Bhargav Ramasahayam

Highly motivated and detailed-oriented candidate passionate about using data to improve business performance and customer experience. Skilled at leveraging data to develop actionable solutions to business challenges and utilizing data mining and data visualization to create meaningful insights. Excellent technical aptitude and knowledge of programming languages, data analytics and data visualization.

• AWS Solutions Architect Associate
• Introduction to Data Science
• Data Analysis with python

Keras Text Summarization | Python, Keras, TensorFlow, Recurrent Neural Networks (RNNs)

Oct 2023 – Nov 2023

●  Engineered and deployed an end-to-end text summarization system leveraging encoder-decoder recurrent neural networks and seq2seq models in Keras; achieved a 40% reduction in time.

●  Synthesized complex data into clear and concise insights for stakeholders.

●  Presented findings and insights to senior management and stakeholders in clear and compelling visualizations.

●  Implemented attention mechanisms to improve the model's ability to focus on relevant parts of the input text, leading to more accurate and coherent summaries.

Fake News Detection using Logistic Regression | Python, Pandas, NumPy, scikit-learn, seaborn, NLTK

June 2023-July2023

●  Built a simple Machine Learning Model using Logistic Regression to detect whether a news article is fake or not.

●  Created data models to represent complex relationships between various data entities, optimizing data storage and retrieval.

●  Examined large datasets using statistical methods to derive actionable insights and identify trends.

●  Used Matplotlib to visualize the graphs that are generated to distinguish between fake news and true news.

Real-Time Social Distance Monitoring System using Computer Vision | Python, OpenCV, PyTorch, YOLO

Mar 2023-April 2023

●  Developed an automated system to detect and alert violations of social distancing norms in public spaces,  leveraging computer vision and deep learning techniques.

●  Visualized data using Tableau to create interactive dashboards and reports.

●  Validated data accuracy and completeness through rigorous testing methodologies.

●  Implemented object detection models (YOLO, Faster R-CNN) to accurately locate and track individuals within video footage or live camera feeds.

Traffic sign recognition

Nov 2022-Dec 2022

●  Developed and trained a convolutional neural network (CNN) model achieving an accuracy of over 95% in detecting and classifying traffic signs from real-time video feeds.

●  Managed data quality by implementing rigorous validation processes and error-checking routines.

●  Leveraged Excel to perform statistical analysis and create insightful reports.

●  Implemented advanced image processing algorithms for preprocessing and enhancing traffic sign images, contributing to the robustness of the recognition system.

●  Integrated the developed system with existing autonomous vehicle platforms, enhancing their capabilities for accurate real-time traffic sign interpretation and decision-making.

●  Identified areas for improvement through thorough data analysis and interpretation.

●  Designed and maintained data pipelines for seamless data extraction, transformation, and loading (ETL).

●  Leveraged TensorFlow and OpenCV frameworks to streamline the model development and deployment process, ensuring efficiency and scalability in the project's implementation.