You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
Welcome to the Resume Screener project! This project aims to predict the role of a candidate based on their uploaded resume. It's built using Natural Language Processing (NLP) techniques and deployed as a web application using Streamlit.
How it Works
Upload Resume: Users can upload their resume in either .txt or .pdf format using the file uploader provided in the web application.
Prediction: The uploaded resume is then processed using NLP techniques to extract relevant features. These features are used to make predictions about the role of the candidate.
Display Prediction: The predicted role of the candidate is displayed to the user along with a confidence score. Additionally, the application provides a brief explanation of how the prediction was made.
Technologies Used
Streamlit: Used to build the web application interface.
Scikit-learn: Utilized for training and deploying the machine learning model.
NLTK: Used for text preprocessing tasks such as tokenization and stopword removal.
Joblib and Pickle: Used for serializing and deserializing the trained model and vectorizer.
Git: Version control system used for managing project files.