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precipitation-analysis

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AAlkiyumi / Project-4-Big-Data-Analysis-with-PySpark-on-Weather-Data

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In this project, I analyzed weather data from the NCEI Global Surface Summary of Day dataset using PySpark in Jupyter Notebook. Tasks included data cleaning, statistical analysis, and forecasting for temperature, wind speed, precipitation, and extreme weather events. The project also predicts future weather patterns for Cincinnati and Florida.

data-science machine-learning jupyter-notebook pyspark data-engineering statistical-analysis uc data-analysis predictive-modeling data-cleaning big-data-analytics time-series-forecasting university-of-cincinnati precipitation-analysis temperature-forecasting wind-speed-data uc2026 cs5165 introduction-to-cloud-computing
  • Updated Nov 12, 2024
  • Jupyter Notebook

omidemam / PPTSA

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PPTSA (Precipitation Probabilistic Time Series Analysis) examines the statistical structure and predictability of precipitation, emphasizing the impulse-like nature of rainfall and its implications for forecasting. The study uses ~140 years of daily precipitation data from Central Park, New York City.

uncertainty lstm gaussian-processes lstm-neural-networks precipitation marked-point-process precipitation-data rainfall-prediction rainfall-data rainfall-frequency-analysis precipitation-prediction precipitation-analysis
  • Updated Jan 26, 2026
  • Jupyter Notebook

AAlkiyumi / Predicting-Hospital-Readmission-Risk

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This project aims to create a predictive model that forecasts the likelihood of a patient being readmitted to the hospital within 30 days of discharge.

data-science machine-learning jupyter-notebook pyspark data-engineering statistical-analysis uc data-analysis predictive-modeling data-cleaning big-data-analytics university-of-cincinnati precipitation-analysis uc2026 cs5165 introduction-to-cloud-computing
  • Updated Dec 25, 2024
  • Jupyter Notebook

fulan-m / AraPresSao_._I-SICAm-VIII-JoGAAm-UFSCar-2025

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Result of a Research Initiation grant given by the São Paulo State Research Foundation (FAPESP) from 2024-2025. Analyzes precipitation trends calculated with the Theil-Sen slope estimates. The database used was the CHIRPS product on Google Earth Engine, with the study are being the Regions of Araçatuba, Presidente Prudente and São José do Rio Preto

trend-analysis precipitation-analysis sens-slope
  • Updated Apr 12, 2026

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