Passionate about building data-driven solutions and intelligent systems. B.Tech CSE (Data Science) student at Anurag University, Hyderabad.
Passionate about building data-driven solutions and intelligent systems.
Hi, I'm Devarashetty Rishitha, a B.Tech CSE (Data Science) student at Anurag University, Hyderabad (2022–2026). I am passionate about building data-driven solutions and intelligent systems. With hands-on experience in machine learning, data analysis, full-stack development, and mobile app development, I enjoy transforming raw data into meaningful insights and scalable applications.
Pursuing a Bachelor of Technology in Computer Science and Engineering with specialization in Data Science. Coursework covers Machine Learning, Data Analytics, DBMS, Python, Java, and full-stack development. Actively involved in project exhibitions and hackathons.
Completed Intermediate education with Mathematics, Physics, and Chemistry (MPC) stream. Built a strong analytical and problem-solving foundation that paved the way for a career in technology and data science.
Completed schooling under the CBSE curriculum from Class I through Class X. Developed core academic skills in sciences and mathematics, and participated in various school-level activities and competitions.
Participated in Smart India Hackathon, one of India's largest national-level hackathons, with a women safety mobile application. Collaborated with a team to design and develop a real-world safety solution under competition conditions.
Participated in multiple college-level project exhibitions and poster presentations, showcasing technical projects to faculty, industry guests, and peers. Gained experience in communicating complex ideas clearly and confidently.
Interactive data visualization tool built using R and Shiny to analyze employee datasets. Visualizes key HR metrics like attrition, gender ratio, department performance, and salary distribution to support data-driven decision-making.
Machine learning framework built to predict medical test results and assist in clinical decision-making. Features data preprocessing using SMOTE, Exploratory Data Analysis, and evaluation of multiple algorithms including Random Forest and Gradient Boosting.
Hybrid ML framework for real-time DDoS attack detection in Software-Defined Networks. Utilizes SVM, Random Forest, and custom hybrid models (LR-KNN, GB-XGB) to analyze network traffic patterns, classify anomalies, and ensure robust network integrity.
Intelligent web-based career evaluation and job matching system. Integrates Google Gemini Pro APIs and FAISS-based semantic search to automatically analyze resumes against job descriptions, generate ATS scores, and provide personalized improvement recommendations.
Full-stack web application unifying the entire data analysis pipeline. Features workspace-based dataset management, data preprocessing, interactive visualizations, and an automated insight generation module that translates complex analytical results into human-readable reports.
Personal finance tracker mobile app built using React Native (Expo SDK 54) and Supabase. Features expense logging, category-wise analytics, and Google Sign-In authentication. Fully functional with new features being actively added.
Suryapet, Telangana, India
