Career-Climb: An Application To Support Students Drom University In Searching Job Opportunities And Applications

Abstract

Nowadays, technology is crucial in modern life for its convenience and ease of use. However, technology applications for careers should be more accessible and user-friendly for everyone. It is essential to develop an application with a personalized experience and comprehensive support functions for both employers and job seekers. Online job searching platforms play a crucial role in connecting employees and employers, as well as in predicting an employee’s character through their answers to given questions. This thesis focuses on developing a job-search application on a web platform for easy access from anywhere with an internet connection. The solution involves using technologies from the NodeJS library and implementing a personality prediction model for job seekers. Predictive modeling is a mathematical process used to forecast future events or outcomes by analyzing patterns in a given set of input data. This process relies on users providing measurements and filling out forms on the website. Subsequently, an algorithm can be implemented using the user's form-filling information(Myers–Briggs Type Indicator). Furthermore, the NodeJS library offers numerous resources to support the ultimate website construction. This greatly aids developers in research and saves time in building user interfaces. Together, these technologies provide a robust toolkit for creating a job-finding website, enabling developers to build scalable, efficient, and secure applications that deliver a personalized experience to users. The method involves the following steps: Data discovery: Kaggle provides a wide range of reliable datasets for developers to train useful models for their applications. Developers can easily explore and access datasets on Kaggle that cover various topics such as character analysis. (The link to the original dataset[24]: https://www.kaggle.com/datasets/anshulmehtakaggl/60k-responses-of-16-personalities-test-m bt/data) Data Preprocessing: Once collected, the data must undergo preprocessing before analysis. This includes cleaning the data, removing duplicates, and converting it into an easily analyzable format. Feedback and Improvement: The final step involves continuously gathering user feedback and enhancing the prediction model based on this feedback. This ensures that the model provides accurate predictions and timely advice to users.