Available for opportunities

UPENDRA CHAUHAN

ML Engineer · Data Scientist · AI Builder

Building predictive models and intelligent systems — from EDA and feature engineering to deep learning and agentic AI. Turning raw data into decisions that matter.

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↗ GitHub ↗ LinkedIn ↗ LeetCode

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About

BUILDING ML
THAT SHIPS

Not just notebooks — models that solve real problems.

~/upendra — bash

➜ whoami

upendra_chauhan

➜ cat stack.txt

python · scikit-learn · tensorflow

langchain · chromadb · rag

pandas · numpy · matplotlib

➜ cat education.txt

B.Tech CSE @ Galgotia's College

Greater Noida · CGPA 7.25 · 2022–2026

➜ echo $CURRENT_STATUS

training models · solving DSA · building future

➜ _█

I'm Upendra Chauhan, a CS undergrad from Galgotia's College of Engineering & Technology, Greater Noida. I specialize in machine learning, predictive modeling, and building intelligent systems.

My work spans classical ML, deep learning with TensorFlow, NLP pipelines, and modern LLM tooling including RAG and agentic AI. I believe the best model is one that reliably solves a real problem.

When I'm not building, I'm grinding DSA on LeetCode, competing on Kaggle, or exploring what's next in AI research.

400+

LeetCode Problems

90%

Best Model Accuracy

3

ML Projects

4

Certifications

Projects

WHAT I'VE
BUILT

Selected ML systems and predictive pipelines.

01 / MACHINE LEARNING

LAPTOP PRICE PREDICTOR

Built an end-to-end regression pipeline to predict laptop prices from hardware specs with 90% accuracy. Full EDA, feature engineering, and model evaluation with data visualization.

Scikit-Learn Pandas NumPy Regression EDA Feature Eng.

↗

02 / DEEP LEARNING · NLP

SENTIMENT ANALYSIS (RNN)

Deep learning sentiment classifier on the IMDb dataset using Recurrent Neural Networks. Applied tokenization, padding, and word embeddings to classify reviews as positive or negative.

TensorFlow Pandas NumPy RNN NLP Word Embeddings

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03 / FRAUD DETECTION · ENSEMBLE

UPI FRAUD DETECTION

ML-based UPI fraud detection using transaction behavior analysis. Applied SMOTE for imbalance handling and ensemble methods. Evaluated with precision, recall, F1-score and AUC-ROC.

Scikit-Learn Matplotlib Pandas Ensemble SMOTE AUC-ROC

↗

Skills