Data Scientist with EDA, ML & DL Experience

RASAGNYA KUNA

**************@*****.*** — 799-***-**** — Hyderabad

LinkedIn: https://www.linkedin.com/in/rasagnyakuna/ — GitHub: https://github.com/kunarasagnya Professional Summary

Data Scientist with hands-on experience in exploratory data analysis (EDA), machine learning, and deep learning. Proficient in Python, SQL, data preprocessing, feature engineering, model training, and evaluation. Experienced in applying analytical and statistical techniques to healthcare, e-commerce, and real-world datasets to deliver data-driven insights. Education

M.Sc. Data Science — University Post Graduate College, Osmania University — 77% — 2024–2026 B.Sc. Mathematics, Statistics, Data Science — Osmania University — 96% — 2021–2024 Intermediate (MPC) — BIE Telangana — 91% — 2021

Technical Skills

Programming Languages: Python, R

Databases: PostgreSQL, SQL

Tools & Platforms: Google Colab, Jupyter Notebook, Git, GitHub, Power BI Libraries & Frameworks: Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn, TensorFlow, Keras Core Concepts: Data Analysis, Exploratory Data Analysis (EDA), Statistics, Machine Learning, Deep Learn- ing, Data Visualization, Feature Engineering, Model Evaluation Projects

Automated Detection of Peptic Ulcer Using Deep Learning Tech Stack: Python, TensorFlow, ResNet50, Transformer, Transfer Learning GitHub: https://github.com/kunarasagnya/AUTOMATED-DETECTION-OF-PEPTIC-ULCER-USING-DEEP- LEARNING

• Developed a hybrid CNN–Transformer model to classify Wireless Capsule Endoscopy images into Normal, Ulcer, and AVM categories.

• Applied transfer learning and attention-based feature learning to improve model robustness, generalization, and stability.

• Achieved 89% accuracy and 0.89 recall for ulcer class; used saliency maps for clinical interpretability and model explainability.

AI-Powered Trending Product Recommendation & Revenue Forecasting Tech Stack: Python, Pandas, NumPy, Scikit-learn, XGBoost, Seaborn, Matplotlib, Streamlit GitHub: https://github.com/kunarasagnya/AI-Powered-Trending-Product-Recommendation-Revenue-Forecasting- with-Customer-Segmentation

• Performed EDA and forecasted product revenue while identifying trending items using ML on a dataset of 42K+ Amazon electronics products.

• Implemented XGBoost regression (R

2

= 0.99) and K-Means clustering to segment products into high-demand, niche, and premium categories.

• Built an interactive Streamlit dashboard integrating analytics, forecasting, recommendations, and customer segmentation for business decision-making.

Internship

Medical Inventory Optimization — Data Science Intern – 360DigitMG (Virtual) Tech Stack: Python, SQL, PostgreSQL, AWS, Kafka, Power BI, Excel

• Developed a data-driven solution to reduce hospital inventory bounce rate by 30% and increase revenue by Rs 20L.

• Conducted data cleaning, EDA, SQL querying, and dashboarding using Python, Power BI, and AWS.

• Utilized Apache Kafka for real-time data ingestion and Generative AI techniques (LLMs) for actionable busi- ness insights.

Certifications

• Tata Group Data Analytics Job Simulation – Forage

• Machine Learning – NIMSME

• Data Analytics using Python – Learning Links

• Tableau Visualization Workshop – Pantech

1

Contact this candidate