Popular GitHub Repositories for Nyc

Materials for the Deploy and Monitor ML Pipelines with Python, Docker and GitHub Actions workshop at the PyData NYC 2024 conference

Training and Testing a Set of Machine Learning/Deep Learning Models to Predict Airbnb Prices for NYC

Materials for O'Reilly DL 4 NLP tutorial (NYC, June 2017)

Testing Boring SL with DuckDB

Develop ML models predict taxi trip duration in NYC. Ranked : Top 6% | RMSLE : 0.377 (Kaggle) | #DS

Prediction of call count for Call center workforce planning for 311 dataset

[Masters Thesis] Predicting the gentrification of NYC using ML

Predicting the gentrification of NYC using ML [Masters Thesis]

End-to-end PySpark analysis of NYC Taxi, Ethereum, and NASA logs (batch, graph, streaming)

Repo to my official submission to #2 HF Gradio NYC Hackathon 2022

Synthetic grocery retail dataset ...

End-to-end MLOps pipeline for NYC short-term rental price prediction using MLflow and W&B. Features data validation, feature engineering, model training, hyperparameter optimization, and automated deployment with weekly retraining.

NYC311 Infra Hotspots: End-to-end BigQuery AI pipeline that turns 311 tickets into ranked, map-ready polygons + Gemini briefs (embeddings → vector search → DBSCAN → time-decay scoring). US-region, audit/QA-guarded

End-to-end CRISP-DM forecasting pipeline for hourly NYC taxi demand using classical ML, tree ensembles, Conv1D+LSTM deep learning, and hybrid blending.

A reusable ML pipeline that is used to predict short-term rental prices in NYC, based on the property-related features.

This repository contains two machine learning projects: Air Quality Prediction, which predicts CO(GT) levels using environmental and pollutant data with AutoML, and NYC Taxi Fare Prediction, predicts taxi fares based on trip data using automated machine learning. Both projects showcase data analysis,preprocessing, and predictive modeling techniques

A semantic HTML project that builds a structured NYC blog layout, created as part of Codecademy's Learn HTML course.

NYC taxi fare prediction using XGBoost, RF & GBDT on 1M+ rows (RMSE ≈ 4.25). Includes feature engineering, peak hour bins, MLflow logging & AWS deployment via FastAPI.