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Schedule Interview NowAt Softaims, I have been fortunate to work in an environment that values creativity, precision, and long-term thinking. Each project presents a unique opportunity to transform abstract ideas into meaningful digital experiences that create real impact. I approach every challenge with curiosity and commitment, ensuring that every solution I design aligns not just with technical requirements, but also with human needs and business objectives. One of the most rewarding aspects of my journey here has been learning how to bridge the gap between innovation and practicality. I believe technology should simplify complexity, enhance efficiency, and empower people to do more with less friction. Whether building internal systems, optimizing workflows, or helping bring client visions to life, my focus remains on developing solutions that stand the test of time. Softaims has encouraged me to grow beyond coding—to think about design, communication, and sustainability in technology. I see every project as part of a larger ecosystem, where small details contribute to long-lasting results. My daily motivation comes from collaborating with people who share the same passion for doing meaningful work, and from seeing the tangible difference our efforts make for clients around the world. More than anything, I value the culture of learning and improvement that defines Softaims. It’s a place where ideas evolve through teamwork and constructive feedback. My goal is to continue refining my craft, exploring new approaches, and contributing to solutions that are not only efficient but also elegant in their simplicity.
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The Movie Recommender System is a Python project that offers personalized movie suggestions using collaborative and content-based filtering. It analyzes user ratings and movie attributes, like genre and director, to generate recommendations. The system combines both methods for improved accuracy and features a user-friendly interface. Available on GitHub, this project provides an open-source solution for developing movie recommendation systems.
Property Scout is a Python tool that analyzes real estate data for investors, realtors, and homebuyers. It scrapes property listings from real estate websites, cleans and preprocesses the data, and provides insights on price trends, location, and investment potential. The tool includes visualization features to display market trends and property evaluations. It's open-source and available on GitHub for customization and further development.
This project uses Convolutional Neural Networks (CNNs) to detect and classify emotions from facial images. The model, trained on a diverse dataset, accurately identifies emotional states based on facial features. Key features include high accuracy in emotion detection, robustness across expressions and demographics, and applications in sentiment analysis and interactive systems. The code and documentation are available on GitHub, enabling replication and further research. This project showcases the power of CNNs in understanding human emotions.
I scraped the data of holistic doctors from the public library like yelp and Yellowpages.
I wrote a code to scrape hitta.se and also build a pipeline to store the data in PostgreSQL.
Bachelor of Science (BS) in Chemistry
2015-01-01-2018-01-01