Final-year B.Tech Computer Science student with over 2 years of hands-on experience, specializing in Machine Learning, Deep Learning, and Computer Vision. Proven ability to build end-to-end ML systems and production-ready pipelines using tools like DVC and MLflow, evidenced by two IEEE conference publications. Seeking to apply strong analytical, engineering, and research skills to impactful data science challenges.
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Bachelor of Technology (B.Tech.)
Computer Science
Grade: CGPA-8.85
Courses
Operating System (OS)
Computer Organisation and Architecture (COA)
Software Engineering (SE)
Object-Oriented Programming (OOP)
Database Management (DBMS)
Statistics
Probability
Published by
IEEE ICCCNT 2024
Summary
Developed a diagnostic tool for PCOS using real-world health data, applying Gradient Boosting to achieve 90.16% accuracy with strong clinical metrics. This research's effectiveness led to its publication at the 15th IEEE ICCCNT 2024.
Published by
IEEE ICCCNT 2024
Summary
Developed a credit risk prediction system using real-world financial data, employing Stacking Classifiers to achieve 99.76% accuracy and surpass traditional models. The robustness of this system led to its publication at IEEE ICCCNT 2024, in collaboration with faculty and peers.
Python, Java, C, JavaScript, HTML, CSS.
NumPy, Pandas, Scikit-learn, Matplotlib, Seaborn, OpenCV, MediaPipe, NLTK, Flask, Streamlit, Dialogflow, TensorFlow, PyTorch, Keras, Scipy, FastAPI, AI Frameworks, Langchain, RAG, FAISS.
Supervised Learning, Unsupervised Learning, Deep Learning, Computer Vision, Natural Language Processing (NLP), Time Series Forecasting, Hyperparameter Tuning, Model Evaluation, HuggingFace Transformers, Sentence Transformers.
Exploratory Data Analysis (EDA), Feature Engineering, SQL, Power BI, Data Cleaning, Data Forecasting.
MLflow, DVC, Optuna, CI/CD, Git, GitHub, Dagshub.