Headline
AI/ML Engineer | Turning Data & AI into Clear Insights, Smarter Decisions & Business Growth
About
I am Pyaram Preethi, a passionate and motivated B.E. student specializing in Artificial Intelligence and Machine Learning at Acharya Institute of Technology, Bengaluru. I have a strong interest in Artificial Intelligence, Machine Learning, Data Analytics, and Software Development. Through my academic journey and projects, I have developed a solid foundation in programming, problem-solving, data handling, and application development. I am proficient in Python, Java, SQL, JavaScript, React.js, MySQL, MongoDB, and data visualization tools such as Tableau and Power BI. I have also gained practical exposure to Machine Learning and Deep Learning through academic projects and an internship, where I worked on projects involving regression, clustering, image classification, and neural networks. I enjoy learning new technologies and applying my knowledge to solve real-world problems. I am currently strengthening my Data Structures and Algorithms, SQL, Machine Learning, and software development skills to prepare for a successful career in the technology industry. I consider myself a continuous learner who values teamwork, communication, creativity, and problem-solving. My goal is to build impactful technology solutions, grow as a skilled technology professional, and contribute meaningfully to innovative projects and organizations.
Experience
Education
Projects
datamindai
DataMindAI is an AI-powered data analytics platform designed to simplify the process of analyzing datasets and generating meaningful insights. The platform allows users to upload datasets, perform automated data analysis, visualize patterns and trends, generate AI-based insights, and create analytical reports through an interactive web dashboard.
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IPL Win Prediction & Performance Analytics System
An interactive ML-powered IPL analytics platform that combines historical data analysis, win prediction, visualizations, and Power BI dashboards in a web-based Streamlit application.
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Comorbidity Analysis Of Epilepsy and Depression
Developing an EEG-based AI/ML system to analyze and identify patterns associated with epilepsy and depression, with a focus on their comorbidity. The project uses EEG datasets from Bonn and MODMA, applying signal preprocessing, segmentation, feature extraction, and machine learning/deep learning techniques. The aim is to develop a reliable model that can distinguish neurological and mental health conditions from EEG signals and support data-driven analysis for early detection and clinical research.
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Portfolio
Pyaram Preethi
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