AWS-Powered MLOps Workflow
This project encapsulates the creation of an MLOps pipeline leveraging AWS services, streamlining the path from model development to production with automated CI/CD, testing, and scalable deployment.
This project encapsulates the creation of an MLOps pipeline leveraging AWS services, streamlining the path from model development to production with automated CI/CD, testing, and scalable deployment.
This project enhances DataCo Global’s ETL data pipeline, employing Mage AI for orchestration, Google Cloud Storage and BigQuery for data handling, and Looker Studio for insights visualization.
This project aims to optimize bike distribution for CitiBike using a STGCN model. It involves cleaning and analyzing ride data, identifying key patterns through network analysis, and predicting bike traffic at various stations.
This project leverages network analysis and machine learning to analyze over half a million Amazon product metadata, focusing on understanding product relationships, predicting sales ranks, and provide product recommendations.
This project utilizes Microsoft DeBERTa and data preprocessing techniques to classify tweets as related to real disasters or not, exploring the impact of model choice and data cleaning on prediction accuracy.
Walmart Sales Forecasting project involving a comprehensive analysis of Walmart’s retail data, utilizing predictive models like SARIMA, Ridge Regression, Random Forest, XGBoost, and LSTM to forecast weekly sales.
This project explores the enhancement of flight fare and delay predictions by merging conventional datasets with Twitter data, employing data processing and sentiment analysis to assess social media’s influence on aviation trends.
This project applies K-means clustering to segment customers based on various features related to their purchasing behavior, and the analysis was focused on understanding the distinct customer groups to tailor marketing and sales strategies effectively.
This study leverages advanced graph convolutional networks to accurately predict bike demand, incorporating complex spatial and temporal dynamics to enhance the efficiency and reliability of urban bike-sharing systems.
Developed a Deep Reinforcement Learning model for trading strategies using Python, TensorFlow, and Keras. Integrated action augmentation, custom Q-networks, and experience replay to optimize trading decisions and capture temporal dependencies.
In this project, I combined Deep Reinforcement Learning (DRL) and Graph Neural Networks (GNN) to solve routing optimization problems in Optical Transport Networks (OTN).
This project employs a hybrid MCDM approach integrating DEMATEL and ANP (DANP) to analyze and prioritize supply chain risks in the Indian Electronics Industry, offering a comprehensive view of risk interrelationships and their impact on supply chain dynamics.
This project applied Six Sigma DMAIC to enhance the to optimize the assembly process in an automotive company, resulting in a 40% reduction in defects and a significant increase in process capability.
Application of ARENA simulation software in process flow improvement, achieving enhanced productivity and resource efficiency.
This project examines Xerox Corporation’s use of benchmarking to optimize its supply chain, highlighting improved efficiency and customer satisfaction.
This project encapsulated the adoption of a new module within an existing ERP system, integrating fault tree analysis to systematically address and mitigate critical risks associated with communication and business process reengineering.
Statistical tests to be used for A/B testing
Understanding N-Gram Models: Perplexity and Smoothing Techniques in Natural Language Processing
Published in Journal 1, 2009
This paper is about the number 1. The number 2 is left for future work.
Recommended citation: Your Name, You. (2009). "Paper Title Number 1." Journal 1. 1(1). http://academicpages.github.io/files/paper1.pdf
Published in Journal 1, 2010
This paper is about the number 2. The number 3 is left for future work.
Recommended citation: Your Name, You. (2010). "Paper Title Number 2." Journal 1. 1(2). http://academicpages.github.io/files/paper2.pdf
Published in Journal 1, 2015
This paper is about the number 3. The number 4 is left for future work.
Recommended citation: Your Name, You. (2015). "Paper Title Number 3." Journal 1. 1(3). http://academicpages.github.io/files/paper3.pdf
Published in GitHub Journal of Bugs, 2024
This paper is about fixing template issue #693.
Recommended citation: Your Name, You. (2024). "Paper Title Number 3." GitHub Journal of Bugs. 1(3). http://academicpages.github.io/files/paper3.pdf
This is a description of your talk, which is a markdown files that can be all markdown-ified like any other post. Yay markdown!
This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.