Hello, I'm Gagandeep

Welcome to my portfolio!

About Me

I am a dedicated machine learning enthusiast, skilled in Python, SQL, and Git. I thrive in exploring innovative solutions in machine learning, AI, and data visualization, turning complex datasets into meaningful insights. Passionate about transforming raw data and communicating technical findings effectively, I enjoy the challenge of uncovering patterns and making data-driven decisions. Join me as we harness the power of data to shape the future of technology!

My Experience

This is my past experience.


Co-op Student - Industry Project

January 2025 - April 2025

ACE Red River College Polytech - Winnipeg
  • Collaborated with a cross-functional team to contribute to the development and enhancement of a software project aligned with real-world business needs
  • Utilized industry-standard tools and methodologies to maintain clean, efficient, and well-documented code
  • Ensured code quality and performance through testing, debugging, and version control practices
  • Led the team as Scrum Master by facilitating sprint planning, stand-ups, and retrospectives, ensuring effective collaboration and timely delivery of project milestones.
  • Contributed to the development of a generative image platform, utilizing Python and SQL for backend tasks, while learning and applying React and Django to enhance the project’s technical stack.
  • Contributed to the development of a generative image platform, utilizing Python and SQL for backend tasks, while learning and applying React and Django to enhance the project’s technical stack.

Data Analyst Intern – Cybersecurity Insights

September 2022 - August 2023

Right Turn Security - India
  • Developed relational database schemas to store and manage cybersecurity incident data, enabling faster querying and analytics.
  • Analyzed patterns in historical cybersecurity breaches using Python (Pandas, Scikit-learn) to identify high-risk behaviors, contributing to the development of predictive security protocols.
  • Created interactive dashboards in Power BI, visualizing cybersecurity trends and presenting insights to stakeholders.
  • Collaborated with the team to define AI/ML standards for threat detection models, such as anomaly detection and classification of malicious activities.

My Portfolio

Here are some of my projects that I have done in the past.

Underwater Object Detection-YOLOv11

Project 1
  • Neural Networks
  • Tensorflow
  • Transfer Learning
  • Object Detection

This project focuses on developing a robust object detection system capable of identifying humans in underwater environments. Leveraging the YOLOv11 deep learning model, it addresses challenges such as low visibility, distortion, and debris often encountered in underwater rescue missions.

AirBnb Open Data Dashboard

Project 1
  • Power BI
  • Python
  • SQL
  • Data Viz

This project aims to provide a comprehensive overview of Airbnb listings in Winnipeg using interactive visualizations created with Microsoft Power BI. The dashboard allows users to explore the data and gain insights into different trends and patterns.

SQL Boxstore

Project 1
  • MySQL
  • DBeaver
  • ERD
  • DBMS

The BoxStore project involves handling a dataset from a box store. The dataset includes comprehensive details about the products. The goal of the project is to efficiently store, normalize, and manage the data in a MySQL database, create additional realistic data, and visualize the relationships using an ERD.

Online Student Portal

Project 1
  • Python
  • SQLite
  • File Handling
  • DBMS

This is a terminal-based online student portal project written in Python. The portal allows students to register, log in, and manage their details, documents, and course fees. The application uses SQLite to store data and includes a status tracking system to monitor the student's progress through the registration process.

Passenger Satisfaction Analysis

Project 1
  • Python
  • Data Cleaning
  • Scikit Learn
  • Pandas

The objective of the project is to analyze the data to determine the factors contributing to passenger satisfaction and to predict whether a passenger is satisfied based on these factors. This analysis includes data cleaning, exploratory data analysis (EDA), feature engineering, and building machine learning models.

My Contacts

I'd love to hear from you! Feel free to reach out via email or connect with me on LinkedIn or GitHub.