Machine Learning Data Analyst

VEDIKA SUDHIR SHINDE

Email: ********@**.*** Phone: +1-551-***-****

LinkedIn: https://www.linkedin.com/in/vedika-shinde/ ACADEMIC QUALIFICATION:

Master’s, Computer Science Aug 2023 – May 2025

Indiana University, Bloomington, Indianapolis

● Relevant courses: Applied Algorithm, Element of AI, Introduction to Statistics, Applied Machine Learning, Computer Vision, Machine Learning in signal processing.

B.Tech., Electronics and Communication Engineering Jul 2019 – May 2023 Maharashtra Institute of Technology, World Peace University, Pune, Maharashtra, CGPA: 9.42/10.00 TECHNICAL SKILLS:

Languages: Python, C/C++, JAVA, R programming, SQL, Data Structures, Object-oriented programming, MATLAB, Linux Domain: Machine Learning, Deep Learning, Computer Vision, Natural Language Processing, Large language models, Bigdata Machine Learning Frameworks: TensorFlow, PyTorch, scikit-learn, Keras, OpenCV, pyspark. Data Manipulation and Analysis: Pandas, NumPy, Matplotlib, Tableau, Power BI, Google Analytics, SAS, Alteryx Platforms: Azure, AWS, Docker, Excel

INTERNSHIP:

Data Analyst, Analytics Domain, Pune Mar2022- Jun 2023

● Conducted primary market research on adolescent gaming behavior, leading to a transformed and optimized dataset that improved accuracy and efficiency by 30%, facilitating data-driven strategies in gamified content development.

● Streamlined data pipeline transformation processes with SQL techniques like indexing and batch processing, achieving a 40% reduction in processing time, significantly accelerating the delivery of actionable insights in gaming trend analysis.

● Leveraged Unity 3D to create data-informed gamified content, aligning game design with analytical insights to enhance adolescent user engagement and satisfaction.

● Optimized game deployment using Docker with Unity 3D, improving deployment efficiency; adeptly managed and deployed projects on Azure, ensuring robust data security and scalability. RESEARCH EXPERIENCE:

Pedestrian Intent Estimation in Urban Areas Jan 2022 – May 2023

● Directed an innovative project aimed at improving road safety through the development of an Advanced Driver Assistance System (ADAS) for early pedestrian intent detection using advanced sensor technologies.

● Achieved a top 100 ranking in the 2022 KPIT Sparkle competition, demonstrating the project's significant contribution to technological innovation in traffic safety.

● Created a unique dataset of 800 short video clips from various urban locations in India, providing a critical resource for understanding pedestrian behavior in diverse environments.

● Engineered a MATLAB-based pedestrian intent detection simulation for ADAS, leveraging Automated Driving Toolkit, resulting in significant detection efficiency improvements, and enhanced real-time response capabilities by incorporating vehicle communication data.

● Utilized a skeletal-based approach in computer vision to precisely track pedestrian movements, resulting in a more nuanced understanding of pedestrian dynamics in urban settings.

● Implemented Deep SORT for pedestrian trajectory prediction, elevating predictive accuracy to 70% while significantly reducing false negatives and false positives by 18%. PROJECTS:

Abstractive Text Summarization, MITWPU, India July 2022- Nov 2022

● Initiated the end-to-end development and deployment of the "Abstractive Text Summarization" web application using flask.

● Conducted an extensive analysis of various transformer models, including BART, T-5, and Pegasus, to evaluate their suitability for text summarization tasks.

● Employed rigorous testing and evaluation methodologies, leveraging metrics such as ROUGE-1, ROUGE-2, and ROUGE- L to assess model accuracy and effectiveness systematically.

● Meticulously selected the T-5 transformer model, which achieved an outstanding accuracy rate of 78%, underscoring the project's commitment to delivering high-quality results. Yelp Data Analysis, Analytics Domain, India Apr - May 2022

● Led data analysis and visualization efforts, using Python and SQL to extract insights from customer reviews.

● Applied NLP and Machine Learning algorithms to classify sentiments, improving customer satisfaction analysis.

● Processed 1205 data points, optimizing data pipeline with SQL, and conducted sentiment analysis on diverse restaurant cuisines.

● Achieved a 77% accuracy rate with a predictive model, automating sentiment prediction, and enhanced findings through creating informative PowerBI dashboards.

TECHNICAL CERTIFICATIONS:

● Big Data Computing on NPTEL facilitated by IIT Patna Sept - Oct 2022

● Data Analytics with R programming on Coursera facilitated by Google Nov – Dec 2022

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