Problem

At Sterlite Technologies, we faced the challenge of manually screening hundreds of resumes for our internship program. It was a time-consuming and tedious process that left us with little time to focus on other important tasks.

We needed a solution that could automate the resume screening process and classify resumes based on relevant skills and experience.

Solution

We developed a machine learning model using spaCy, Python, NLP, and text analytics to parse resumes and classify them. The model was trained on a dataset of over 500 resumes from previous internship applications.

The model used natural language processing techniques to extract information such as education, work experience, skills, and achievements from the resumes. It then classified the resumes such as "highly qualified," "qualified," and "not qualified" based on their relevance to the internship program requirements.

Impact

Reduced resume screening time.
Increased accuracy in classifying resumes.

Resume Parser Using SpaCy