Shubhangi Mule

About

Motivated Computer Engineering student with hands-on experience in web development, data analytics, artificial intelligence, and machine learning. Interested in applying technical and problem-solving skills to build practical, data-driven solutions.

Education

• B.Tech in Computer Science Engg
  Sanjivani College of Engineering, Kopargoan
  Computer Science Engineering · 2023 – 2027
• Higher Secondary Certification
  Jijamata Junior College, Bhenda
  2023
• Secondary School Certification
  Jijamata Vidyalaya, Bhenda
  2021

Skills

• Communication
• SQL
• CSS
• Data Visualization
• GitHub
• HTML
• Adaptability
• Data Analytics
• Time Management
• Teamwork
• Python
• Machine Learning
• VS Code
• JavaScript
• Pandas
• scikit-learn
• DBMS
• Empathy
• C++
• Data Structures & Algorithms
• Oop

Projects

• Online Food Ordering System
  Java, SQL, HTML, CSS, JavaScript

  Developed a web-based food ordering system with menu management, order processing, customer records, and database integration.

• Smart Air Ventilation System
  IoT, Sensors

  Designed and developed an IoT-based smart air ventilation system for real-time indoor air quality monitoring and automated ventilation control. Integrated environmental sensors to collect and analyze air quality data, improving system responsiveness.

• Employee Attrition Prediction Using Machine Learning
  Machine Learning, Data Analytics

  Developed a machine learning model to predict employee attrition using HR analytics data. Performed data preprocessing, exploratory data analysis, and model evaluation to identify factors influencing employee turnover.

Courses & certifications

• Generative AI for Engineers · CodeSignal · 2026
• English For Thesis Writing · NPTEL · 2026
• AI Nexus Hackathon · 2026
• SheInspires Zensar Hackathon · 2026

🏆 Achievements & awards

• Research Paper: Employee Attrition Model: A Data-Driven Approach to Workforce Analytics
• Research Paper: IoT Based Smart Ventilation and Air Quality Monitoring System
• Copyright Registered: Employee Attrition Detection using Machine Learning