Competitive Achievements

Hackathon Highlights

Participated in 15+ hackathons — fortunate to have secured a few wins, and even more grateful for the experience.

First Place - Data Analytics Challenge

IIITDM Kancheepuram, 2024

  • Conducted advanced traffic analysis on 1M+ ADAS and CAS data points, using spatio-temporal techniques to identify accident hotspots.
  • Created insightful dashboards and a detailed report incorporating real-time traffic data, enabling data-driven insights for urban mobility and road safety.
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First Place - Machine Learning Mania

Anna University, 2024

  • Built classification models achieving 94% accuracy through rigorous data cleaning and feature engineering.
  • Focused on extracting meaningful patterns and optimizing predictive performance on real-world datasets.
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Winners - Data-A-Thon

VIT, 2025

  • After hunting down clues across the university campus in the scorching heat to unlock the dataset, we developed a CNN model that achieved 91% accuracy on a complex image classification task.
  • The model was complemented with real-time result visualizations to interpret predictions effectively.
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7th Position @ Leaderboard DataSet '24

VIT, 2025

  • In this challenge with 150+ participants, we addressed a problem on predicting and managing user traffic surges in 5G networks.
  • We created a synthetic telecom dataset, built an LSTM-based prediction model, and integrated it into a web app featuring network data visualizations and a custom chatbot (Grok) trained on network-related queries.
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Finalist - CodeCycle

Anna University, 2024

  • A two-phase hackathon where the first round focused on cryptography, deep learning, and JavaScript.
  • Our team qualified for the finals, where we built ThunAi — a real-time women’s safety app with live location tracking, secure Firebase storage, and an SOS alert system.
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Finalist - DataQuest

VIT, 2024

  • Performed EDA and developed impactful visualizations on customer credit finance data, focusing on patterns linked to user behavior.
  • In the finals, we presented the classic customer churn prediction system, showcasing actionable insights and key financial indicators to support strategic decisions.
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