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Naram Charan

◢SPECIALIZING INRecommendation Systems

“I teach machines to predict things.”

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J.A.R.V.I.S // MARK XLII
LET'S DIVE INSCROLL TO INITIALIZE
01SYSTEM MODULE

Production-grade ML systems — each scanned, validated, and leakage-checked before deployment. Tap any card or FRIDAY for the full brief.

MK-05MEDICAL IMAGING · DEPLOYEDFEATUREDAUG 1 – SEP 4, 2026

RSNA Pneumonia Detection

83% recall

Pneumonia screening on chest radiographs — transfer learning across three CNNs on the RSNA challenge dataset, then shipped: DICOM pipeline, FastAPI service, React interface, one Docker container live on Azure.

KEY RESULTS

  • 83% pneumonia recall · 0.79 F1 on 1,836 held-out studies
  • Found RandomCrop discarding 95% of every 1024² X-ray — the single biggest fix
  • Live on Azure: one container serves API + UI at $5.06/month
PyTorchpydicomFastAPIReactDockerAzure
LIVE DEMO ↗VIEW CODE →
MK-04TIME-SERIES FORECASTINGFEATURED

Walmart Store Weekly Sales Forecasting

95.55% R²

Global multi-series forecasting across ~3,000 Walmart store-department pairs — one model learns them all, driven by a custom recursive walk-forward engine that feeds each week's predictions back in as the next week's lag features.

KEY RESULTS

  • 95.55% validation R² — LightGBM beat XGBoost & Random Forest
  • Recursive walk-forward engine built from scratch, no forecasting libs
  • Caught & fixed temporal leakage masquerading as R² ≈ 0.98
PythonLightGBMXGBoostScikit-LearnPandas
VIEW CODE →
MK-03CLASSIFICATION

E-Commerce Customer Churn Prediction

98.28% acc

Four-model churn system — Logistic Regression, Random Forest, XGBoost and a 4-hidden-layer PyTorch network. The deep net tops the leaderboard; XGBoost ships anyway, because a half-point of accuracy doesn't buy a GPU bill.

KEY RESULTS

  • 98.28% accuracy · 94.74% F1 on the held-out test set
  • PyTorch net hit 98.76% — rejected: marginal gain, heavy compute
  • Optuna over 10-fold Stratified CV, scored on recall
XGBoostPyTorchScikit-LearnOptuna
VIEW CODE →
MK-02RECOMMENDER SYSTEMS

Neural Collaborative Filtering Architecture

<10ms retrieval

Custom recommendation engine training 32-dimensional latent embedding vectors on sparse user-item interaction data with L2 regularization, built from scratch in PyTorch.

KEY RESULTS

  • Sub-10ms FAISS similarity search at inference
  • 32-dim latent embeddings with L2 regularization
  • Meta's FAISS index for real-time retrieval
PyTorchFAISS
VIEW CODE →
MK-01DATA ENGINEERING

Book Data Scraping & Database Pipeline

980+ in <30 min

Automated multi-page scraper using BeautifulSoup + SQLAlchemy ORM, extracting structured data (title, price, rating, availability, description, URL) into a relational schema.

KEY RESULTS

  • 980+ books scraped in under 30 minutes
  • BeautifulSoup + SQLAlchemy ORM pipeline
  • Relational SQLite schema + clean CSV exports
BeautifulSoupSQLAlchemySQLitePandas
VIEW CODE →
02SYSTEM MODULE

Core subsystems and their capabilities — the stack that powers every build. (Go ahead — poke the grid, grab a reticle.)

◢ NEURAL

Deep Learning & GenAI

  • PyTorch
  • Neural Collaborative Filtering
  • Vector Embeddings
  • FAISS Similarity Search
  • Prompt Engineering

MK-03 — neural collaborative filtering built from scratch, 32-dim embeddings, FAISS L2 retrieval under 10ms.

◢ MODELS

Machine Learning

  • XGBoost
  • LightGBM
  • Random Forest
  • Logistic Regression
  • Scikit-Learn
  • Optuna

MK-05 & MK-04 — LightGBM at 95.55% R² across ~3,000 series; XGBoost at 98.28% accuracy, tuned with Optuna.

◢ SIGNALS

Data Intelligence

  • Pandas
  • NumPy
  • Feature Engineering
  • KNNImputer
  • Imbalance Handling
  • RobustScaler

MK-05 — recursive walk-forward feature engine; diagnosed and killed the leakage inflating R² to 0.98.

◢ CORE

Engineering Core

  • Python 3 · Advanced OOP
  • Data Structures
  • FastAPI · REST APIs
  • SQL · Schema Design
  • SQLAlchemy
  • Docker · Azure
  • Claude Code · Agentic Dev
  • Git Workflow
  • BeautifulSoup

MK-05 — FastAPI serving a model and a React bundle from one container on Azure. This site itself — Next.js + WebGL, built by directing Claude Code.

03SYSTEM MODULE

It started with a man in a suit of armor — the coolest hero I'd ever seen. But my interest went past the armor, to the AI he talked to. Because you know what was even cooler than the suit? His AI.

I'm a 3rd-year CS student specializing in AI & Machine Learning, focused on end-to-end data pipelines, predictive modeling, and scalable systems — not notebook exercises.

I build recommendation architectures with deep learning and vector-index search, tune tree-based classifiers with hyperparameter optimization, and obsess over data-separation methodology so models hold up in production, not just on a leaderboard.

EDUCATION

B.Tech Computer Science Engineering

Artificial Intelligence & Machine Learning

GD Goenka University, Gurugram · 3rd Year

CGPA AVERAGE
8.98 / 10.0
GRADUATION
Expected May 2028
CERTIFICATIONS

Deep Learning Specialization

DeepLearning.AI

Jul 2026 · VERIFY →

Claude Code: A Highly Agentic Coding Assistant

DeepLearning.AI · Anthropic

Jul 2026 · VERIFY →

Machine Learning Specialization

DeepLearning.AI · Stanford University

May 2026 · VERIFY →

Databases & SQL for Data Science with Python

IBM · Coursera

Nov 2025 · VERIFY →

Python for Everybody Specialization

University of Michigan · Coursera

Mar 2025 · VERIFY →

Prompt Engineering & Generative AI

Google · Vanderbilt University

Mar 2025 · VERIFY →

04SYSTEM MODULE

The whole field on one branch diagram — split by the question that decides your entire approach: is the data a table, or isn't it. Open any model to see what I built with it.

PROJECTION · ML TAXONOMY8/11 MAPPED TO PROJECTS
ROOT

MACHINE LEARNING

WHAT SHAPE IS THE DATA?

TABULAR DATA

TRADITIONAL ML

Rows and columns, engineered features. The model learns from structure you designed.

SUPERVISED

Labelled targets — the answer exists in the training data.

UNSUPERVISED

No labels — the model finds the structure itself.

UNSTRUCTURED DATA

DEEP LEARNING

Pixels, tokens, signals. The model learns the features instead of being handed them.

COMPUTER VISION · CNN

Convolutions learn spatial features — edges, textures, structure.

SEQUENTIAL MODELS

Order carries meaning — state is passed along the sequence. Studied in theory: these are the building blocks transformers were built to replace, and knowing exactly where they break is what makes attention make sense.

TRANSFORMERS

Attention replaces recurrence — every position sees every other in one step. This is the architecture running inside ChatGPT, Gemini and Claude, and the reason the modern era of AI happened when it did.

HOW TO READ THIS.A filled cyan dot means I've shipped a project with that model — open the node to jump to the code. Amber is in progress. FOUNDATIONmeans I've studied it in theory and understand how it works, and it carries no project by design: RNN → LSTM → attention is the line the modern era of AI was built on, and the architecture it ends at is the one running inside ChatGPT, Gemini and Claude today. A hollow dot is a method I know but haven't built with yet.

05SYSTEM MODULE

Channels are open. Reach out for internships, collaborations, or to talk shop about ML.

jarvis@naramcharan:~$ contact

$ whoami

Naramreddy Charan Kumar Reddy — AI & Machine Learning Engineer

$ list --channels

$

DESIGNED & BUILT BY NARAM CHARAN · POWERED BY J.A.R.V.I.S