Anusha Reddy Email & Phone Number

New York, New York, Us

Orlando, Florida, Us

BUSINESS CONTEXT: With increasing regulations and risk of sensitive data leakage, firms employ several stringent practices to ensure that the data is protected. However, these practices drive-up operational costs and lead in opportunity losses.PROJECT OBJECTIVES: Understand the trade-off between data privacy and predictive power of various synthetic data generation techniques. Determine the effectiveness of synthetic data in preserving privacy while maintaining reasonable predictive power by comparing the performance of models trained on real and synthetic data.PROCESS OVERVIEW: Synthetic data generation using techniques such as CTGAN, Gaussian Copula, and TVAE from the SDV library and Data Synthesizer library in Python. Finally, for imbalanced binary classification problems, we compare the similarity of generated artificial data to real data and evaluate model performance using ROC scores for the method with the best achieved outcome.PYTHON LIBRARIES: 1. Numpy and Pandas for data preprocessing and manipulation2. Scikit-learn for machine learning models and evaluation metrics3. SDV for synthetic data generation using CTGAN, Gaussian Copula, and TVAE4. Data Synthesizer for synthetic data generation5. Seaborn and Matplotlib for data visualizationMACHINE LEARNING MODELS: K-Means clustering, Logistic Regression, and Decision Trees

Kirkland, Wa, Us

BUSINESS CONTEXT: Introducing a new subscription package that adds few features to the security camera system with the goal of expaning this area of the business and increasing the average lifetime value of the clientele through the of sale of supplemental services, thereby increasing the total margin of the subscription service.PROJECT OBJECTIVES:1. Enhance customer subscription plan retention time2. Boost subscription service profits3. Determine Wyze's competitive edgeTOOLS AND TECHNOLOGIES:1. NLP libraries such as NLTK, SpaCy, and TextBlob, along with machine learning frameworks like Scikit-learn and TensorFlow for sentiment analysis2. Business Analysis for hypothesis testing2. Google Analytics for analysis of website traffic, behaviour, key performance metrics, and advertising3. Tableau for data visualization4. Python and SQL for data preprocessing and manipulation

SAP CPQ:1. Product Configuration: Configuring complex products and services based on product attributes, quantity, and customer type quickly and accurately to improve sales efficiency.2. Pricing and Discounting: Setting up flexible pricing and discounting options to help businesses respond to competitive pressures and customer demands.3. Quoting: Enabling sales teams to create and deliver accurate quotes quickly and easily.4. Workflow and Approvals: Designing the approval process pipeline by routing quotes through predefined workflows.5. Integration with CRM and ERP Systems: SAP CPQ integration with leading CRM and ERP systems to provide a complete solution for managing sales processes.SAP Analytics Cloud:1. In-memory computing: Generating real-time analytics and reporting on critical day-to-day pricing data2. Integration: Building open APIs to integrate with other systems and data sources3. Machine Learning Algorithms: Automating data modeling, predictive analytics to predict outcomes and identify patterns and trends in dataTOOLS AND TECHNOLOGIES:Python, SQL, HTML, CSS, JavaScript, CTX Tags, SAP Analytics Cloud Modeler, Lumira Discovery, Smart Insights, Predictive Scenarios, SAP HANA

Houston, Texas, Us

OBJECTIVE: Optimize the Configure Price Quote (CPQ) solution revisions, focusing on performance, end-user flexibility, automated triggers, and complex approval systems. The outcomes of the project resulted in a 34% boost in client's quarterly sales cycles.RESPONSIBILITIES:1. Optimized CPQ solution revisions with a focus on performance, end-user flexibility, automated triggers, and complex approval systems.2. Analyzed and manipulated historical data reports on Tableau and MySQL databases to determine quotation won/lost parameters and reveal crucial pricing propositions for product portfolio expansion.3. Collaborated on solution development, configuration, unit testing, and production management of the SAP Callidus CPQ system for launching two product lines through MVP1 and MVP2 phases.4. Revitalized underlying model components to optimize existing development processes, reducing quote processing time and yielding large volumes of quotes.TOOLS AND TECHNOLOGIES:1. SAP Callidus CPQ2. Tableau3. MySQL4. Python5. JavaScript6. CSS7. JSON8. RESTful API

Houston, Texas, Us

New York, Ny, Us

A) PROJECT OBJECTIVE: Eliminate third-party dependencies for authorization by building microservicesDESCRIPTION: 1. To achieve this goal, we built the microservices using the Spring Boot framework, which is built on top of the Java programming language. It handled the authentication and authorization of users, enabling secure access to the client data. 2. Spring Boot allowed us to create standalone, production-grade Spring-based applications quickly and easily. We implemented event-driven architecture using Apache Kafka, to enable communication between the microservices which allowed us to decouple the different components of the system, making it more flexible and scalable.B) PROJECT OBJECTIVE: Optimize sensitive client data processing using NLP techniquesDESCRIPTION: 1. The Rasa NLU extension was used to improve the accuracy of the natural language processing model, specifically in the area of client data processing. It invloved leveraging Python, which is a popular programming language for data analysis, machine learning, and natural language processing. 2. The fuzzywuzzy library, which is a Python library that uses Levenshtein distance to calculate the difference between two sequences allowed us to match user input to pre-defined templates with a higher degree of accuracy, reducing the number of false positives and improving the overall user experience.TOOLS AND TECHNOLOGIES:1. Spring Boot framework2. Apache Kafka3. Java4. Rasa NLU5. Fuzzywuzzy library6. Natural Language Processing7. Python8. Machine Learning

Master Of Science - Ms, Business Analytics And Information Management

Bachelor Of Engineering - Be, Information Technology

High School Diploma, Mathematics And Computer Science