Divya Yalla

Motivated and detail-oriented data science student with hands-on experience in machine learning, big data, and analytics projects. Seeking an internship opportunity to apply technical skills and contribute to real-world business problems in a collaborative environment.

Wholesale Price Index Exploratory Data Analysis, 

- Performed in-depth exploratory data analysis (EDA) on Wholesale Price Index datasets using Python libraries to uncover economic trends and seasonal variation.

- Created interactive visualizations to highlight sector-wise inflation and pricing anomalies.

· Customer Segmentation with PySpark

- Leveraged PySpark MLlib to implement customer segmentation on large datasets, applying K-Means clustering to identify behavioral groups.
- Enhanced processing efficiency and enabled targeted marketing strategy through data-driven insights.

Fake Review Detection Using Supervised Machine Learning, 

- Developed an NLP pipeline using TF-IDF and classification algorithms (SVM, Logistic Regression) to detect fake reviews in e-commerce datasets.

 - Achieved 85%+ accuracy and improved model robustness with hyperparameter tuning.

Disaster Response Tracking and Scheduling System (DRTSS),

 - Designed a web-based system for real-time disaster tracking and rescue team scheduling using GPS and SMS integration.

- Automated resource allocation based on real-time updates, reducing manual coordination delays by 40%. 

Design of Low-Noise, Low-Power Instrumentation Amplifier for Biomedical Applications, 

– Developed an energy-efficient instrumentation amplifier optimized for biomedical systems, ensuring low noise and high signal integrity for accurate physiological signal monitoring.

– Focused on power optimization and noise reduction to enhance performance in wearable and implantable medical devices.

Research Paper – The Use of Big Data Analytics in Cardiovascular Disease Management.

- Co-authored a technical paper focusing on machine learning applications in predicting and managing cardiovascular diseases. Analyzed large healthcare datasets to highlight the role of big data in improving patient diagnosis and treatment outcomes.