🧠 Automated Resume Data Extraction Project

📘 Overview

This project focuses on automating the extraction of key information from resumes using Natural Language Processing (NLP) techniques. It streamlines the process of identifying candidate details such as name, email, and phone number, while demonstrating how NLP can convert unstructured data into structured, analyzable information.
The ultimate goal is to make resume screening faster, more accurate, and scalable for real-world recruitment workflows.

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🎯 Objectives

• Extract essential information such as Name, Email, and Contact Number from resume files.
• Support multiple file formats including TXT, DOCX, and PDF.
• Convert unstructured text into structured formats like JSON or CSV.
• Demonstrate the real-world use of NLP in recruitment systems.

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🧩 Key Methods

• Text Extraction: Reading resumes across formats using libraries like PyMuPDF, pdfminer, and docx2txt.
• Named Entity Recognition (NER): Leveraging spaCy to identify entities such as names, emails, and phone numbers.
• Regex Matching: Extracting specific entities using regular expression patterns.
• Data Structuring: Organizing extracted data into tabular formats for easy analysis or integration.

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📊 Visualizations

🧾 Extracted Resume Data

DataFrame displaying names, phone numbers, and emails extracted from resumes across formats.

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🔍 Key Insights & Outcomes

🔹 Automated Information Extraction

The NER model successfully extracted Name, Email, and Phone Number from resumes across formats.
This validates the ability of NLP to convert unstructured resume data into structured information.

🔹 Format Independence

The pipeline performed consistently across multiple file types (TXT, DOCX, PDF), demonstrating robustness and adaptability to real-world resumes.

🔹 Improved Efficiency

Manual resume screening is time-intensive.
Automation reduces effort, minimizes errors, and accelerates candidate filtering.

🔹 Scalability

The system can process large volumes of resumes with minimal additional effort.
It also provides a strong foundation for future extraction of skills, education, and work experience.

🔹 Practical Applicability

This project highlights how NLP can be applied in recruitment systems.
It can be integrated into Applicant Tracking Systems (ATS) to enhance hiring efficiency.

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🛠️ Technologies Used

• Python 🐍
• Jupyter Notebook
• spaCy / NLTK for NLP
• pandas for data manipulation
• re (Regex) for pattern-based extraction
• PyMuPDF / pdfminer / docx2txt for text parsing

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⚙️ Setup & Installation

1. Clone the repository:

git clone https://github.com/indu-explores-data/Automated-Resume-Data-Extraction.git
cd Automated-Resume-Data-Extraction

2. Install Required Dependencies:

pip install -r requirements.txt

3. Launch the Jupyter notebook:

jupyter notebook "Automated Resume Data Extraction.ipynb"

▶️ Usage Instructions

• Upload or specify resume files (TXT, DOCX, or PDF) - Refer to the Resume formats zip folder.
• Run each notebook cell to extract and clean the data.
• View structured output and sample visualizations.
• Export the results to CSV/JSON for analysis or ATS integration.

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🔗 Connect with Me

Let’s connect on LinkedIn for project discussions or data-driven collaborations:

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🙌 Feedback & Support

If you found this project helpful, please ⭐ star the repository and share your thoughts. Suggestions and contributions are always welcome!