Kamran Akmal

Conducted advanced time series forecasting on regional solar power generation across India using LSTM and deep learning models, achieving high predictive accuracy.

Led the end-to-end data science pipeline, including collection, preprocessing of large meteorological datasets, and fine-tuning models with TensorFlow.

Identified key seasonal and geographical patterns in solar power generation data, contributing to enhanced predictive insights.

Contributed to a research paper under Professor review, demonstrating expertise in data science research and publication.

Built an automation tool for manual Level Sheet Processing, streamlining operations and improving efficiency by 10 times.

Integrated AI-driven features into internal workflows, enabling faster decision-making and reducing processing times.

Enhanced data management processes, leading to significant improvements in operational throughput and accuracy.

Built a Selenium-based automation system, efficiently handling tasks equivalent to four interns' combined workload, significantly boosting operational efficiency.

Streamlined professional data collection and cleaning processes, ensuring seamless server integration with Python libraries like NumPy and Pandas.

Automated critical data management workflows, reducing manual effort and improving data accuracy for internal operations.

Developed three distinct, high-performance websites: a professional portfolio, an event management system, and a real-time stock market dashboard.

Leveraged modern web technologies including React.js, Node.js, Express.js, and MongoDB to ensure efficient functionality and robust performance.

Designed and implemented intuitive user interfaces and seamless backend integrations, enhancing user experience and data accessibility.