Jobs Analyst

Introduction

The Jobs Analyst project tackles one of the most complex workforce challenges—finding the best job opportunities for candidates based on their resumes. This application not only provides personalized job recommendations but also equips users with career improvement insights from industry experts. Additionally, the platform features a career blog powered by Indeed, offering users valuable career advice.

Delivered as a web application, users can register, log in, browse the career blog, and engage with other users by posting comments. The core feature allows users to upload their resumes and receive job recommendations based on their skillsets.

To ensure a secure and efficient platform, the application utilizes modern technologies such as password encryption with salting, SQL injection prevention, secure forms, object-oriented design, and secure file uploads. By adhering to industry-standard security practices, we ensure that the application is both intelligent and secure.

AI Problem Statements and Use Cases

AI Problem Statements:

The application focuses on matching candidates with the best job opportunities based on their resumes and skillsets. Traditionally, manual hiring processes are time-consuming and inefficient, but with the integration of AI and machine learning, we can streamline this process and make accurate, timely job recommendations. The system will specifically recommend positions in the field of mathematics based on user qualifications.

Use Cases:

One primary use case is the job recommendation system, where users submit their resumes detailing education, projects, and skills. The system then applies a machine learning model to provide job suggestions tailored to the user’s profile.

Tools:

• Deployment: Heroku
• Development: PyCharm
• Machine Learning: Jupyter Notebook
• Languages: Python, MySQL, PostgreSQL, JavaScript, HTML, CSS

Key Packages:

• Flask (for full-stack web development)
• Flask-Bootstrap (for front-end development)
• Werkzeug (for security and form validation)
• Pandas and NumPy (for data manipulation)
• Seaborn (for data visualization)
• Scikit-learn (for machine learning model development)