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Experienced in developing and deploying machine learning pipelines with hands-on expertise in data streaming, computer vision, and natural language processing.\n\n---\n\n## Education\n- **VIT Bhopal University, B.Tech in CSE with specialization in Health Informatics**  \n  *CGPA:* 8.67\n- **Delhi Public School, Ruby Park, Class 12 (CBSE)**  \n  *Score:* 86%\n- **Calcutta Public School, Kalikapur, Class 10 (ICSE)**  \n  *Score:* 94%\n\n---\n\n## Experience\n\n**Machine Learning Intern – PreProdCorp**  \n*Jan 2024 – Feb 2024*  \nOffered placement after securing 4th place in Buildathon 2024. Worked on agile-based ML pipelines involving Apache Kafka, Linux/WSL, and PySpark for data preprocessing. Built tree-based models and a Deep Q-Network for CartPole using reinforcement learning. Contributed to full-stack workflows with MLFlow, DVC, and Dagshub, and explored NLP using Hugging Face (DistilBERT, RoBERTa, GPT-2, T5). Also led sentiment analysis on Amazon Reviews with TF-IDF, Word2Vec, and fine-tuned models.  \n**Codebases:** [Internship Work](https://github.com/Recker-Dev/PreProd-Internship-Work) | [Exploring NLP](https://github.com/Recker-Dev/Exploring-NLP)\n\n---\n\n## Skills\n- **Hardware:** IoT, Raspberry Pi  \n- **Languages:** Python, SQL, Java, C++  \n- **Frameworks:** Pandas, Scikit-Learn, PyTorch, Matplotlib, LangGraph, LangChain  \n- **Soft Skills:** Project Management, Problem Solving\n\n---\n\n## Projects\n- **GenAI Based Mini-CDSS:**  \n  Developed a Streamlit application to assist doctors during initial patient encounters by generating preliminary reports and diagnoses, enhancing clinical decision-making.  \n  [GitHub](https://github.com/Recker-Dev/Mini-CDSS-Streamlit-Frontend)\n\n- **IoT-based Network Attack Predictor:**  \n  Created a machine learning tool to detect cyberattacks in IoT healthcare networks using PCA for dimensionality reduction and an ANN for classification. Coupled with a FastAPI backend and Streamlit UI for real-time inference.  \n  [GitHub](https://github.com/Recker-Dev/IOT-Healthcare-Network-Traffic-Attack-Predictor)\n\n- **Alzheimer Detection:**  \n  Implemented a TinyVGG16-based CNN to classify MRI scans into stages of Alzheimer's Disease (No Impairment, Very Mild, Mild, Moderate Impairment) and built a Streamlit app for user-friendly inference.  \n  [GitHub](https://github.com/Recker-Dev/alzheimer-cnn-tinyVGG16)\n\n- **Sign Language Detection Prototype:**  \n  Developed a prototype to recognize sign language gestures using machine learning, currently under re-evaluation for performance improvements.  \n  [GitHub](https://github.com/Recker-Dev/Project-Sanket-Sign-Language-Detection-Prototype)\n\n- **Upcoming Project – IoT-based Dynamic Attendance Management System:**  \n  Designing a system that manages attendance dynamically based on time slots, leveraging IoT technologies for enhanced accuracy and efficiency.\n\n---\n\n## Achievements\n- **4th Place** in Buildathon 2024 at VIT Bhopal (offered placement with PreProdCorp)\n- **6th Place** among 500+ teams in a Johns Hopkins University HealthHack organised in association with VIT Bhopal\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frecker-dev%2Fresume","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frecker-dev%2Fresume","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frecker-dev%2Fresume/lists"}