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Achievement: Pull Sharkx2

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pjaiswalusf/README.md

Hi there, I'm Pratik Jaiswal! 👋

🚀 Machine Learning | Data Science | Software Engineering | Automation Engineering

I am passionate about technology and innovation, constantly seeking new ways to push the boundaries of AI, automation, and software development. With a background in Machine Learning, Data Science, and Software Engineering, I thrive on solving complex problems and creating impactful solutions.

🔧 Technical Skills

Programming Languages & Frameworks

  • Python, JAVA, C#
  • .NET Framework, RESTful APIs, MVC Architecture, Microservices

Web Development

  • Nest.js, React.js, HTML, CSS, JavaScript, Angular
  • UI/UX Design

Databases & Cloud

  • MySQL, PostgreSQL, MongoDB, Snowflake, Azure, Prisma ORM

DevOps & Automation

  • Selenium, Docker, Jenkins, CI/CD, Git, Agile Methodology, Test Automation

Machine Learning & AI

  • TensorFlow, Deep Learning, LSTM, Sentiment Analysis, NLP
  • OpenCV (Facial Emotion Recognition)

FinTech Tools

  • Stripe Payment Integration, Sanity.io CMS, Umami Analytics

📌 Pinned Projects

DEAL - A Full-Stack E-Commerce Platform

A full-stack e-commerce platform inspired by TEMU, built with Next.js, React, Prisma, and Stripe. Features include authentication, product browsing, persistent cart, secure payments, and a gamified Spin the Wheel.

Emotional Facial Recognition

A deep learning-based Emotion Recognition System utilizing Transfer Learning with ResNet50 as the base model. It classifies facial expressions into 8 emotion categories, including happiness, sadness, anger, surprise, fear, contempt, disgust, and neutrality.

Amazon USA Sales Analysis Project using SQL

Analyzed 20,000+ sales records from an Amazon-like platform using PostgreSQL. Covers sales trends, customer behavior, inventory management, and business insights through advanced SQL queries, data cleaning, and optimization. Includes an ERD, revenue analysis, CLTV, shipping delays, and stored procedures.

Stroke Prediction

Utilizes machine learning to predict stroke risk using XGBoost, Random Forest, and Logistic Regression. Incorporates advanced data preprocessing, class imbalance handling with SMOTE, and hyperparameter optimization using Optuna. Model interpretability is enhanced with SHAP to identify key risk factors.

📫 Connect With Me

🚀 Motivated by innovation and driven to build scalable solutions that shape the future of technology!

Pinned Loading

  1. DEAL---A-Full-Stack-E-Commerce-Platform DEAL---A-Full-Stack-E-Commerce-Platform Public

    DEAL is a full-stack e-commerce platform inspired by TEMU, built with Next.js, React, Prisma, and Stripe. Features include authentication, product browsing, persistent cart, secure payments, and a …

    TypeScript 3 1

  2. Emotional-Facial-Recognition- Emotional-Facial-Recognition- Public

    This project focuses on building a deep learning-based Emotion Recognition System utilizing Transfer Learning with ResNet50 as the base model. The system classifies facial expressions into 8 emotio…

    Jupyter Notebook

  3. Amazon-USA-Sales-Analysis-Project-using-SQL Amazon-USA-Sales-Analysis-Project-using-SQL Public

    This project analyzes 20,000+ sales records from an Amazon-like platform using PostgreSQL. It covers sales trends, customer behavior, inventory management, and business insights through advanced SQ…

    PLpgSQL

  4. Stroke-Prediction Stroke-Prediction Public

    This project leverages machine learning to predict stroke risk using XGBoost, Random Forest, and Logistic Regression. It incorporates advanced data preprocessing, class imbalance handling with SMOT…

    Jupyter Notebook

  5. Socio-Economic-Factors-Influencing-Crime-in-India Socio-Economic-Factors-Influencing-Crime-in-India Public

    Analyzed the impact of socio-economic factors like education, unemployment, and income disparity on crime rates in India using Python. Applied machine learning models for pattern identification and…

  6. Heart-Failure-Prediction Heart-Failure-Prediction Public

    A machine learning project predicting heart failure risk using Random Forest and XGBoost. It involves data cleaning, feature engineering, and EDA before training. The best model is saved using Jobl…

    HTML