Muhammad Danish Nadeem
Generative AI Developer
New to Contra
Muhammad Danish is ready for their next project!
Engineered a personalized news recommendation system leveraging the Microsoft MIND dataset to deliver highly relevant, user-centric content at scale. The solution models user engagement patterns through collaborative filtering techniques to predict and surface articles aligned with individual reading behavior. It encompasses a complete machine learning pipeline, including data preprocessing, model training, evaluation, and performance validation using click-through-based metrics on real-world interaction data.
0
18
This Music Genre Classification System features a modern, AI-powered dashboard designed to classify music genres from uploaded audio files. The interface provides a seamless user experience with a drag-and-drop audio upload section, real-time prediction results, confidence score visualization, and probability distribution across multiple genres.
The dashboard follows a clean dark-theme design with vibrant purple accents, making it visually appealing while maintaining usability. Users can upload audio files in various formats, view the predicted genre instantly, and analyze model confidence through interactive charts and progress bars. Additional features such as prediction history and workflow guidance enhance transparency and user engagement.
Overall, the frontend effectively combines machine learning functionality, intuitive user interaction, and modern UI/UX principles to create a professional music genre classification platform suitable for academic projects, research demonstrations, and production-ready AI applications.
0
23
AI-Powered Resume Screening System
Tired of manually sifting through hundreds of resumes? This intelligent screening system does the heavy lifting — automatically parsing resumes, extracting key skills, and semantically matching candidates to job descriptions in seconds.
🧠 How It Works
The system uses advanced Natural Language Processing (NLP) to deeply understand both resumes and job descriptions — going far beyond simple keyword matching. It calculates semantic similarity using cosine similarity, meaning it understands context, not just words.
⚙️ Key Features
📄 Smart Resume Parsing — Automatically extracts skills, experience, and qualifications from any resume format
🔍 Semantic Job Matching — Matches candidates to roles based on meaning, not just keywords
🏆 Candidate Ranking — Instantly ranks applicants by relevance score
📊 Match Scoring — Clear percentage-based compatibility scores for every candidate
🕳️ Skill Gap Analysis — Identifies exactly what skills a candidate is missing for a role
🚀 Streamlit Dashboard — Clean, interactive UI deployable in one click
🛠️ Tech Stack
Python · NLP · Scikit-learn · Cosine Similarity · Streamlit · SpaCy / NLTK
💼 Perfect For
HR teams, recruitment agencies, startups, and any business drowning in job applications — this tool cuts screening time by up to 80%.
📈 Results It Delivers
✅ Faster hiring decisions
✅ Bias-reduced candidate evaluation
✅ Clear, data-backed shortlisting
✅ Scalable to thousands of resumes
0
31
Drug discovery usually takes 10 to 15 years, our service proposes a way to lower the time and the cost by an estimated ten years, by simulating drug molecule behaviour with protein, docking, to give researchers a tool to speed up clinical trials by instead relying on simualtions
0
45