The problem JOB RECOMMENDATION solves

An AI-based job recommendation system solves the problem of inefficiency and ineffectiveness in the traditional job search process. The traditional job search process can be time-consuming and frustrating for job seekers, who may have to sift through numerous job postings that may not match their skills, experience, or preferences. This can lead to a mismatch between job seekers and job opportunities, resulting in a less satisfying employment outcome for both the job seeker and the employer.

An AI-based job recommendation system solves this problem by using machine learning algorithms to analyze job seeker and job data and identify the best matches based on factors such as job requirements, job seeker skills, education, and work experience. This leads to more personalized and relevant job recommendations for job seekers, increasing the likelihood of finding suitable employment opportunities.

Challenges we ran into

DATA QUALITY:
The success of an AI-based job matching system is heavily dependent on the quality and quantity of the data used to train the machine learning algorithms. Ensuring that the data is accurate, relevant, and up-to-date can be a significant challenge.

USER ADOPTION:
Even the best-designed AI system will not be successful if users do not adopt it. Developers need to ensure that the system is user-friendly and provides real value to job seekers and employers.

SCALABILITY:
As the number of users and job postings increases, the system needs to be able to handle the increased load without compromising performance.

DEPLOYMENT:
http://datageekstechnoverse.pythonanywhere.com/
Issue with the module installations, will be resolved shortly.