A recent graduate with a bachelor's degree in technology in computer science and engineering who is enthusiastic and committed. My enthusiasm for data science has grown significantly. My education has given me proficienc…
A recent graduate with a bachelor's degree in technology in computer science and engineering who is enthusiastic and committed. My enthusiasm for data science has grown significantly. My education has given me proficiency in Python, SQL, PowerBI, LLMs, NLP and machine learning. I'm great at doing classification jobs and evaluating past data to get accurate predictions. I'm also always looking for new ways to leverage data to generate insights.
Leveraged Python for the analysis of extensive datasets and managed databases with SQL, achieving a reduction in operational time while maintaining data integrity and availability.
Preprocessed and analyzed large datasets for the training and validation of AI models, ensuring data quality and optimizing feature engineering to enhance model performance.
Developed and implemented machine learning models to streamline business processes, resulting in a 20% increase in efficiency.
Executed similarity searches on document embeddings to retrieve pertinent information.
Conducted in-depth research on cutting-edge generative AI techniques, experimenting with various algorithms and frameworks to stay updated with the latest advancements in the field.
In a group data science project, the project goal was to minimize the bounce rate to the pharmacy company.
Collected research papers related to the problem.
Cleaned and preprocessed the dataset to address the business problem.
Formed clusters and then performed forecasting with SARIMAX.
Built a model using scikit-learn libraries to evaluate the accuracy with various metrics.
Deployed the model.
Achieved good model accuracy.
In a group data science project, oversaw the creation of a chatbot that used the Hugging Face library's Tapex model to improve the effectiveness of inventory management.
The project's goal was to lessen reliance on the IT team by addressing the issue of time-consuming data access and retrieval.
Refined the Tapex-based chatbot to expedite data retrieval procedures and offer real-time insights into inventory levels and status.
Used sophisticated natural language processing techniques to provide an intuitive user interface that facilitates easy communication and effective inventory control.
Used Streamlit to deliver the Tapex chatbot, which provides users with an easy-to-use interface for quick and accurate inventory information access.
Achieved good model accuracy.
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