Abhiram Chavali

Data Analytics enthusiast with a strong foundation in Python, SQL, and Power BI, and practical experience in building AI/ML models, developing responsive front-end interfaces, and teaching core programming concepts. Skilled in working with text, audio, and image-based projects, as well as creating intuitive web pages using modern frontend technologies. Experienced in explaining complex topics such as C programming and Data Structures, reflecting strong communication and problem-solving abilities. Passionate about using data, technology, and analytical thinking to create meaningful solutions and support informed decision-making.

• Overview Of Advances in Communication Technologies
• NXT Wave
• TCS-ION Job Skills Training
• Sensors for Industry 4.0

• Amazon Sales Report:

Conducted comprehensive data analysis of Amazon sales reports to identify key sales trends, customer behavior patterns, and regional performance metrics. Delivered actionable insights and strategic recommendations to enhance revenue growth, optimize service efficiency, and support data-driven decision-making.

• Crowd-Aware Public Transport ETA System:

Developed a smart web-based system to enhance public transportation by integrating real-time Estimated Time of Arrival (ETA) data with dynamic crowd density insights. Implemented features like a crowd-reporting interface, predictive analytics, and heatmap visualizations using Python, Firebase, HTML, CSS, and JavaScript. The solution improves commuter decision-making, reduces congestion, and supports transport authorities with data-driven planning, aligning with smart city and sustainable mobility initiatives

• Text Summarization Tool – Jupyter Notebook | GitHub:

Developed a Python-based tool using Natural Language Processing techniques to generate concise summaries from lengthy articles automatically. Implemented using libraries like NLTK and Sumy, the project takes user input text and returns key highlights. Demonstrated in Jupyter Notebook with examples and hosted on GitHub.

• Offline Emergency Mesh Communication App:

Developed a peer-to-peer emergency communication app capable of functioning without internet or cellular networks. The system forms a dynamic mesh network using Bluetooth, allowing messages to hop across multiple nearby devices until they reach the intended recipient. Implemented decentralized routing, automatic node discovery, and message forwarding to ensure reliable delivery even in disaster zones or network outages. Designed with lightweight protocols for low latency and battery efficiency, enabling real-time alerts, location sharing, and broadcast messaging in fully offline environments.