shubhambhatia0321@gmail.com ⋄ linkedin.com ⋄ Github.com

EDUCATION

B.Tech, IT	Inderprastha Engineering College (2020-2024)
XII, CBSE	SSD Saraswati Bal mandir (2020)
X, CBSE	SSD Saraswati Bal mandir (2018)

SKILLS

Languages: Python Programming, C++, MySQL.

Developers Tools: VS Code, Git Version Control, Anaconda, Google Colab, Jupyter Notebook.

Technologies/Framework: Machine learning libraries, AWS (IAM, S3, EC2, Lambda, Lex), QuickSight, ETL, Bash scripting, Linux, Pandas, NumPy, BeautifulSoup4, Docker, Streamlit.

EXTRA-CURRICULAR & CERTIFICATION

Cognizant Artificial Intelligence Job Simulation on Forage

• Conducted exploratory data analysis (EDA) for Gala Groceries using Python and Google Colab.
• Developed Python module for training machine learning models and outputting performance metrics.
• Utilized skills: Data Analysis, Data Modeling, Data Visualization, Machine Learning, Python.

J.P. Morgan Software Engineering Virtual Experience on Forage

• Set up a local dev environment by downloading the necessary files, tools, and dependencies.
• Utilized JPMorgan Chase’s open-source library Perspective to generate a live graph that displays a data feed in a clear and visually appealing way for traders to monitor
• Utilized skills: Basic Programming, Contributing to The Open-Source Community, Financial Analysis, Web Applications, Python, Technical Communication, Git, Typescript.

PROJECTS

Netflix Catalog Trends Visualization Designed and implemented visual analytics for Netflix’s catalog using Amazon QuickSight, connecting with AWS S3 to explore trends in movie and TV show releases. (Try it here)

• Implemented a data pipeline by uploading the dataset to AWS S3 and creating manifest.json for structured data.
• Connected AWS S3 to Amazon QuickSight for seamless data analysis and visualization.
• Developed interactive visualizations using QuickSight AutoGraph to compare movies and TV shows by release year.
• Enhanced insights by applying filters to focus on content released after 2015, refining trend analysis.

Conversational-Chatbot-Groq Developed Groq API Chat Assistant to enhance Customer Support and Information Retrieval by using Large Language Models (LLMs) and NLP. (Try it here)

• Utilized LangChain API to build Streamlit applications that enhance user engagement and accessibility.
• Integrated the Groq API seamlessly for contextually relevant responses through NLP.
• Managed session state to autonomously save and display chat history to enhance user interaction.
• Improved user satisfaction and experience by implementing a robust conversational interface.

Twitter Sentiment Analysis Developed a deep learning model that trains on 1.6 million tweets for sentiment analysis to classify any new tweet as either being positive or negative (Try it here)

• Achieved 78.2% accuracy in sentiment analysis using NLP techniques
• Led implementation of NLP algorithms, improving sentiment analysis accuracy
• Optimized model performance with data preprocessing, stemming, feature extraction
• Utilized Kaggle API, manipulated Sentiment140 dataset for analysis

ONLINE COURSES

• Problem-Solving by CodeChef: Ready to apply Python enriched skill set to real-world challenges.
• Mode SQL: Exploration of all the fundamental topics such as data manipulation, querying, and database management.

RELEVANT COURSES

Data Structures & Algorithms - OOPS - Operating System - DBMS - Computer Networks - DAA - ML

ACHIEVEMENTS

• 5-star Gold Badge in SQL on HackerRank.
• 4-star Silver Badge in 30 days of code on HackerRank.
• 3-star Silver Badge in C++ on HackerRank.
• Earned Microsoft Azure AI Fundamentals: Generative AI Trophy.