Weather Data Analysis

The purpose of this project is to analyze weather data from various cities across the world, and selecting a potential vacation spot that fits all the wanted weather conditions.

Dataset Sources:

• https://openweathermap.org/api

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Project Description

Step 1: WeatherPy

• Randomly select 2000 sets of latitude and longitude, find the nearest city on the picked set (discard repeated cities), and perform weather API calls and gather various weather data.
• Create scatter plot on the following:
  1. Temperature (F) vs. Latitude
  2. Humidity (%) vs. Latitude
  3. Cloudiness (%) vs. Latitude
  4. Wind Speed (mph) vs. Latitude
• Create linear regression on the below relationships:
  1. Northern Hemisphere - Temperature (F) vs. Latitude
  2. Southern Hemisphere - Temperature (F) vs. Latitude
  3. Northern Hemisphere - Humidity (%) vs. Latitude
  4. Southern Hemisphere - Humidity (%) vs. Latitude
  5. Northern Hemisphere - Cloudiness (%) vs. Latitude
  6. Southern Hemisphere - Cloudiness (%) vs. Latitude
  7. Northern Hemisphere - Wind Speed (mph) vs. Latitude
  8. Southern Hemisphere - Wind Speed (mph) vs. Latitude
• Example Screenshots:

Step 2: VacationPy

• Create a heat map that displays the humidity for every city from step 1.
• Narrow down the data to find the ideal weather condition below:
  1. A max temperature lower than 80 degrees but higher than 70.
  2. Wind speed less than 10 mph.
  3. Zero cloudiness.
• Using Google Places API to find the first hotel for each city located within 5000 meters of the coordinates.
• Plot the hotels on top of the humidity heatmap with map marker.

Final Screenshot