This is a web application designed to automate the process of screening resumes using Natural Language Processing (NLP) techniques. The app predicts the category of a resume based on its content, allowing recruiters to quickly filter through large numbers of resumes.
- Upload a resume in .txt or .pdf format.
- Predict the category of the job role based on the resume content.
- Display a word cloud visualization of the most frequent words in the resume. .
- Easily customizable and deployable.
- Clone the repository:
git clone https:/githubcomyour_usernameresume-screening-app.git
- Install the required Python packages:
pip install -r requirements.txt
- Navigate to the project directory:
cd resume-screening-app
- Run the Streamlit app:
streamlit run app.py
- Upload a resume using the file uploader in the sidebar.
- View the prediction result, word cloud, and model accuracy plot.
The model is trained on a dataset of labeled resumes, where each resume is associated with a job category. The dataset used for training can be found in the data directory.
The machine learning models used for prediction are trained using the train_model.py script. The trained models are saved in the model directory.
Contributions are welcome! If you find any issues or have suggestions for improvements, please open an issue or create a pull request.