During my internship, I had the opportunity to dive deep into various Microsoft and Azure technologies. My journey began with an introduction to Azure OpenAI, where I explored Azure AI Studio and Microsoft's responsible AI practices. I gained hands-on experience with Microsoft Copilot, including its integration with Microsoft Loop, MS Graph, and Power Platform, where I focused on Power Automate and its advanced features like process mining.I also explored Azure Functions, GitHub Copilot, and Viva, deepening my understanding of AI's role in enhancing productivity. A significant highlight was my involvement in a comprehensive Olympics data analytics project using Azure Data Factory, Azure Databricks, and Power BI.In addition to these, I worked on deploying Azure Virtual Desktops, setting up Active Directory Domain Services, and learning about Azure architecture, including HPC deployments. I also focused on Power Automate projects, such as email sentiment detection and integrating Adaptive Cards. My experience culminated in working with Azure Functions, deploying them using Visual Studio Code, and understanding Azure Machine Learning and Microsoft Defender for Endpoint.This internship allowed me to gain practical knowledge and experience in cloud computing, AI, and automation, which I'm excited to bring to future projects.

During my internship, I developed a real-time vehicle cut-in detection and collision warning system using YOLOv5 and PyTorch. The system processes live video feeds from a webcam, identifying vehicles and calculating their distances from the camera using the IDD Dataset. By determining the Time-to-Collision (TTC), the system provides proactive visual and audible alerts to prevent collisions.Key Contributions:Integrated YOLOv5 for real-time vehicle detection and PyTorch for deep learning.Computed distance and TTC for vehicle collision prediction.Designed an interactive UI using Streamlit to monitor live video feeds and alerts.Utilized OpenCV for efficient video processing and annotation.Delivered proactive collision alerts, enhancing transportation safety through deep learning.This project showcases the application of advanced AI techniques in improving road safety and vehicle dynamics.

During my internship as a Full Stack Developer at EdifyTechnoSolutions from May 2024 to June 2024, I underwent extensive training in a variety of web technologies and was involved in building a web application.The training focused on both front-end and back-end development. We learned the following:JavaScript: Gained hands-on experience in scripting and building dynamic, interactive features for web pages.HTML and CSS: Focused on creating the structure and styling of web pages. I worked extensively with Cascading Style Sheets (CSS) to design responsive layouts and ensure the site was aesthetically appealing across different devices.SQL: Developed skills in database management and querying, particularly with Oracle SQL Developer, where we performed CRUD (Create, Read, Update, Delete) operations on databases. This allowed for efficient data handling in the application.Project:As part of the internship, we applied these skills to a website development project. The project involved:Designing the front-end: Using HTML, CSS, and JavaScript, I helped create a responsive and interactive user interface, ensuring a seamless user experience.Back-end development: Leveraged SQL databases to manage user data and other site content. The back-end was built with a focus on secure and efficient data management, using Oracle SQL Developer.

During my internship as a Data Analytics Intern at Cognifyz Technologies from April 2024 to May 2024, I had the opportunity to work on several tasks related to data processing, analysis, and visualization. This was a remote internship where I gained hands-on experience with industry-standard tools and techniques for data analytics.Key Areas of Work:Data Preprocessing and Cleaning:One of the primary tasks was handling raw datasets, which often contained missing, inconsistent, or irrelevant information.I used Pandas, a powerful Python library, to manipulate and clean the data. This included handling null values, removing duplicates, formatting columns, and filtering out outliers to ensure the dataset was ready for further analysis.I also learned about different data preprocessing techniques such as data normalization and standardization.Data Analysis:After preprocessing, the next step was to perform exploratory data analysis (EDA). I worked on uncovering trends, patterns, and insights from the data.I used Pandas to run statistical analyses, create pivot tables, and generate summary reports that highlighted key metrics.Data Visualization:To effectively communicate the findings, I created various data visualizations. Using libraries like Matplotlib and Seaborn, I developed interactive and informative charts, graphs, and plots.The visualizations helped present the data insights in a way that could be easily understood by stakeholders and decision-makers.Projects and Outcomes:Throughout the internship, I was involved in various mini-projects that required working with real-world datasets. These projects helped me build strong skills in data cleaning, transformation, and visualization. By the end of the internship, I was proficient in using tools like Pandas and learned how to translate raw data into actionable insights.