SAINATH PALAVALA

Logical and results-driven Web Developer dedicated to building and optimizing user-focused websites for customers with various business objectives. Judicious and creative when crafting effective websites, apps and platforms to propel competitive advantage and revenue growth. Technically proficient and analytical problem solver with calm and focused demeanor.

UT Arlington Website

• Technologies: React, Node.js, PHP, Laravel, MySQL, Azure
• Description: Led the development of a responsive website for UT Arlington, featuring React for front-end and a Node.js, PHP, and Laravel-based back-end. Implemented user authentication, interactive elements, and data management functions.
• Deployment: Utilized Azure VMs for backend hosting and Azure Blob for data storage.
• Role: Managed full-stack development, from UI/UX design to back-end architecture and database integration.

Fare Prediction System for Cab Services

• Technologies: Logistic Regression, Linear Regression, SVM, KNN, React.js, HTML, CSS, PHP
• Description: Engineered a predictive model integrating multiple algorithms to accurately forecast cab fares. Developed the front-end using React.js and integrated real-time data from Uber and Lyft APIs.

Image Segmentation with COCO Datasets

• Technologies: UNET, FCN, LSTM, CNN
• Description: Executed advanced image segmentation using a combination of UNET, FCN, and LSTM. Employed CNN for efficient image captioning, enhancing raw data preprocessing.

Rice Leaf Disease Detection

• Technologies: CNN (VGG16), Flask, Transfer Learning
• Description: Designed an AI model using CNN (VGG16) and transfer learning to detect diseases in rice leaves. Developed a Flask interface for seamless hardware integration.

Video-Based Dynamic Human Authentication System

• Technologies: IoT, Computer Vision, ResNet, FaceNet, YOLO
• Description: Innovated an IoT-based authentication system using cutting-edge Computer Vision technologies including ResNet and FaceNet, augmented with real-time YOLO detection.

Recommendation System

• Technologies: Spark, Hadoop, Apache Pig, AWS
• Description: Developed a sophisticated recommendation engine for IMDb and MovieLens datasets using neighborhood models. Managed large-scale data storage and processing with AWS, Spark, and Hadoop.

"Price Prediction Analysis for Uber and Lyft." Achieved 91% accuracy in predicting optimal pricing based on API data analysis.