Hello 👋 I'm
Peketi Yaswanth
4th-Year Integrated M.Tech CSE Student | Machine Learning Enthusiast
Motivated 4th-year Integrated M.Tech CSE student at VIT-AP University with a strong interest in Machine Learning and building real-world applications. Passionate about solving problems through technology and continuously improving technical and analytical skills. Also experienced in event management and leadership, with the ability to organize, coordinate, and deliver impactful results.
About Me
Motivated 4th-year Integrated M.Tech CSE student with strong foundations in Java, Python, HTML, and CSS, along with growing expertise in Machine Learning. Skilled at applying programming and ML concepts to problem-solving and practical projects. Proficient in event management and time analysis, with a proven ability to organize, coordinate, and deliver results effectively. An enthusiastic learner with a keen interest in business management, combining technical expertise with leadership and management skills to excel in academic and professional environments.
Skills
Python
Java
HTML5
CSS3
DSA
Machine Learning
Projects
Smart Agriculture System (IoT)
Technologies: Arduino UNO, ESP32, IoT Sensors, Android (Java), RS485, L298N
Developed an IoT-based smart farming system to monitor temperature, humidity, soil moisture, and nutrients. Built an Android app for real-time crop recommendations using CSV-based logic. Automated irrigation using RS485 communication and an L298N motor driver. Enabled remote monitoring, live data logging, and analysis for better farming decisions.
Food Donation Platform (Hackathon)
Technologies: HTML, CSS, JavaScript, Node.js, MySQL, Map APIs
Developed a web platform connecting donors, NGOs, and volunteers for food and money donations. Allowed users to submit food details, donate funds, and track distribution. Used map-based location features to find nearby donations quickly. Built during a 24-hour hackathon, demonstrating teamwork and fast problem-solving.
Diabetes Prediction System (Machine Learning)
Technologies: Python, Scikit-learn, Pandas, NumPy, Streamlit
Built a machine learning model to predict diabetes risk using health parameters. Applied preprocessing techniques such as handling missing values and feature scaling. Trained models to produce prediction results and developed a Streamlit app for real-time input and user interaction.