Early-career candidate learning core skills and exploring paths to launch a professional career.
A collection of early projects, coursework, and practice work that shows what I am learning and the skills I am developing.
Developed an IoT-based emergency response system using Arduino, GPS, GSM, and accelerometer sensors to detect vehicle breakdowns, accidents, and medical emergencies. Enabled real-time alerts to hospitals, police, and towing services, improving response time and highway safety
Analyzed real-time detergent sales data to identify the cause of an annual sales drop and proposed actionable insights. Utilized Excel (Pivot Tables, Charts) and Power BI for trend visualization and PowerPoint for presenting solutions to stakeholders
Early-career candidate learning core skills and exploring paths to launch a professional career.
I am Neha Shinge. I have little work experience and have not yet started my career. I am exploring options and building foundational skills as I prepare for professional roles.
Experience
Data Analytics Intern
NextGen Analytix · 01/2025 - 03/2025
Data Analytics Intern
KultureHire · 01/2025 - 01/2025
AI Intern
TechSaksham by Microsoft & SAP · 11/2023 - 01/2024
Education
PDEA's College Of Engineering
Bachelors in Computer Engineering - SGPA -9.36 · Class of 2026
Skills
Developed an IoT-based emergency response system using Arduino, GPS, GSM, and accelerometer sensors to detect vehicle breakdowns, accidents, and medical emergencies. Enabled real-time alerts to hospitals, police, and towing services, improving response time and highway safety
Analyzed real-time detergent sales data to identify the cause of an annual sales drop and proposed actionable insights. Utilized Excel (Pivot Tables, Charts) and Power BI for trend visualization and PowerPoint for presenting solutions to stakeholders
Developed a machine learning-based text classification model using Python, Scikit-learn, and Natural Language Processing (NLP) techniques to detect spam messages. Performed data cleaning, preprocessing, feature extraction (TF-IDF), and model training for classification accuracy. Utilized Matplotlib and Seaborn for visualization and result analysis
Designed an IoT-based object detection system using Raspberry Pi, YOLOv8, OpenCV, PyTorch, and pyttsx3, achieving 92% detection accuracy. Enabled real-time voice alerts to assist visually impaired individuals, enhancing safety and navigation