AI Career Readiness Analyzer

Analyze student skills, identify gaps, and get AI-powered career readiness insights.

Comment

Inspiration

As a student, I noticed many peers struggle to understand how their skills match real-world job requirements. Most career readiness tools are generic, hard to interpret, or not student-focused. I wanted to create an AI-powered tool that gives personalized, actionable insights, helping students identify skill gaps and take concrete steps to improve their employability.

What it does

AI Career Readiness Analyzer:

  • Accepts student-entered skills or resume text
  • Compares them against real IT job roles and required skills
  • Calculates a Career Readiness Score (0–100)
  • Highlights missing skills for chosen job roles
  • Suggests learning paths to bridge gaps using reputable platforms

The tool provides students with a data-driven, personalized assessment rather than generic advice.

How we built it

  • Data: Used the publicly available Hugging Face dataset NxtGenIntern/IT_Job_Roles_Skills_Certifications_Dataset with real IT job roles and skills
  • Preprocessing: Cleaned the text, removed duplicates, standardized skill descriptions
  • Feature Engineering: Converted skills into numerical vectors using TF-IDF
  • Modeling: Built a Logistic Regression model to assess skill readiness
  • Scoring: Calculated readiness scores with cosine similarity and skill coverage metrics
  • App: Developed a Streamlit dashboard for students to input skills and view results, including visualizations of missing skills and suggested learning paths

Challenges we ran into

  • Dataset inconsistencies: Skills were described differently for similar job roles, so we had to standardize and clean text carefully
  • Balancing complexity and simplicity: Ensuring the AI calculations were meaningful yet the app remained beginner-friendly
  • Realistic recommendations: Designing learning paths that are actionable and not overwhelming

Accomplishments that we're proud of

  • Built a fully functional, end-to-end AI application from scratch
  • Automated data loading, preprocessing, and scoring
  • Created a user-friendly dashboard that students can use immediately
  • Ensured ethical AI practices with transparent scoring and no bias

What we learned

  • How to use TF-IDF and cosine similarity to evaluate skill overlap
  • Best practices for data preprocessing and text cleaning
  • Building interactive web applications with Streamlit
  • Importance of clear communication of AI results for end-users

What's next for AI Career Readiness Analyzer

  • Integrate resume parsing to extract skills automatically
  • Link with LinkedIn API for real-time job matching
  • Add multi-domain support (Healthcare, Finance, etc.)
  • Improve learning path recommendations with personalized course links
  • Expand analytics dashboard for universities and career centers

Built With

  • logistic-regression)-streamlit-(web-dashboard)-hugging-face-datasets-matplotlib-/-plotly-(optional-visualizations)-open-source-learning-resources-(coursera
  • numpy-scikit-learn-(tf-idf
  • python-3.x-pandas
Share this project:

Updates