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Krishi AI is an AI-driven solution developed to support farmers in making informed agricultural decisions. The system uses machine learning and computer vision techniques to analyze crop images and identify potential diseases or stress conditions in plants. By detecting issues early, it helps farmers reduce crop losses and improve productivity. Along with disease detection, Krishi AI provides actionable suggestions, such as recommending treatments, fertilizers, or irrigation practices, making farming more efficient and sustainable. This project aims to bridge the gap between advanced AI technologies and traditional farming practices, giving farmers access to intelligent tools for crop management. Though still at a prototype stage, Krishi AI demonstrates how technology can be applied to solve real-world challenges in agriculture and ensure better yields, reduced costs, and improved food security for the future.

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Jashn-e-hub Planner is designed to transform how people organize events. From weddings and birthdays to corporate meetings, event planning involves many tasks such as booking venues, arranging catering, managing guests, and sticking to a budget. Jashn-e-hub makes this process easy with an intelligent platform that centralizes everything. Users can create customized event plans, allocate and track budgets, and even get AI-driven vendor recommendations tailored to their needs. By providing these features, the app saves time, reduces stress, and ensures smoother event execution. This project demonstrates how AI and smart tools can modernize traditional planning processes, making event management more efficient, accessible, and enjoyable.

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Car Damage Detection was my first deep learning project where I explored the use of convolutional neural networks (CNNs) for real-world applications. The system analyzes car images and predicts if visible damage exists, highlighting areas of concern. While it’s a basic implementation, it reflects how AI could help automate insurance claim processes, saving time for both insurers and customers. The project taught me valuable lessons in model training, image preprocessing, and data handling. Although it is far from production-ready, it marks the starting point of my deep learning journey and inspired me to build more advanced applications in computer vision.

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CrowdMind AI is one of my favorite projects where I combined computer vision with creative design. The working version counts the number of people in crowd images using AI, while the conceptual extension envisions a system capable of real-time crowd monitoring, density mapping, and safety alerts. I even created a PPT mockup showing how such a system could look in practice. While incomplete, CrowdMind AI shows my ability to combine technical skills with innovative thinking, pushing ideas beyond prototypes into real-world problem solving. It reflects both my AI knowledge and creative vision for applying technology in impactful ways.

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The Resume Extractor project applies natural language processing (NLP) techniques to simplify the hiring process. It takes resumes as input (PDF or text) and extracts structured data such as candidate name, skills, education background, and work experience. Recruiters can quickly scan this output instead of reading through each document manually, saving significant time in large applicant pools. This tool demonstrates how AI can support HR teams by reducing repetitive tasks and enabling more data-driven hiring. For me, building Resume Extractor was a hands-on experience in NLP, text preprocessing, and entity extraction, allowing me to apply theory in a real-world use case.

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CrimeScope AI is an experimental project that explores how AI can support public safety through predictive analytics. It analyzes historical crime datasets to identify patterns related to time, location, and type of incidents. By recognizing these patterns, the system suggests potential hotspots and provides valuable insights for better law enforcement planning. The project includes visualization dashboards to show trends clearly. While it’s not a production-ready policing tool, it highlights how AI and data science can help address real-world challenges such as resource allocation, crime prevention, and policy making. Building CrimeScope AI expanded my skills in data analysis, machine learning, and visualization while addressing societal impact problems.

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