Overview ℹ️ This project aims to analyze weather trends, seasonal variations, and correlations between various weather attributes using Excel, SQL, and Power BI. The dataset includes information on city attributes, humidity, pressure, temperature, weather descriptions, wind direction, and wind speed for multiple cities.
Purpose: Metadata about each city in the dataset. Columns: City, Country, Latitude, Longitude. Usage: Mapping cities to countries and geographical coordinates, location-based analysis.
Purpose: Hourly humidity levels for each city. Columns: Datetime, City-wise humidity levels. Usage: Analyzing humidity trends, seasonal variations, correlation with other factors.
Purpose: Hourly air pressure levels for each city. Columns: Datetime, City-wise air pressure levels. Usage: Studying pressure patterns, weather change prediction, correlation analysis.
Purpose: Hourly temperature data for each city. Columns: Datetime, City-wise temperature records. Usage: Analyzing temperature trends, heatwaves, cold spells, energy consumption correlation.
Purpose: Textual weather descriptions on an hourly basis. Columns: Datetime, City-wise weather descriptions. Usage: Understanding qualitative aspects of weather, weather type categorization, frequency analysis.
Purpose: Hourly wind direction data for each city. Columns: Datetime, City-wise wind directions. Usage: Studying wind patterns, predicting wind-related events, pollution dispersion.
Purpose: Hourly wind speed data for each city. Columns: Datetime, City-wise wind speeds. Usage: Understanding wind patterns, predicting wind-related hazards, impact analysis.
The Power BI dashboard developed from this analysis provides comprehensive insights into weather patterns, seasonal variations, and correlations between different weather attributes. Users can interactively explore data trends, visualize historical weather data, and derive actionable insights.
Connect with me on LinkedIn for more insights and discussions: https://www.linkedin.com/in/gaurav-wankhede-5244101b8/