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This project involved using Python and an API to investigate weather trends near the equator by collecting and analyzing weather data. The analysis helped to draw conclusions and provide insights into the factors affecting weather trends in this region.
Interact with API's to gather current weather conditions for global random cities. Use the gathered Weather data to determine vacation opportunities by identifying the nearest hotel.
A visual analysis using linear regression, r-values, geoViews, and Geoapify to examine weather changes as the equator is approached. Results are used to create a map displaying the most pleasant vacation spots.
Web scrapping all Egyptian houses, classifying their area on administrative 2 level, and calculating the average house prices for every region. Topics: Web Scrapping, Geopandas, Geoapify API, Data Analysis.
Create two Jupyter Notebooks: use API calls to determine weather conditions for 500 cities around the equator; use Geoapify API based on the weather analysis to plan future vacations.
Weather and Vacation Analysis: Explore the relationship between latitude and weather variables. Generate scatter plots and regression models. Filter weather data to find cities with desired conditions. Locate nearby hotels for vacation planning. Python, Jupyter Notebook, Pandas, Matplotlib, Citipy, OpenWeatherMap API, Geoapify API.