With a strong foundation in Data Science, I am particularly passionate about projects that leverage my expertise in Machine Learning, Neural Networks, Large Language Models, Deep Learning, Generative AI, and Natural Language Processing. My proficiency in Data Analysis and Computer Vision enables me to address intricate problems effectively. I am eager to contribute to innovative projects that push the boundaries of technology and drive impactful results.
0 + Projects completed
During my internship, I leveraged predictive modeling techniques to optimize operational parameters of pharmaceutical machinery such as FBD, HSMG, and GCM. Additionally, I developed "Document Assist," an AI chatbot for document interactions in English audio, and spearheaded "DMAR," a real-time Min/Max CPP report system for machinery and batch comparisons. These initiatives aimed to enhance user accessibility and facilitate data-driven decision-making in pharmaceutical manufacturing processes. By analyzing historical data and employing machine learning algorithms, the goal was to predict optimal settings for factors like temperature, pressure, and speed, ultimately improving production efficiency, reducing batch time, and maintaining stringent quality standards.
During my time at TTI as a Quality Engineer, I learned a lot about checking software by hand and understanding the important steps for making sure it works well. I got to practice a lot by actually testing software myself and following clear steps to make sure it was good enough for use. It was all about making sure the software worked right and didn't have any problems when people used it.
Grade: 9.01 CGPA
Grade: 9.32 CGPA
Below are the sample Machine Learning, Natural Language Processing, Large Language Models, Data Analytics projects which I have done during my post graduage study along with internship.
Engaged in optimizing pharmaceutical machinery parameters, including fluidized bed dryers, high shear mixer granulators, and Glatt coating machines. Utilized data visualization and various optimization techniques to ensure consistent batch times by identifying and implementing optimal parameter values.
Developing a project enabling users to upload research papers for automated summarization into audio formats in multiple languages, including English, Gujarati, and Hindi. Leveraging pretrained deep learning models to facilitate this process, ensuring efficient summarization and multilingual Text-to-Audio translation.
The study explores using machine learning to detect fake news and clickbait on Twitter, created and used clickbait as input parameter to get better accuracy, showcasing LSTM's strong performance and a hybrid model's creation for improved accuracy.
We are designing and implementing a pioneering chatbot using large language models for a pharmaceutical firm. This chatbot will enable document upload interactions and deliver responses in English audio, significantly improving user accessibility and experience.
Successfully developed a palm classification project distinguishing between male and female palms using various computer vision models. Achieved peak accuracy employing Resnet50, showcasing proficiency in model selection and implementation for enhanced classification outcomes.
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Gandhinagar, Gujarat, India