Here's an outline of your project plan: Task: Collect and analyze weather data across cities worldwide. Purpose: PlanMyTrip will use the data to recommend ideal hotels based on clients' weather preferences. Method: Create a Pandas DataFrame with 500 or more of the world's unique cities and their weather data in real time. This process will entail collecting, analyzing, and visualizing the data. Your analysis of the data will be split into three main parts, or stages. Collect the Data Use the NumPy module to generate more than 1,500 random latitudes and longitudes. Use the citipy module to list the nearest city to the latitudes and longitudes. Use the OpenWeatherMap API to request the current weather data from each unique city in your list. Parse the JSON data from the API request. Collect the following data from the JSON file and add it to a DataFrame: City, country, and date Latitude and longitude Maximum temperature Humidity Cloudiness Wind speed
Latitude versus temperature Latitude versus humidity Latitude versus cloudiness Latitude versus wind speed
Latitude and temperature Latitude and humidity Latitude and cloudiness Latitude and wind speed
Latitude and temperature Latitude and humidity Latitude and cloudiness Latitude and wind speed
Create a heatmap with pop-up markers that can display information on specific cities based on a customer's travel preferences. Complete these steps: Filter the Pandas DataFrame based on user inputs for a minimum and maximum temperature.
Find a hotel from the cities' coordinates using Google's Maps and Places API, and Search Nearby feature. Store the name of the first hotel in the DataFrame. Add pop-up markers to the heatmap that display information about the city, current maximum temperature, and a hotel in the city.