic Regression for text classification.
• Apply TF-IDF for converting text to numerical features.

Topics

• Resume Classification
• Classical NLP
• Logistic Regression
• TF-IDF
• Text Preprocessing

Code references

• JaspinderKaurWalia26/Resume-Job-Description-Classifier
• JaspinderKaurWalia26/Resume-Job-Description-Classifier

Best for: Machine Learning Engineer, NLP Engineer, AI Student

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Editorial summary, takeaway, and curation by AIssential. Original article published by NLP on Medium.