Winner 🏅 2025 Global Price Prediction Challenge | Quant | An IIT Madras Scholar | FinTech | AI-ML Developer | Love Cooking
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Assessing your cultural and operational fit
zen-tui
April 25, 2026 – Present
Zen-TUI is a lightweight terminal sanctuary designed to combat burnout. While your code compiles or your tests run, step into a world of lo-fi vibes. No metrics, no deadlines—just you and the sand. 🌸
View ProjectSentientAspect-AICinema-Engine
April 17, 2026 – Present
A high-fidelity, aspect-based sentiment analysis engine that decomposes cinematic reviews into granular emotional insights. Leveraging transformer-based LLMs to distinguish between acting, cinematography, and narrative direction.
View Projectmushroom-edibility-classifier
April 3, 2026 – Present
A comprehensive classification project developed for an IITM Kaggle competition. Features extensive Exploratory Data Analysis (EDA) and an automated preprocessing, multi-model pipeline utilizing ensemble learners for high-accuracy predictions.
View Projecthotel-churn-model
April 2, 2026 – Present
An end-to-end Machine Learning pipeline predictive model developed for an IIT Madras Kaggle competition to forecast hotel booking cancellations using different Machine Learning Models. Includes EDA, feature engineering, and model evaluation.
View ProjectBookHub
May 4, 2025 – May 10, 2025
A lightweight book-tracking and recommendation system where users can log books they’ve read, rate them, and get personalized recommendations.
View Projectsmart-portfolio-analytics
April 27, 2025 – May 2, 2025
Optimize, Backtest, and Visualize Your Portfolio
View ProjectDeepEchoAI-
January 30, 2025 – January 30, 2025
DeepEchoAI is an AI-powered recommendation system that delivers personalized suggestions for music, podcasts, and audiobooks based on user preferences, listening habits, and content features.
View ProjectImageClassification-using-CNN-and-MLP-on-MNIST-and-FashionMNIST-Datasets
September 26, 2024 – September 28, 2024
This project showcases the implementation and comparison of Convolutional Neural Networks (CNN) and Multi-Layer Perceptrons (MLP) for image classification on the MNIST and Fashion MNIST datasets. It includes experiments with hyperparameter tuning, model evaluation through accuracy/loss plots, and confusion matrices.
View ProjectCultural Fit Analysis
The candidate's project portfolio indicates a strong interest in personal projects and self-driven learning, which could align with a culture that values initiative and continuous improvement. However, the lack of team-based projects or professional experience makes it difficult to fully assess cultural fit in a collaborative environment.
Soft Skills & Operational Fit
Insufficient data to assess soft skills and operational fit. The candidate's project descriptions suggest an ability to work on complex problems independently.