Inspiration
Interviews can be really tough. Impressing someone you've met just half an hour ago, but whose impression of you holds the future of your career, seems daunting!. For that very reason many job seekers are looking to hone their interview skills in order to get their dream job. But there is a surprising lack of modern and effective tools for interview preparation. The most effective way to prepare for interviews is by simulating an interview environment and learning from your mistakes. And that's what we do!
What it does
Intermeow enables prospective employees to practice their interview skills, we provide varied questions based on their skills, and provide a detailed analysis of their answers for the question.
How we built it
We used Google Cloud Platform Speech and Natural Language APIs to obtain and analyze the responses for interview questions given by the user, and provide a detailed report of the user's performance using HTML, CSS,and d3. In particular, we ask a variety of code- related questions, and we search for communication efficiency, i.e. through the number of filler words the user uses. To obtain the interview questions, we pulled data from multiple sources such as StackOverflow, HackerRank, and Geeks4Geeks.
Challenges we ran into
We were hoping to obtain information of the user from a LinkedIn profile, but we were unsuccessful in obtaining full permission authentication from their API.
Accomplishments that we're proud of
We're most proud that our project integrates code from multiple APIs, frameworks, and languages, and being able to build the entire stack in languages we had previously had little experience in.
What we learned
We learnt how to authenticate oneself to use Google APIs, as well as how to integrate python scripts into web applications built using Python Flask. We also learned D3 to visualize and portray our results.
What's next for InterMeow
Request full profile access from LinkedIn. Implement real-time video intelligence analysis to provide more information to the user.
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