Hush-Hush-Recruiter

Hush-Hush-Recruiter is an automated recruitment tool that streamlines the entire process of selecting potential candidates for Doodle firm. This secretive process automates the selection analysis and sends an email to a candidate if they are selected for a potential role at Doodle.

Features

• Data Fetching: Fetches candidate data from GitHub through API.
• Data Preprocessing: Cleans and preprocesses the fetched data.
• Candidate Selection: Uses K-means clustering algorithm for selecting candidates.
• Database Integration: Stores candidate names in a database.
• Automated Emails: Automatically sends emails to selected candidates for further examination rounds.
• Backend Maintenance: Managed by the Viseral app for seamless backend operations.

Table of Contents

• Installation
• Usage
• Configuration
• Contributing
• License

Installation

1. Clone the repository:

   git clone https://github.com/Rakesh-Seenu/Hush-Hush-Recruiter.git

2. Navigate to the project directory:

   Hush-Hush-Recruiter

3. Install the required dependencies:

   pip install -r requirements.txt

Usage

1. Fetch Data:

   from data_fetcher import fetch_data
   data = fetch_data()

2. Preprocess and Clean Data:

   from data_preprocessor import preprocess_data
   cleaned_data = preprocess_data(data)

3. Candidate Selection using K-means Clustering:

   from candidate_selector import select_candidates
   selected_candidates = select_candidates(cleaned_data)

4. Store Selected Candidates in Database:

   from database_manager import store_candidates
   store_candidates(selected_candidates)

5. Send Automated Emails:

   from email_sender import send_emails
   send_emails(selected_candidates)

Configuration

• Database Configuration: Ensure your database credentials are correctly configured in database_manager.py.
• Email Configuration: Set up your email server settings in email_sender.py.
• API Configuration: Update your GitHub API token in data_fetcher.py.