Machine Learning Data Science

SIRISHA GINNU

Washington, DC +1-571-***-**** ************@*****.***

SUMMARY

Data Science graduate student at The George Washington University with expertise in machine learning (95% CNN accuracy), predictive analytics, and AWS cloud engineering. Proven ability to deliver measurable impact through projects like crime trend analysis (25% patrol time reduction) and biomedical research mapping (40% faster data processing). Skilled in Python, TensorFlow, and Apache Spark, with certifications in AWS and deep learning.

EDUCATION

The George Washington University Jan 2024 - Present M.S., Data Science

• Coursework: Machine Learning, NLP, Deep Learning, Data Warehousing, Cloud Computing, Linux for DevOps Presidency University Aug 2019 - May 2023

B.E., Computer Science & Engineering

TECHNICAL SKILLS

• Programming & Databases: Python, R, Java, SQL, PL/SQL

• Machine Learning & AI: Supervised & Unsupervised Learning, Feature Engineering, TensorFlow, PyTorch

• Big Data & Cloud: Apache Spark, Data Pipelines, ETL, AWS (S3, Lambda, EC2, RDS)

• Data Visualization: Tableau, Power BI, Matplotlib, Seaborn

• Statistical Analysis: Statistical Inference, Data Modeling, Hypothesis Testing WORK EXPERIENCE

Teach nook AI Intern Jun 2022 - Aug 2022

• Engineered machine learning models, improving classification accuracy by 15% using Python, Scikit-learn, and Random Forest.

• Automated data workflows using Pandas and Scikit-learn, reducing processing time by 20%.

• Designed a Python-based productivity tool with pause/resume automation, enhancing workflow efficiency. PROJECTS

End-to-End Data Science Pipeline for Real-World Business Solutions Aug 2024 - Dec 2024

• Designed scalable ETL pipelines for 1M+ records, integrating SQL, Python, and Apache Spark for scalable data processing.

• Engineered high-impact features using domain-driven insights, boosting model accuracy by 20%.

• Created interactive Power BI dashboards for real-time monitoring, enabling data-driven decisions that cut operational costs by 15%. Crime Trend Analysis in Washington, DC Sep 2024 - Dec 2024

• Visualized intricate crime data using Tableau, uncovering a 15% spike in residential burglaries during evening hours, enabling targeted law enforcement deployment strategies and reducing incident response times.

• Conducted statistical analysis to detect seasonal crime fluctuations, improving predictive policing strategies. Movie Revenue & Rating Prediction (ML) Jan 2024 - Apr 2024

• Implemented predictive models (Gradient Boosting, SVR) with 10% MAPE accuracy in revenue forecasting, driving strategic marketing investments.

• Applied feature importance analysis to uncover key revenue drivers (budget, genre, production company). Biomedical Literature Analysis Using Neo4j & Apache Spark Jan 2024 - Apr 2024

• Architected a Neo4j graph database for 5,000+ cancer research papers, visualizing collaboration networks.

• Integrated Apache Spark for big data processing and trend analysis in scientific literature. Customer Purchasing Behavior Analysis (ML) Jan 2024 - Apr 2024

• Conducted EDA and statistical inference, uncovering 70% variance in consumer purchasing patterns.

• Constructed a Random Forest classifier (85% accuracy) paired with K-Means clustering, refining customer segmentation and boosting campaign ROI by 25%.

CERTIFICATIONS

• AWS Cloud Practitioner Essentials: AWS Skill Center

• Graphic Design Masterclass: Udemy

• Google IT Support: Google

• Deep Learning Specialization: Coursera (In Progress) Achievements

• 1st Place, AI-Driven Healthcare Solutions Hackathon (2023)

Contact this candidate