JobTinder

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As students in BCIT's CST program we were anxious about our future careers and any potential upcoming downturns in the technology job field. When hack the break was announced with the theme of job security we knew that we all had an opportunity to work on something that would help alleviate our anxieties and help us in our careers going forward.
JobTinder is a job search buddy app that uses machine learning algorithms to match job seekers with suitable jobs based on their skills, experience, and preferences. It provides a platform for job seekers to upload their resumes, and receive feedback on their resumes. Additionally, JobTinder aggregates job listings from various sources, making it a one-stop-shop for job seekers to find and apply for jobs. Overall, JobTinder aims to simplify and streamline the job search process for job seekers. For building JobTinder we used HTML, JavaScript, and CSS for the front-end development of the web application. For the back-end development, we used Python as the main programming language and the Flask web framework for building the server-side logic. Additionally, we utilized the Selenium web testing framework for automating testing tasks and Beautiful Soup for web scraping and data extraction. Time constraint was our biggest challenge. We felt that if we had further time to work on our project we would have deployed and delivered a prize-winning project, however we're all happy with our progress as it stands and we're proud of our contributions and teamwork and the comradery we all exhibited throughout the hackathon . Working on this hackathon challenge taught us that extensive testing and user feedback is absolutely essential to creating a user-friendly app. We took turns testing things others made so that we had continuous feedback throughout the design/implementation process. Effective communication and teamwork were also vital aspects of our success without which we would not have been able to deliver our finished product in time. Going forward we have other features in mind which we would like to add, like extensive resume feedback, resume editor options and a user profile. In the future, we also intend to use a machine learning model that uses linear regression to fine tune feedback and the score rating for resumes. This would give even better insights to the user, however this could not be implemented at present due to the time constraint.

Built With

• beautiful-soup
• css
• flask
• html
• javascript
• python
• selenium