The Resume Analyzer is a powerful tool designed to help job seekers and recruiters streamline the resume screening process. By leveraging advanced natural language processing techniques, this application extracts key features from resumes and job descriptions, enabling users to quickly identify the most relevant candidates for a particular position. With its intuitive interface and robust analytical capabilities, the Resume Analyzer aims to revolutionize the way resumes are evaluated and matched with job opportunities.

To set up the Resume Analyzer, follow these simple steps:

1. Clone the repository to your local machine using:

   bash
   git clone https://github.com/ali-Eskandarian/Resume_Analyzer.git

2. Navigate to the project directory:

  bash
  cd Resume_Analyzerho

3. Navigate to the project directory:

  bash
  cd Resume_Analyzerho

4.Set up the necessary configuration files and directories as specified in the documentation.

Using the Resume Analyzer is straightforward. Simply provide the application with a job description and a set of resumes, either individually or in a directory format. The tool will then process the data, extracting relevant features and performing a detailed analysis. Users can choose to visualize the data using UMAP (Uniform Manifold Approximation and Projection) for dimensionality reduction and clustering. Additionally, the application offers the option to train a clustering model, which can be used to group similar resumes and identify the best matches for a particular job description.

Example Here's an example of how to use the Resume Analyzer:

  python
  from reader import SingleResumeReader, ClusteringResumeReader
  from models import ClusterModel
  from utils import visualize_data_with_umap
  
  # Process a single resume
  resume_path = 'path/to/resume.pdf'
  job_description_path = 'path/to/job_description.txt'
  resume_analyzer = SingleResumeReader(job_description_path, resume_path)
  features_json = resume_analyzer.get_resume_features_as_json()
  
  # Process a directory of resumes
  resume_dir = 'path/to/resumes'
  dataset_cluster = ClusteringResumeReader(job_description_path, resume_dir)
  data = dataset_cluster.process_resumes()
  visualize_data_with_umap(data)
  
  # Train a clustering model
  cluster_model = ClusterModel()
  cluster_model.fit_predict(data)

The future of the Resume Analyzer holds exciting possibilities. The development team is actively working on expanding the application's capabilities, including: 1.Integrating machine learning algorithms for more accurate resume matching and ranking. 2.Developing a web-based interface for easier accessibility and collaboration. 3.Incorporating additional data sources, such as LinkedIn profiles and job boards, to enhance the quality of the analysis. 4.Providing customizable templates and settings to accommodate the specific needs of different industries and organizations.

The Resume Analyzer was developed by a team of dedicated researchers and engineers, including: Ali Eskandarian