Vishal Maurya

Hi, I'm Vishal Maurya

Data Science | Blockchain | AI/ML | Cybersecurity Developer

About Me

I'm a versatile developer specializing in Data Science, Blockchain, and Cybersecurity. With a strong foundation in both frontend and backend technologies, I create innovative solutions that leverage cutting-edge technologies.

My expertise includes Python (Django, Flask) for backend development, React.js and Next.js for frontend, and databases like MongoDB and PostgreSQL. I&aposm also proficient in machine learning and AI technologies, including TensorFlow, Keras, and OpenCV.

I'm also experienced with cloud platforms such as Azure Web Services and Google Cloud, which I leverage to deploy scalable and efficient solutions. My expertise extends to real-time data processing using Kafka and Zookeeper, enabling me to build robust, high-performance systems.

I'm passionate about creating secure, efficient, and scalable applications that solve real-world problems. Whether it's developing smart contracts with Solidity and Rust or implementing machine learning models, I'm always eager to take on new challenges and push the boundaries of what's possible in tech.

My Skills

My Projects

Study Buddy

Study Buddy

A collaborative learning platform for students.

PythonDjangoReact
CYBERNET

CYBERNET

A cybersecurity network monitoring tool.

PythonMachine LearningNetwork Security
Employee Performance Prediction

Employee Performance Prediction

ML model to predict employee performance.

PythonMachine LearningData Analysis
Query Agent

Query Agent

An AI-powered query resolution system.

PythonNLPAI
ContentGenie

ContentGenie

AI-driven content generation platform.

PythonNLPReact
Flexible Pay Crowdfund App

Flexible Pay Crowdfund App

Blockchain-based crowdfunding platform.

RustReactEthereum

My Contributions

SpoonShare

Contributed to a food sharing platform.

View on GitHub

Jarvis

Assisted in developing an AI assistant.

View on GitHub

stencil-cli

Contributed to a CLI tool for web development.

View on GitHub

Postman Challenge

Participated in API development challenge.

View on GitHub

Open-source Practice 1

Contributed to open-source learning projects.

View on GitHub

Open-source Practice

Participated in collaborative coding exercises.

View on GitHub

Work Experience

WIKI TECH CLUB

Road to WIKI Program (COHORT 1)

Sept 2024 – Present

  • Contributing to open-source documentation and content creation, enhancing accessibility and comprehension of technical concepts for a broader audience.
  • Collaborating with a team to refine technical articles and tutorials, improving community engagement and promoting knowledge-sharing within the Wiki Tech ecosystem.

SMART INTERNZ

Machine Learning Engineer

July 2024 – Aug 2024

  • Developed an Employee Performance Prediction model using machine learning algorithms, analyzing key features to forecast productivity, aiding data-driven HR decision-making.
  • Implemented data preprocessing, feature engineering, and model evaluation techniques to optimize accuracy; presented insights through data visualization tools to enhance interpretability and usability for stakeholders.

EDUNET FOUNDATION

Artificial Intelligence & Cloud Technology

June 2024 – July 2024

  • It was a 4-week Internship, leveraging SkillsBuild & IBM Cloud Platform in EMERGING TECHNOLOGIES (AI & CLOUD)
  • Worked with AI tools on IBM Platform for developing Chatbot which have Machine Learning integration to reply based on their Feelings and Emotions.

EI SYSTEM

Data Science Using Python

Aug 2023 – Sept 2023

  • Learned to Build Power BI dashboard to visualize Data.
  • Learned to analyze data using Python Frameworks (Such as: Matplotlib, Numpy, Pandas, Scikit-Learn, SciPy, etc.)

YBI FOUNDATION

Foundation of Data Science

June 2023 – July 2023

  • Conducted Exploratory Data Analysis (EDA) on large datasets utilizing Python and SQL to extract valuable insights for the purpose of optimizing marketing strategies and enhancing customer engagement.
  • Created and implemented predictive models using machine learning techniques such as regression and random forests resulting in a significant 15% improvement in demand forecasting accuracy.

Contact Me

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