Monica Chaganti

Tree Mortality

• Utilized GIS to analyze spatial patterns and environmental factors by implementing machine-learning techniques including decision trees and neural networks to predict tree mortality and capture significant variance.
• Integrated GIS data layers for enhanced feature extraction, achieving a 25% reduction in unnecessary tree removal.

Malware Detection

• Implemented ML algorithms and feature selection models to focus on the most relevant malware indicators, enhancing detection accuracy.
• Engineered and implemented advanced ML algorithms for dynamic behavioral analysis, detecting anomalies in system behaviors achieved 40% reduction in false positives, enhancing operational efficiency and reducing downtown.

Mini-Project: Sea Level Rise Visualization

• Utilized Python libraries (Geopandas, Folium) to analyze and visualize NOAA-based sea level rise projections.
• Created an interactive map highlighting at-risk coastal areas due to projected sea level rise.
• Processed and visualized geospatial datasets to support data-driven coastal resilience strategies.

Mini-Project: Climate Data Analysis

• Conducted climate data analysis using Python to identify trends in temperature and precipitation from historical datasets.
• Visualized temperature and precipitation patterns over time with Matplotlib and Seaborn, providing insights into climate change trends.
• Applied statistical methods to summarize key metrics, enhancing data-driven decision-making.

Facial Recognition with Raspberry Pi

• Developed and implemented deep learning models for real-time face recognition using Convolutional Neural Networks (CNNs).
• Utilized Haar cascade classifiers and custom-trained models to achieve 90% facial feature detection accuracy and demonstrated expertise in Python, TensorFlow, and OpenCV.