I'm Akash Chauhan — I build machine-learning systems and backend automation tools that find structure in messy, real-world data. I care less about the buzzwords and more about the numbers a production system actually moves.
An interactive map of every tool, language, and technique I work with — and how they connect.
Applying engineering discipline and machine learning to build scalable, data-driven systems.
End-to-end builds across NLP, tabular regression, applied physics, and secure software — each judged by a number, not a vibe.
An end-to-end detector trained on 6,000+ real-world articles. Text cleaning, stopword removal, and TF-IDF feature extraction, comparing Logistic Regression, Naïve Bayes, and Random Forest — then shipped as a live Streamlit web app.
A full-featured password manager built from scratch in Python. Secure login, password hashing & encryption, strength validation, search, update, and clean modular architecture with JSON storage, logging, and exception handling.
A sales-forecasting model on real transactional data. Full cycle of cleaning, feature engineering, and EDA — comparing Linear Regression and Random Forest, with Random Forest winning on non-linear patterns. Surfaced seasonal trends and country-wise revenue distribution.
Predicting the stiffness of lattice structures from geometry — density, thickness, cell size. Built a synthetic dataset to model real material behaviour, trained Linear Regression and Random Forest, and used feature importance to identify the drivers of stiffness.
An AI-assisted system to help game designers discover board game mechanics. Web scraping, data collection, NLP with semantic similarity and vector representations to match game ideas to relevant mechanics.
Open to Python development, data-science, and machine-learning roles. The fastest way to reach me is email — I reply quickly.