Jack is the head of analysis for the user interface team. He works for PlanMyTrip, a leading travel technology company. Jack asked me to help him collect and present data for customers via the search page, which they will then filter based on their preferred travel criteria in order to find their ideal hotel anywhere. To perform this task. I used Python, Jupyter Notebook and the city PI module to get the cities for more than 700 random latitudes and longitudes then I performed requests on the open weather map API and retrieve the JSON weather data from these cities. I then added the weather data to the Panda’s dataframe. From there I used Matplotlib to create a series of scatter plots to show the relationship between the latitude and a variety of weather parameters for over 700 cities around the world. As part of the analysis I completed a series of statistical calculations on the data using linear regression on the weather parameters in the Northern and Southern hemispheres. This data helped my team predict the best time of the year for people to plan their vacation. Finally, I exported the data, cleaned it and used the weather data to choose the best cities for a vacation based on certain weather criteria and then mapped these cities using Jupyter G Maps and the Google Places API.
Jack and beta testers provided recommendations for a few changes. They recommended adding the weather description to the weather data I already retrieved in the original project. Then, I added input statements for the best testers to filter the data for their weather preferences, which will be used to identify potential travel destinations and nearby hotels. From the list of potential travel destinations, the beta tester will choose four cities to create a travel itinerary. Finally, using the Google Maps Directions API, I will create a travel route between the four cities as well as a marker layer map. My code can be found on the following files: "Weather_database.ipynb,"Vacation_Search.ipynb," and Vacation_Itinerary.ipynb."
For this deliverable I had to Generate a set of 2,000 random latitudes and longitudes, retrieve the nearest city, and perform an API call with the OpenWeatherMap. In addition to the city weather data you gathered in this module, use your API skills to retrieve the current weather description for each city. Then, create a new DataFrame containing the updated weather data.
Below is a snippet of Python code used for performing an API call with the OpenWeatherMap:
The below is a city_data dataframe that I created.
I also generated a csv output file containing the weatherPy_database.csv.
For this deliverable I created input statements allow beta testers to retrieve customer weather preferences, then I used those preferences to identify potential travel destinations and nearby hotels. Then, I showed those destinations on a marker layer map with pop-up markers.
Below is the hotel dataframe that I developed.
Below is the WeatherPy vacation map that I developed.
For this deliverable I used the Google Directions API to create a travel itinerary that shows the route between four cities chosen from the customer’s possible travel destinations. Then, create a marker layer map with a pop-up marker for each city on the itinerary.
Below is the itinerary travel map that I developed.
Below is the itinerary travel map with markers that I developed.
