Coding Problem Recommender

The Intelligent Coding Problem Recommendation System maps job descriptions to relevant LeetCode problems so candidates can focus interview prep around role-specific requirements instead of generic topic lists.

Software engineering job descriptions vary by stack, seniority, and domain. A strong prep workflow should understand those requirements semantically and recommend practice problems with topic diversity.

• Processed and embedded 1,571 LeetCode problems with Sentence Transformers for semantic retrieval.
• Implemented cosine similarity ranking and K-Means clustering to improve relevance and avoid repetitive recommendations.
• Built a FastAPI backend and Streamlit interface with LLM-generated explanations for why each problem matches the input role.
• Used Azure ML for deployment experimentation and model workflow management.

Delivered an interactive recommendation workflow with relevance scoring, cluster-aware diversity, and clear explanations that connect job requirements to coding practice.