Karuna Vallabhapurapu

Applied clustering analysis to unstructured data (extraction, transportation, integration) by writing a machine learning algorithm that enhanced resource allocation for sales teams by reducing bias and using more correct data, increasing sales by 20%. Developed a new ETL pipeline using a python script that utilized data from the SQL Server, Salesforce Marketing Cloud Environment to reduce the runtime of the company's core BI model by more than 50% and process more than 1TB of data. Reinforced a significant improving conversion rate by 12.5% through A/B testing and multivariate analysis to measure the impact of features on crucial business metrics, including average profit and average profit, with the help of a team of 3 Data Analysis. Implemented an NLP model to classify text data into different topics to suggest the following action when dealing with a customer, which led to a 7% reduction in customer support time, as measured by a metric of average response time for each topic. Developed and fine-tuned machine learning and deep learning models to achieve optimal accuracy in structured and unstructured data. Demonstrated proficiency in supervised and unsupervised algorithms, applying them to diverse problem domains. Managed the complete machine learning life cycle, from data collection and preprocessing to model deployment and monitoring. Successfully addressed regression and classification challenges to meet specific business needs. Effectively handled unbalanced datasets, selecting and implementing appropriate models. Utilized a deep understanding of Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN) to tackle complex tasks. Managed and processed data in various formats, including Excel, CSV, and TSV files. Implemented RNN-LSTM models for tasks such as next-word prediction and text summarization in Generative AI. Measured document similarity using Cosine similarity for various applications. Conducted time se