Available for Opportunities

PRIYANSHU VISHWAKARMA

I am a Full Stack Developer

Building intelligent, scalable, and user-focused applications using modern web technologies, machine learning, and Generative AI. Bridging cutting-edge research with production-grade engineering.

View My Work Download Resume
Connect:
AI ENGINE ARCHITECTURE v2.6
DATA → MODEL → INSIGHT → APP
$ python -m py_engine.train --model XGBoost
[INFO] Loading Customer Dataset (n=7,043)...
[SHAP] Computing Kernel Explainer matrix...
[ACCURACY] Model ROC-AUC: 0.923 | F1: 0.88
[DEPLOY] Exposing REST API on port 8000 ✓
Real-time Inference Latency: 14ms
About Me

Building with curiosity. Creating with purpose.

Computer Science Engineering Undergraduate 2023 - 2027

I am an undergraduate Computer Science Engineering student at G. V. Acharya Institute of Engineering and Technology (University of Mumbai). My focus spans Full Stack Web Development, Machine Learning, Generative AI, and Agentic AI architectures.

Whether architecting robust REST APIs with Node.js & Express, training XGBoost models with SHAP explainability, building Web3 crypto applications, or conducting academic research published in peer-reviewed international journals, I thrive at the intersection of practical code execution and data-driven intelligence.

Core CS Topics
Data Structures & Algorithms Object Oriented Programming (OOP) Database Management Systems (DBMS) Operating Systems Computer Networks

Technical Foundation

A comprehensive toolkit cultivated through rigorous coursework, practical internships, open source development, and published machine learning research.

Languages
Python JavaScript C SQL
AI & Data Science
Machine Learning Scikit-learn Pandas NumPy LLMs Generative AI Agentic AI
0.00 / 10
Cumulative CGPA
0
Major Projects
0
Internships / Industry
0
Published Paper
Capabilities

Interactive Technology Wall

Hover over or filter technologies to explore my technical stack across software development, data science, and core computer science.

Python
Python
Programming
C Language
C Language
Programming
JavaScript
JavaScript
Programming
HTML5
HTML5
Frontend
CSS3
CSS3
Frontend
Vite
Vite
Frontend
Node.js
Node.js
Backend
Express.js
Express.js
Backend
MySQL
MySQL
Database
SQLite
SQLite
Database
Pandas
Pandas
Data Science
NumPy
NumPy
Data Science
LLMs
LLMs
AI / ML
Generative AI
GenAI
AI / ML
Agentic AI
Agentic AI
AI Architecture
Git & GitHub
Git & GitHub
Tools
VS Code
VS Code
IDE
Postman
Postman
API Testing
OOP Concepts
OOP Concepts
Core CS
DBMS
DBMS
Core CS
Operating System
Operating Systems
Core CS
Computer Networks
Computer Networks
Core CS
Career & Practical Track

Professional & Research Experience

May 2025 – June 2025 Virtual Internship

Artificial Intelligence & Generative AI Intern

YBI Foundation
  • Applied core Python and machine learning principles to solve practical data science challenges.
  • Worked on project-based AI & Generative AI assignments, building hands-on mini projects.
  • Explored real-world AI model applications, prompt structuring, and LLM inference integrations.
Nov 24, 2025 – Jan 30, 2026 Project Internship

Dynamic Text Analysis Platform

Infosys Springboard
  • Engineered an automated document summarization and sentiment analysis web platform.
  • Implemented NLP keyword and entity extraction algorithms for structured insight generation.
  • Built interactive data visualizations to present sentiment distribution and topic graphs.

NLP ENGINE SANDBOX

Infosys Platform Demo

Try the live NLP Sentiment & Entity Extraction widget built during the Infosys Springboard project:

Sentiment Polarity: POSITIVE
Score Confidence: 92% Confidence
Extracted Key Phrases:
XGBoost Model Accuracy
Portfolio

Things I've Built

Explore production-grade full stack applications, Web3 systems, explainable machine learning architectures, computer vision platforms, and Generative AI builders.

Customer Churn Analytics Dashboard
Research-Based Featured

Customer Churn Analysis & Retention Master Plan

An end-to-end machine learning system that predicts customer churn, provides SHAP explainability, and converts risk insights into retention strategies.

React.js Python XGBoost SHAP
NutriFit AI Health Dashboard
Generative AI

NutriFit AI

Medical document OCR & LLM-driven personalized diet and fitness roadmap system with integrated AI health guidance.

Python OCR LLMs React.js
Web3 Crypto Wallet Dashboard
Web3 & Crypto

Web3 Crypto Wallet

A cryptocurrency wallet platform with seed phrase key vaults, multi-token balance tracking, live market feeds, and token swaps.

JavaScript Web3.js Node.js Crypto
Smart CV & Resume Generator UI
AI Builder

Smart CV & Resume Generator

An AI-assisted resume builder with side-by-side live document preview, ATS keyword optimization scoring, and PDF export.

JavaScript Generative AI HTML5/CSS3 PDF
Facial Recognition Attendance System
Computer Vision

Student Attendance System

Automated facial recognition attendance platform built with OpenCV and Node.js with real-time student tracking.

Node.js Python OpenCV MySQL
NLP Dynamic Text Analysis Platform
NLP & Analytics

Dynamic Text Analysis Platform

Infosys Springboard project performing automated summarization, sentiment classification, and entity graphs.

Python AI/ML NLP
⭐ FEATURED RESEARCH

Research & Publications

From building systems to contributing knowledge. Advancing machine learning explainability and proactive retention architectures.

Published Research • 2026 e-ISSN: 2582-5208
✓ Peer Reviewed • Open Access • IRJMETS Vol. 08, Issue 04

Customer Churn Analysis: Prevention Tactics and Retention Masterplan

Authors: Saurabh Kumar Mishra, Shivesh Vishwakarma, Priyanshu Vishwakarma (3rd Author), Patole Vrunda, Prof. Shalaka Patkar
Department of Computer Science & Engineering, G. V. Acharya Institute of Engineering and Technology, University of Mumbai, Maharashtra, India.
Journal: International Research Journal of Modernization in Engineering Technology and Science (IRJMETS) | April 2026
Publication Abstract

"A machine learning-based customer churn analysis and retention system integrating XGBoost prediction, SHAP explainability, a risk-based retention strategy engine, conversational AI, and an interactive visualization dashboard."

Download Publication Paper Ref: IRJMETS-V8-I4-2026

Core Research Contributions

01

XGBoost Prediction

Primary gradient-boosted classification model trained for high-precision churn risk identification.

03

Risk Tiering

Classifies customers into High Risk (>0.70), Medium Risk (0.40–0.70), and Low Risk (<0.40) segments.

04

Retention Strategy Engine

Dynamically maps risk score drivers to automated retention interventions and customer outreach actions.

05

Conversational AI

Enables non-technical business users to query customer risk profiles using natural language prompts.

06

Visualization Dashboard

Delivers executive-level visibility into churn drivers, revenue loss trends, and strategy metrics.

Published Model Performance

Empirical benchmark metrics strictly matching the published journal paper results:

91.3%
Accuracy
0.923
ROC-AUC
0.88
F1 Score

MODEL COMPARISON BENCHMARK IRJMETS Empirical Data

XGBoost (Proposed System) 91.3% Acc | 0.923 ROC-AUC
Random Forest 86.4% Acc | 0.897 ROC-AUC
Logistic Regression 79.6% Acc | 0.831 ROC-AUC

End-to-End Research System Architecture

Interactive node pipeline: Hover over nodes to inspect component roles.

Interactive Diagram
NODE 01
Customer Data
Telemetry, Demographics, Billing
→
NODE 02
Data Preprocessing
Encoding & Imputation
→
NODE 03
XGBoost Model
Probability Inference
→
NODE 04
SHAP Explainability
Feature Attribution
→
NODE 05
Risk Tiering
Low / Med / High Segmentation
→
NODE 06
Retention Strategy
Action Recommendation
→
NODE 07
AI Assistant + UI
Conversational Dashboard
Credentials

Certifications & Recognitions

Oracle Logo

Oracle Cloud Infrastructure 2025 AI Foundations Associate

Oracle Cloud • 2025

Validated proficiency in cloud-based Artificial Intelligence fundamentals, machine learning workflows, and OCI AI service architectures.

Claude AI Logo

Claude 101 — Anthropic

Anthropic Educational Program

Specialized training in large language model capabilities, prompt design patterns, API integration, and AI safety practices.

Edunet Foundation Logo

Green Skill and Artificial Intelligence

Edunet Foundation

Applied coursework exploring sustainable tech practices and green computing combined with practical AI implementations.

Academic Background

Education

G. V. Acharya Institute of Engineering and Technology

Bachelor of Engineering — Computer Science Engineering
University of Mumbai, Maharashtra, India

Pursuing a comprehensive curriculum in Computer Science Engineering, combining theoretical computer science fundamentals with advanced research projects and practical software development.

Cumulative CGPA
7.75 / 10
Expected Graduation
July 2027
Professional Attributes

Soft Skills

Problem Solving Analytical Thinking Team Collaboration Communication Adaptability
Fluency

Languages

English
Professional
Hindi
Native / Fluent
Get In Touch

Let's build something intelligent.

Have an idea, research collaboration, internship opportunity, or software project in mind? Let's connect.

Contact Information

Location
Mumbai, Maharashtra, India
Email Me LinkedIn GitHub
Priyanshu Vishwakarma
Full Stack Developer × AI/ML Engineer
© 2026 Priyanshu Vishwakarma. All rights reserved.
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