pAthfInder

Inspiration

We were inspired by the need for students to effectively develop the skills required for their desired job positions. Recognizing that many students struggle with identifying the right skills and managing their time effectively, we aimed to create a comprehensive tool to streamline this process.

What we do

pAthfInder is an application designed to assist students in building essential skills for their future careers. Users can input their desired job positions, and the app recommends relevant skills. It features a skills tracker for monitoring progress, a scheduling tool that respects users' availability, and an integrated ML model that learns from user input to optimize study schedules. Once users complete their training, they can access a certifications page that links to various certification providers.

How we built it

We developed pAthfInder using a React and TypeScript frontend paired with a Python Django backend. This tech stack allows for a dynamic user interface and robust server-side processing. We are hosting the application on Microsoft Azure. The application integrates user input forms for job positions and skills tracking, utilizes a calendar upload feature to manage training sessions, and employs machine learning to analyze user data and suggest optimal study times. We also created a certification resources page to provide users with pathways to obtain relevant certifications.

Challenges we ran into

One of the primary challenges we encountered was deploying pAthfInder onto the cloud, which involved configuring Azure services to support our application. Additionally, we faced difficulties in fixing the models to ensure they accurately interpreted user data. We also aimed to integrate APIs from platforms like LinkedIn, Glassdoor, LeetCode, and HackerRank, but we could not connect due to the absence of feasible APIs or because those available were enterprise-only.

Accomplishments that we're proud of

We are proud of the seamless integration of multiple features into a cohesive application that directly addresses student needs. The ability of our ML model to learn and adapt based on user interactions is a significant accomplishment, as is the creation of a user-friendly interface that encourages ongoing engagement with skill development. We also have a chatbot that students can interact with and get information from in a very conversational and friendly manner.

What we learned

Through this project, we learned the importance of user feedback in shaping application features and functionality. We discovered that there’s a fine line between pivoting and committing to one topic; while we had to shift our ideas slightly from the beginning, we ultimately settled down and successfully built a product. We also gained valuable insights into the complexities of machine learning implementation and calendar management. Furthermore, we learned how crucial it is to maintain a balance between robust functionality and an intuitive user experience.

What's next for pAthfInder

Moving forward, we plan to enhance pAthfInder by incorporating more advanced machine learning techniques to improve skill recommendations and scheduling suggestions. We aim to expand our database of job positions and associated skills while also adding features such as community forums for users to share experiences and tips. Additionally, we are exploring partnerships with educational institutions to provide users with exclusive access to training resources and certifications.

Built With

• azure
• django
• python
• react
• shadcn
• tailwindcss
• typescript
• vercel
• vite