Project Title : Smart Resume Parser

Objective:
To build a machine learning-based system that can automatically extract relevant information from resumes, such as name, contact, education, skills, experience, etc., and convert it into a structured format.

Technologies Used:

Programming Language: Python

Libraries/Tools: Natural Language Toolkit (NLTK), spaCy, PyPDF2 / pdfminer, pandas, scikit-learn

Techniques: Natural Language Processing (NLP), Regular Expressions, Named Entity Recognition (NER)

Approach:

Resume Collection & Input Handling:

Collect sample resumes in PDF, DOCX, or TXT format

Extract raw text using PDF parsers or document readers

Data Preprocessing:

Clean the text (remove special characters, stop words)

Normalize and tokenize the content

Information Extraction:

Use NER models or regex patterns to extract:

Name, Email, Phone Number

Education details

Work experience

Skills and certifications

Classify content into sections like Summary, Experience, Education, etc.

Model Training (Optional):

Train custom ML models for section classification or skill detection

Use labeled resume data for supervised learning

Output & UI:

Display extracted information in structured format (e.g., JSON, tables)

Optionally build a simple web app using Streamlit or Flask

Outcome:
A smart resume parser that automates resume screening and converts unstructured resumes into structured data, saving time for HR teams and enhancing recruitment workflows.