Weather Data

This dataset contains synthetic weather data generated for ten different locations, including New York, Los Angeles, Chicago, Houston, Phoenix, Philadelphia, San Antonio, San Diego, Dallas, and San Jose. The data includes information about temperature, humidity, precipitation, and wind speed, with 1 million data points generated for each parameter.

Features:

• Location: The city where the weather data was simulated.
• Date_Time: The date and time when the weather data was recorded.
• Temperature_C: The temperature in Celsius at the given location and time.
• Humidity_pct: The humidity in percentage at the given location and time.
• Precipitation_mm: The precipitation in millimeters at the given location and time.
• Wind_Speed_kmh: The wind speed in kilometers per hour at the given location and time.

Additional Information:

• Variability and Complexity: The dataset incorporates variability and complexity to simulate realistic weather patterns. For example, adjustments have been made to temperature and precipitation based on seasonal variations observed in certain locations. In New York, higher temperatures and precipitation are simulated during the summer months, while in Phoenix, lower temperatures and increased precipitation are simulated during the winter months.
• Data Generation Method: The dataset was generated using Python's Faker library to create synthetic weather data for each location. Random values within realistic ranges were generated for temperature, humidity, precipitation, and wind speed, with adjustments made to reflect seasonal variations.

Potential Use Cases:

• Weather Prediction Models: Researchers and data scientists can use this dataset to develop and train weather prediction models for various locations.
• Climate Studies: The dataset can be used for climate studies and analysis to understand weather patterns and trends in different regions.
• Educational Purposes: Students and educators can use this dataset to learn about data analysis, visualization, and modeling techniques in the context of weather data.

Acknowledgements:

• This dataset was generated using Python's Faker library.
• Special thanks to the Faker library developers for providing tools to create synthetic data for various purposes.

Image Credits :

Image by Mohamed Hassan from Pixabay