IBM NOAA Project: Predicting the Rain: A Data Science Journey through JFK Airport Weather Patterns
By Matthew Ternenge Beeun Weather forecasting has always felt like a mix of high-level physics and a bit of magic. But as a Data Scientist, I know it’s actually a mix of high-quality data and robust statistical modeling. I recently completed a deep-dive analysis into the local climatological data from John F. Kennedy International Airport (JFK) . Using R Studio, I set out to answer a fundamental question for a weather forecast firm: Can we accurately predict daily precipitation using variables like temperature, wind speed, and pressure? The Approach: The 7-Step Workflow I followed a rigorous data science lifecycle to ensure the insights were both statistically sound and business-relevant: Data Ingestion: Sourcing historical NOAA records. Wrangling: Cleaning "Trace" values and converting raw strings into usable numbers. Exploratory Data Analysis (EDA): Using histograms and scatterplots to see the "shape" of the weather. Modeling: Building a Multiple Linear Reg...