Carrer Compass: A Career Path Recommender using Machine Learning

In the ever-expanding corporate realm, making an appropriate career choice is crucial for building a successful career. Students often face confusion due to increased choices or lack of guidance, leading to missteps in career selection. Such decisions might result in individuals working in fields unsuited to their strengths and interests, reducing overall efficiency. The proposed project highlights the need for a personalized career prediction system. Traditional methods often fall short in capturing the interests and strengths of individuals while solely depending on standard tests. This research study focuses on building a personalized career recommendation system based on the skills and interests of an individual. The project promotes the idea of dynamic and tailor-made tests for identifying suitable careers and an interactive, user-friendly mobile application using the Flutter framework. Machine Learning models like Random Forest and Support Vector Machine are employed for predicting and recommending careers. By evaluating personality traits, interests, and skills, the application will recommend a career. Continuous improvement is fostered through user feedback, ensuring the model's adaptability to changing job markets. This approach provides a comprehensive solution to challenges faced by individuals while choosing a career.