I have a proven track record of successfully leveraging cutting-edge technologies, particularly BERT and BART, to analyze financial news URLs. My primary achievement involved developing a sophisticated system aimed at summarizing and analyzing sentiment in financial news articles based on user-provided URLs or text. This project showcased my deep interest and proficiency in natural language processing within the dynamic field of data science.In executing this project, I applied a comprehensive data science approach:1. Fine-tuned Large Language Model (LLM): - Enhanced large language models to grasp domain-specific nuances in financial news articles.2. Finbert for Sentiment Analysis: - Integrated a pre-trained model, Finbert, specifically designed for accurate financial sentiment analysis.3. Large-Bart CNN for Summarization: - Implemented a Large-Bart CNN model for extractive summarization, ensuring concise and informative summaries of financial articles.In overcoming challenges, I addressed issues such as imbalanced data through augmentation and re-sampling techniques. Additionally, I successfully mitigated overfitting using a combination of regularization and hyperparameter optimization.The tangible outcomes of this project speak volumes about its success. I achieved a commendable 75% accuracy, providing users with quick and insightful summaries of financial articles. This project not only demonstrates my technical expertise but also underscores my commitment to delivering impactful solutions that empower informed decision-making in the realm of financial analysis.

During mp 6-month internship at Rishabh Software, I gained practical experience in several key areas. I were trained in neural networks, machine learning (ML), natural language processing (NLP), SQL, and cloud technologies. Throughout my internship, I had the opportunity to apply these skills in real-world projects and gain hands-on experience in developing and deploying ML models, working with NLP techniques, utilizing SQL for data management, and leveraging cloud platforms for data analysis and deployment.

During my 5-month internship as an Artificial Neural Network Intern at Utkarshini Edutech, my primary focus was on research work. I engaged in various research projects related to artificial neural networks, exploring their applications, advancements, and potential improvements. I conducted literature reviews, analyzed existing neural network models. My research work contributed to the organization's knowledge base and helped in advancing the field of artificial neural networks.

During my 4-month internship as a Data Science Intern at Neubrain Solutions Pvt. Ltd., my primary responsibilities focused on data cleaning and data extraction. I worked on various projects where I were involved in preprocessing and cleaning datasets, ensuring data quality and accuracy. Additionally, I performed data extraction from different sources, gathering relevant information for analysis and modeling purposes. My contributions in these areas helped me to improve the overall data quality and facilitated effective data-driven decision-making within the organization.

I contributed to various projects and tasks. I worked on data preprocessing and cleaning, performed exploratory data analysis and implemented machine learning models, and evaluated their performance. Additionally, I have collaborated with the team to analyze and interpret the data, generate actionable insights.