About Me
I’m Vibhanshu Botke, a final-year Electrical Engineering student at NIT Surat, specializing in data science, software development, and programming languages such as Python, Java, and C. With a deep passion for technology and problem-solving, I’ve honed my skills in SQL, web development, and AI. Driven by curiosity and a desire to stay ahead of emerging trends, I continuously explore new domains within data science and artificial intelligence. I’m eager to apply my diverse skill set to build innovative solutions that drive progress. Let’s collaborate and create impactful experiences together.
Skills
Experience
Education
Internship
- Programming Languages
Python, Java, SQL, JavaScript, C, MATLAB, LaTeX -
Computer Fundamentals
Data Structures and Algorithms, DBMS, Operating Systems (OS), System Design -
Data Science
AI/ML, Data Mining, Data Visualization, Data Scraping, Statistical Analysis -
Tools & Technologies
MySQL, Git, GitHub, Docker, Fabric, Postman -
Web Technologies
MongoDB, Express.js, React.js, Node.js, HTML, CSS, WordPress - Environments
Linux, Ubuntu, Azure, VSCode, PyCharm, Eclipse, AWS
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Cutting Edge Visionaries (CEV), NIT Surat
Technical Head -
Co-Curricular Affairs Council, NIT Surat
Website Development Head - MINDBEND'24 - ACM, NIT Surat
Senior Executive
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B.Tech(EE), NIT Surat , 2021-25
CGPA - 8.36 -
HSC, Navrachana Vidyani Vidyalaya, Vadodara , 2021
93.5% in Class 12th -
SSC, Delhi Public School, Vadodara, 2019
92.1% in Class 10th
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Amazon
Manager - I Intern
- Managed a team of 180+ in the Outbound Department, improving performance metrics by 9%.
- Collaborated with cross-functional teams and leveraged data analysis to improve key quality metrics, achieving a 50% improvement and reduced cost-related losses by 44%.
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Aeiota Solutions, Noida
Data Science Intern
- Implemented an AI-powered call transcript and summary generator with a speech recognition accuracy of 88% and a summary coherence score of 82%, extracting critical call insights for companies.
- Created 600+ banking features and designed an ML model for money-lending companies to evaluate loan eligibility based on customer banking profiles.
- Collaborated with a team of 4 on a business rule engine that enables clients to implement custom rules without coding and thus streamlines operational processes.
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Dr. Sukanta Halder, Surat
Research Intern
- Built a real-time machine learning model to categorize driver activities, distinguishing between safe and risky behaviors with 98.75% accuracy.
- Processed 102,150 in-car images using a CNN architecture and integrated OpenCV and MediaPipe to detect driver activities and distracting facial features.
