This project aims to provide personalized job recommendations based on specific criteria such as location, salary range, employment status, and job role. The system utilizes NLTK, ML , and Flask to create a web application that delivers 10 best job recommendations.
- Customizable Search Criteria: Users can input their preferences for location, salary range, employment status, and desired job role.
- Advanced Data Processing: NLTK is used for natural language processing, ML algorithms for recommendation, and Pandas and NumPy for data handling.
- Web Interface: Flask is employed to create a user-friendly web interface for seamless interaction.
- NLTK: Natural Language Toolkit for processing textual data.
- Machine Learning (ML): Algorithms used to recommend jobs based on user preferences.
- Pandas and NumPy: Libraries for efficient data processing and analysis.
- Flask: Web framework for building the user interface.
The core dataset is sourced from Kaggle, featuring job-related information. It has been enriched with additional details, including company URLs and logos URLs.
- Input Preferences: Users enter their preferred criteria - location, salary range, employment status, and job role.
- Get Recommendations: The system processes user inputs and provides a list of the top 10 recommended jobs.
- Clone the repository to your local machine.
- Run the Flask application using
python app.py. - Access the system through your web browser at
http://localhost:5000.