Hi, I'm Suparn Nayak 👋

🚀 Aspiring Machine Learning Engineer passionate about building AI-powered solutions for real-world problems.
Currently working on Drone-based Computer Vision, Machine Learning projects, and enhancing my skills in Deep Learning, MLOps, and DSA.

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🔭 I’m currently working on

• Drone Person Detection & GPS Mapping using YOLO + SORT
• KCET Admission Predictor using ML on historical cutoff data
• Movie Recommender System (Content-based filtering)

🌱 I’m currently learning

• Deep Learning (CNNs, RNNs, Transformers)
• MLOps & Model Deployment
• Advanced DSA for interview preparation

👯 I’m looking to collaborate on

• AI/ML projects in Computer Vision or NLP
• Open-source Python utilities
• Deep learning

🤔 I’m looking for help with

• Optimizing ML models for real-time drone processing
• Implementing large-scale MLOps pipelines

💬 Ask me about

• Python, Machine Learning, and Computer Vision
• Data Structures & Algorithms
• Deploying ML models with Flask, Render, or Google Cloud

📫 How to reach me

• LinkedIn: linkedin.com/in/suparnnayak
• GitHub: github.com/suparnnayak
• Email: [email protected]

😄 Pronouns

He/Him

⚡ Fun fact

My ML models can predict results with high accuracy… but they still can’t predict when my coffee will run out. ☕🤖

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🛠 Skills & Technologies

• Languages: Python, SQL, Java
• ML/DL Frameworks: Scikit-learn, TensorFlow, PyTorch, OpenCV
• Tools & Platforms: Git, GitHub, VS Code, Jupyter Notebook, Google Cloud, Render
• Core CS: Data Structures & Algorithms, DBMS, Operating Systems
• Special Interests: Computer Vision, NLP, Predictive Modeling, Drone Automation

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📌 Featured Projects

• Drone Person Detection & GPS Mapping – Person detection using YOLO, SORT tracking, and GPS coordinate mapping for drone surveillance.
• Movie Recommender System – Content-based filtering using TF-IDF & Cosine Similarity on TMDB dataset.
• KCET Admission Predictor – ML model predicting college admission chances based on past cut-off data.

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📊 GitHub Stats

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💡 “Code, Create, and Conquer — one project at a time.”