This project matches job descriptions with a given resume based on TF-IDF similarity. It helps identify the job position that best suits the provided resume.
TF-IDF (Term Frequency-Inverse Document Frequency) is a way to measure the importance of words in a document or a collection of documents. It combines two factors: Term Frequency (TF): How often a word appears in a document. If a word appears multiple times, it is likely to be more important. Inverse Document Frequency (IDF): How rare a word is across all documents. If a word is rare, it is likely to be more significant.
TF-IDF helps identify important words that are specific to a document while filtering out common words. It has several benefits:
- Highlighting Important Words: TF-IDF emphasizes words that are important and relevant to a document.
- Extracting Features: It is useful for extracting meaningful features from text for tasks like classification, clustering, and retrieval.
- Reducing Dimensionality: TF-IDF can reduce the complexity of text data while retaining crucial information.
- Filtering Noise: TF-IDF helps filter out common words (stopwords) and focuses on distinctive and meaningful words.
One might think that longer documents have a greater chance to be more similar due to content length, however, TF-IDF does not prioritize longer documents. It looks at the significance of words within a document, considering both local and global measures. The IDF component ensures that important words are not overshadowed by document length. So, whether a document is short or long, TF-IDF allows us to effectively analyze and retrieve relevant information.
- Python 3.7x and above
- NLTK library
- scikit-learn library
Clone the repository:
git clone https://github.com/o-bm/ResumeMatch.gitDownload the necessary NLTK resources:
import nltk
nltk.download('punkt')
nltk.download('stopwords')Provide job description files in the "jobs" directory. Each job description should be a text file with a ".txt" extension. Put the resumes in the "resumes" directory. Each resume should also be a text file with a ".txt" extension.
Run the program:
python runner.pyEnter the full file name with the .txt extention
The program will calculate the similarity between the resume and each job description using TF-IDF. It will then display the most suited job position and a table of the top and bottom five matches.
